Information processing device, information processing system, and information processing method

Abstract

This suppresses the occurrence of position estimation results deviating from the correct value in multi-stage search. [Solution] An information processing device comprising: a candidate point setting unit that sets a candidate point group including multiple candidate points; a signal theoretical value calculation unit that calculates a signal theoretical value corresponding to a signal measured by the measurement sensor unit when an object which is a measurement sensor unit or a signal generating unit is present at the position of at least each candidate point; a position calculation unit that performs a first determination process to determine a first candidate point corresponding to the signal theoretical value corresponding to the measurement value from among a plurality of first candidate points included in the candidate point group as a first point of interest, based on the measurement value, the candidate point group, and the signal theoretical value corresponding to the result of the measurement sensor unit measuring a signal generated from the signal generating unit; and a second determination process to determine a second candidate point corresponding to the signal theoretical value corresponding to the measurement value from among a plurality of second candidate points included in the candidate point group and corresponding to the first point of interest as a second point of interest.

Description

Technical Field

[0001] The present disclosure relates to an information processing apparatus, an information processing system, and an information processing method.

Background Art

[0002] By performing multi-stage search such as two-stage search, the position of a predetermined object is estimated. For example, in a two-stage search, a rough position estimation is performed in the first-stage search, and then, in the second-stage search, a detailed position estimation is performed.

[0003] Non-Patent Document 1 describes a technique related to the distribution of signals of electroencephalogram (EEG) and magnetoencephalogram (MEG) (see Non-Patent Document 1). Patent Document 1 describes a technique related to a template matching method using a coarse-to-fine method or the like (see Patent Document 1).

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Non-Patent Documents

[0005]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] However, in conventional techniques, especially in multi-stage searches such as two-stage searches, there were cases where the final position estimation result deviated from the correct value when the position estimation result from a coarse search was not valid.

[0007] This disclosure was made in consideration of these circumstances, and aims to provide an information processing device, an information processing system, and an information processing method that can suppress the final position estimation result from deviating from the correct value in multi-stage searches such as two-stage searches. [Means for solving the problem]

[0008] One embodiment is an information processing device comprising: a candidate point setting unit that sets a candidate point group including a plurality of candidate points; a signal theoretical value calculation unit that calculates a signal theoretical value corresponding to a signal measured by the measurement sensor unit when an object which is a measurement sensor unit or a signal generating unit is present at the position of at least each of the candidate points; a position calculation unit that performs a first determination process to determine a first candidate point corresponding to the signal theoretical value corresponding to the measurement value from among a plurality of first candidate points included in the candidate point group as a first point of interest, based on the measurement value corresponding to the result of the measurement sensor unit measuring a signal generated from the signal generating unit, the candidate point group, and the signal theoretical value; and a second determination process to determine a second candidate point corresponding to the signal theoretical value corresponding to the measurement value from among a plurality of second candidate points included in the candidate point group and corresponding to the first point of interest as a second point of interest.

[0009] One embodiment is an information processing system having an information processing device and a measurement sensor unit, wherein the measurement sensor unit has a plurality of measurement sensors, the measurement sensor unit outputs a multidimensional time-series signal value as a number of the number of measurement sensors, and the information processing device includes a candidate point setting unit for setting a group of candidate points including a plurality of candidate points, a signal theoretical value calculation unit for calculating a signal theoretical value corresponding to the signal measured by the measurement sensor unit when an object which is a signal source of the signal generation unit is present at the position of at least each of the candidate points, and the measurement sensor The information processing system comprises a position calculation unit that performs the following: a first determination process which determines a first candidate point corresponding to the theoretical signal value corresponding to the measured value from among a plurality of first candidate points included in the candidate point group as a first point of interest, based on the measured value, candidate point group, and theoretical signal value corresponding to the result of the signal generated by the signal generation unit measured by the signal generation unit; and a second determination process which determines a second candidate point corresponding to the theoretical signal value corresponding to the measured value from among a plurality of second candidate points included in the candidate point group and corresponding to the first point of interest as a second point of interest.

[0010] One embodiment is an information processing system having an information processing device and a measurement sensor unit, wherein the measurement sensor unit has one measurement sensor that measures signals generated from a plurality of signal sources having a signal generation unit, the measurement sensor unit outputs a one-dimensional time-series signal value as a measured value, the information processing device has a candidate point setting unit that sets a group of candidate points including a plurality of candidate points, a signal theoretical value calculation unit that calculates a theoretical signal value corresponding to the signal measured by the measurement sensor unit when the object which is the measurement sensor unit is present at the position of at least each of the candidate points, and the signal separation of the signal value, thereby the signal source The information processing system comprises: a signal separation unit that outputs a multidimensional time-series signal value corresponding to each; a position calculation unit that performs a first determination process to determine a first candidate point corresponding to the signal theoretical value corresponding to the measured value from among a plurality of first candidate points included in the candidate point group, based on the measured value, candidate point group, and signal theoretical value corresponding to the result of the measurement sensor unit measuring the signal generated from the signal generation unit; and a second determination process to determine a second candidate point corresponding to the signal theoretical value corresponding to the measured value from among a plurality of second candidate points included in the candidate point group and corresponding to the first point of interest, as a second point of interest.

[0011] One embodiment is an information processing method in which a candidate point setting unit sets a group of candidate points including a plurality of candidate points, a signal theoretical value calculation unit calculates a signal theoretical value for at least each of the positions of the candidate points that corresponds to the signal measured by the measurement sensor unit when an object which is a measurement sensor unit or a signal generating unit is present at the position, and a position calculation unit performs a first determination process in which, based on the measurement value corresponding to the result of the measurement sensor unit measuring the signal generated from the signal generating unit, the group of candidate points, and the signal theoretical value, the first candidate point corresponding to the signal theoretical value corresponding to the measurement value is selected from among a plurality of first candidate points included in the group of candidate points as a first point of interest, and a second determination process in which the second candidate point corresponding to the signal theoretical value corresponding to the measurement value is selected from among a plurality of second candidate points included in the group of candidate points and corresponding to the first point of interest, is selected as a second point of interest. [Effects of the Invention]

[0012] According to the present disclosure, in an information processing apparatus, an information processing system, and an information processing method, in a multi-stage search such as a two-stage search, it is possible to suppress the final position estimation result from deviating from the correct value.

Brief Description of the Drawings

[0013] [Figure 1] It is a diagram showing a configuration example of an information processing system including an information processing apparatus according to an embodiment (first embodiment, second embodiment). [Figure 2] It is a diagram showing a configuration example of the information processing apparatus according to the embodiment. [Figure 3] It is a diagram showing a configuration example of a processing unit in the information processing apparatus according to an embodiment (first embodiment, second embodiment). [Figure 4] It is a diagram showing an example of an outline of processing in an information processing system according to an embodiment (first embodiment, second embodiment). [Figure 5] It is a diagram schematically showing an example of a candidate point group according to the embodiment. [Figure 6] It is a diagram schematically showing an example of a signal theoretical value group according to the embodiment. [Figure 7A] It is a diagram showing an example of a boundary point set in the candidate point group in the first stage according to the first embodiment. [Figure 7B] It is a diagram showing an example of a boundary point set in the signal theoretical value group in the first stage according to the first embodiment. [Figure 7C] It is a diagram showing an example of an auxiliary point set in the candidate point group in the first stage according to the first embodiment. [Figure 7D] It is a diagram showing an example of an auxiliary point set in the signal theoretical value group in the first stage according to the first embodiment. [Figure 7E] It is a diagram showing an example of a separating hyperplane set in the signal theoretical value group in the first stage according to the first embodiment. [Figure 8] It is a diagram showing an example of a procedure of a multi-stage search process according to the embodiment. [Figure 9A] It is a diagram showing an example of a search range set for a candidate point group in the second stage according to the second embodiment. [Figure 9B] It is a diagram showing an example of a search range set for a signal theoretical value group in the second stage according to the second embodiment. [Figure 10] It is a diagram showing a configuration example of an information processing system including an information processing apparatus according to an embodiment (third embodiment, fourth embodiment). [Figure 11] It is a diagram showing a configuration example of an information processing apparatus according to an embodiment (third embodiment, fourth embodiment). [Figure 12] It is a diagram showing an example of an outline of processing in an information processing system according to an embodiment (third embodiment, fourth embodiment). [Figure 13A] It is a diagram showing an example of main candidate points and measurement target points in the first stage of two-stage search. [Figure 13B] It is a diagram showing an example of signal theoretical values and measured values in the first stage of two-stage search. [Figure 13C] It is a diagram showing an example of candidate points (main candidate points and sub-candidate points) and measurement target points in the second stage of two-stage search. [Figure 13D] It is a diagram showing an example of signal theoretical values and measured values in the second stage of two-stage search. [Figure 14A] It is a diagram showing an example of main candidate points and measurement target points in the first stage of two-stage search. [Figure 14B] It is a diagram showing an example of signal theoretical values and measured values in the first stage of two-stage search. [Figure 14C] It is a diagram showing an example of candidate points (main candidate points and sub-candidate points) and measurement target points in the second stage of two-stage search. [Figure 14D] It is a diagram showing an example of signal theoretical values and measured values in the second stage of two-stage search. [Figure 15A] It is a diagram showing an example of candidate points and measurement target points in one-stage search. [Figure 15B] It is a diagram showing an example of signal theoretical values and measured values in one-stage search. [Modes for carrying out the invention]

[0014] The embodiments of this disclosure will be described below with reference to the drawings.

[0015] (First Embodiment) The first embodiment will be described.

[0016] [Information Processing Systems] Figure 1 is a diagram showing an example configuration of an information processing system 1 including an information processing device 11 according to an embodiment. The information processing system 1 comprises an information processing device 11 and a measurement sensor unit 21. Figure 1 also shows a signal source 31 and a communication channel C1. Furthermore, the information processing system 1 may be considered to include either or both of the signal source 31 and the communication channel C1.

[0017] <Signal source of the signal generation unit> For the sake of explanation, Figure 1 shows the signal generation unit 22. In this embodiment, the signal generation unit 22 has one signal generation source 31. In this embodiment, the signal generation unit 22 and the signal generation source 31 may be considered equivalent.

[0018] The signal source 31 generates a predetermined signal. In this embodiment, the signal source 31 is movable. Here, for example, the signal source 31 may have a movable drive unit, or the signal source 31 may be mounted on a predetermined mobile body, and the signal source 31 may move as the mobile body moves. The moving object may be any living or non-living object, such as a person, animal, robot, vehicle, ship, or airplane. For example, the signal source 31 may be located inside a living organism such as a human being.

[0019] <Measurement Sensor Unit> The measurement sensor unit 21 has a plurality of n (where n is an integer of 2 or more) measurement sensors A1 to An. In this embodiment, the number of measurement sensors A1 to An (n) may be referred to as the number of channels, for example. Here, the number (n in this embodiment) and arrangement of the multiple measurement sensors are not limited to the example in Figure 1, and any configuration may be used. For example, the number of measurement sensors may be two, or it may be three or more. Furthermore, in the example shown in Figure 1, the multiple measurement sensors A1 to An are arranged in an array, but the arrangement of the multiple measurement sensors can be arbitrary. The array-like structure may also be referred to as a matrix-like structure, for example.

[0020] In this embodiment, the information processing device 11 and each of the measurement sensors A1 to An are connected to each other via a wired or wireless communication channel C1. The information processing device 11 is then capable of acquiring the measurement results from each of the measurement sensors A1 to An. In the example shown in Figure 1, the details of the connection between the information processing device 11 and the respective measurement sensors A1 to An are omitted from the illustration. In the example shown in Figure 1, a single communication channel C1 is shown for simplification, but for example, different communication channels may be used for each measurement sensor A1 to An. As another example, the measurement sensor unit 21 may combine the measurement results from multiple measurement sensors A1 to An and transmit them to the information processing device 11 via a single communication channel C1.

[0021] In this embodiment, the information processing device 11 communicates with each of the measurement sensors A1 to An to acquire the measurement results from each of the measurement sensors A1 to An. However, as another example, a configuration may be used in which the measurement results from each of the measurement sensors A1 to An are first stored in a portable storage medium, and then the measurement results are output from the storage medium to the information processing device 11, allowing the information processing device 11 to acquire the measurement results.

[0022] In this embodiment, the information processing device 11 and the measurement sensor unit 21 are shown as separate components. However, in other examples, the information processing device 11 and the measurement sensor unit 21 may be integrated, in which case the communication channel C1 may not be provided. For example, Figure 1 shows a case where multiple measurement sensors A1 to An are located outside the information processing device 11, but the multiple measurement sensors A1 to An and the information processing device 11 do not necessarily have to be clearly distinguished as being inside or outside.

[0023] Each of the measurement sensors A1 to An measures the signal generated from the signal source 31. In this embodiment, each of the measurement sensors A1 to An measures the signal generated from the signal source 31 in a non-contact manner.

[0024] The term "measurement sensor" is merely a descriptive term; it may also be called by other names, such as "signal sensor" or simply "sensor." Furthermore, the multiple measurement sensors A1 to An may be referred to as, for example, a group of measurement sensors or a measurement sensor unit. In this embodiment, a case is shown in which multiple measurement sensors A1 to An are configured as an integrated unit (measurement sensor unit 21), but as another example, the multiple measurement sensors may be provided separately.

[0025] In this embodiment, the multiple measurement sensors A1 to An are located in fixed positions without moving, but in other examples, they may be movable. Furthermore, if multiple measurement sensors A1 to An are movable, the information processing device 11 is configured to be able to grasp the movement status (change in position) of the multiple measurement sensors A1 to An, and the information processing device 11 corrects the measurement results from the measurement sensors A1 to An so that they are considered to be in fixed positions.

[0026] Here, various physical quantities may be used as the physical quantity of the signal generated by the signal source 31 and the physical quantity measured by the measurement sensors A1 to An. In this embodiment, magnetic intensity is used as one of these physical quantities. In this embodiment, the signal source 31 is a coil that generates magnetism, and the measurement sensors A1 to An are magnetic sensors. In this embodiment, unless otherwise specified, for the sake of simplicity, it is assumed that the pattern of the signal generated from the signal source 31 (a coil in this embodiment) is uniquely determined once its position is determined.

[0027] Here, other physical quantities may be used as these physical quantities. Examples other than magnetism generated from coils, etc., include physical quantities related to electric current, voltage, light, force such as pressure, sound generated from a sound source, ultrasound, radio waves generated from a radio wave source, etc. As a specific example, measurement sensors A1 to An may be, for example, magnetic sensors, potential sensors, light sensors, pressure sensors, acceleration sensors, vibration sensors, microphones, ultrasonic sensors, radio wave receivers, etc. Measurement may also be called, for example, detection, measurement, or sensing.

[0028] <Information Processing Device> Figure 2 shows an example of the configuration of the information processing device 11 according to the embodiment. The information processing device 11 is configured, for example, using a computer. The information processing device 11 comprises an input unit 111, an output unit 112, a storage unit 113, and a control unit 114. The input unit 111 includes an acquisition unit 131. The output unit 112 includes a display unit 141. The control unit 114 comprises a processing unit 151 and a display control unit 152. The processing unit 151 may also be called, for example, the arithmetic unit.

[0029] The input unit 111 receives input from outside the information processing device 11. In this embodiment, the input unit 111 receives signals (measurement result signals) output from each of the measurement sensors A1 to An. Specifically, the input unit 111 may receive the signals transmitted from each of the measurement sensors A1 to An, or it may receive signals stored in a portable storage device from that storage device. Furthermore, the input unit 111 may have, for example, an operating unit operated by a user, and information corresponding to the operation performed by the user may be input to the operating unit.

[0030] The acquisition unit 131 acquires the signal input by the input unit 111. The acquisition unit 131 may store the acquired signal in the storage unit 113. In this case, if the signal input by the input unit 111 is an analog signal, the acquisition unit 131 may, for example, be equipped with an A / D (Analog to Digital) conversion function to convert the signal from an analog signal to a digital signal. Furthermore, when the information processing device 11 is applied to real-time processing, the acquisition unit 131 acquires signals in real time. Even when the information processing device 11 is not applied to real-time processing, the acquisition unit 131 may still acquire signals in real time.

[0031] The output unit 112 outputs to the outside. The display unit 141 displays and outputs information related to the signal processing results. The display unit 141 has a screen such as a liquid crystal display (LCD), and displays and outputs information related to the signal processing results on this screen. In another configuration example, the display unit 141 may print and output the information related to the signal processing results onto paper. The output unit 112 may also have a function to output in other ways, such as audio output.

[0032] The memory unit 113 has a storage device such as a memory, and stores data. The memory unit 113 stores data such as the input signal and the processing result of the signal. Furthermore, the memory unit 113 stores, for example, control programs. Data may also be referred to as information, for example.

[0033] The control unit 114 performs various processes and controls. In this embodiment, the control unit 114 has a processor such as a CPU (Central Processing Unit), and the processor executes a program (control program) stored in the storage unit 113 to perform various processes and various controls.

[0034] The processing unit 151 performs predetermined processing based on the measurement result signals from the measurement sensors A1 to An. The display control unit 152 outputs various types of information to the screen of the display unit 141.

[0035] <Processing unit in information processing device> Figure 3 shows an example of the configuration of the processing unit 151 in the information processing device 11 according to the embodiment. The processing unit 151 includes a candidate point setting unit 171, a signal theoretical value calculation unit 172, and a position calculation unit 173.

[0036] The candidate point setting unit 171 sets a group of candidate points that includes multiple candidate points. The candidate point setting unit 171 sets multiple candidate points to be placed in the three-dimensional space where the measurement sensors A1 to An and the signal generation source 31 exist. In this embodiment, the multiple candidate points include multiple primary candidate points and multiple secondary candidate points.

[0037] The signal theoretical value calculation unit 172 calculates the estimated signal theoretical value that would be obtained if the signal source 31 were located at one of several different locations (which, for convenience of explanation, will also be called a temporary location). In this embodiment, the theoretical signal value is the theoretical signal value obtained by estimating the value of the time-series multidimensional signal acquired from multiple measurement sensors A1 to An, and is a set of time-series measurement result values ​​acquired from each of the measurement sensors A1 to An. Thus, in this embodiment, the signal theoretical value calculation unit 172 calculates a set of n signal theoretical values ​​for one provisional position.

[0038] The position calculation unit 173 calculates the position of the signal source 31 based on the information of the candidate point group set by the candidate point setting unit 171, the calculation results from the signal theoretical value calculation unit 172, and the measurement results from multiple measurement sensors A1 to An. In this embodiment, the position calculation unit 173 calculates the position of the signal source 31 by performing a multi-stage search (for example, a two-stage search). As a result, the position calculation unit 173 may, for example, track the position of the moving signal source 31. In this embodiment, the position may be calculated, for example, by estimating the position.

[0039] <Overview of processing in information processing systems> Figure 4 shows an example of the general processing in the information processing system 1 according to this embodiment. The candidate point setting unit 171 provides candidate point group information (candidate point group information a1) to the signal theoretical value calculation unit 172 and the position calculation unit 173. In this embodiment, candidate point group information a1 is information representing the position of each candidate point.

[0040] The signal theoretical value calculation unit 172 calculates signal theoretical value information (signal theoretical value group information a2) based on the candidate point cloud information a1, and provides the obtained signal theoretical value group information a2 to the position calculation unit 173. Here, the signal theoretical value calculation unit 172 may, for example, calculate the signal theoretical value corresponding to the candidate points (primary candidate points, secondary candidate points), and further calculate the signal theoretical value corresponding to other points as needed.

[0041] The measurement sensor unit 21 provides the position calculation unit 173 with measurement result information (measurement result information a3) from measurement sensors A1 to An. In this embodiment, the measurement result information a3 is time-series multidimensional (n-dimensional in this embodiment) signal information. The position calculation unit 173 acquires position information (position information a4) of the signal source 31 based on candidate point cloud information a1, signal theoretical value group information a2, and measurement result information a3.

[0042] As another example, the information processing device 11 may be equipped with a signal processing unit that performs predetermined processing on the measurement result information a3 output from the measurement sensor unit 21. This predetermined processing may, for example, be a process to reduce noise included in the measurement result. In the example shown in Figure 4, the signal processing unit is located between the measurement sensor unit 21 and the position calculation unit 173. The signal processing unit performs predetermined processing on the measurement result information a3 output from the measurement sensor unit 21 and outputs the results of this predetermined processing to the position calculation unit 173. The position calculation unit 173 calculates the position based on the measurement result information a3 after the predetermined processing has been performed.

[0043] <Setting the candidate point group> Figure 5 is a schematic diagram showing an example of a candidate point group according to this embodiment. For the sake of explanation, Figure 5 shows the XYZ Cartesian coordinate system, which is a three-dimensional Cartesian coordinate system.

[0044] The candidate point setting unit 171 sets the candidate point group E1 as shown in Figure 5. In this embodiment, a group of candidate points E1 is set, which includes multiple candidate points arranged in directions parallel to the X-axis, parallel to the Y-axis, and parallel to the Z-axis. The candidate point group E1 is set to include (i.e., cover) the spatial portion that is defined as the search range in the multi-stage search (e.g., two-stage search) performed in this embodiment.

[0045] In the example in Figure 5, some of the candidate point sequences of candidate point group E1 are shown, including a sequence of 3 candidate points parallel to the X-axis, a sequence of 6 candidate points parallel to the Y-axis, and a sequence of 3 candidate points parallel to the Z-axis. Thus, in this embodiment, the space of the candidate point group is a three-dimensional space.

[0046] In the example in Figure 5, for the sake of explanation, a hypothetical line connecting adjacent candidate points is shown. However, such a line does not necessarily have to be set; for example, it should only be set when a situation arises where such a line is used in calculations or other operations in various information processing. The same applies to similar diagrams that follow (i.e., diagrams showing multiple candidate points).

[0047] In this embodiment, the spacing between multiple candidate points in the direction parallel to the X-axis, the spacing between multiple candidate points in the direction parallel to the Y-axis, and the spacing between multiple candidate points in the direction parallel to the Z-axis are all the same. However, in the example in Figure 5, to make the figure easier to understand, the spacing between adjacent candidate points in the direction parallel to the Z-axis is shown to be larger so that multiple candidate points aligned parallel to the Z-axis do not overlap.

[0048] As another example, the spacing between multiple candidate points parallel to the X-axis, the spacing between multiple candidate points parallel to the Y-axis, and the spacing between multiple candidate points parallel to the Z-axis may all be different, or they may differ only with respect to one axis. Furthermore, in this embodiment, the interval between multiple candidate points aligned parallel to a single axis (X-axis, Y-axis, or Z-axis) is a constant interval, but other examples may include different intervals.

[0049] In the example in Figure 5, for the sake of explanation, only some of the candidate points in a single XY plane are labeled and described. In this embodiment, for the sake of explanation, a portion of the candidate points will be called primary candidate points, and the remaining portion of the candidate points will be called secondary candidate points. Both primary and secondary candidate points are candidate points. Note that the primary candidate points and secondary candidate points are descriptive names and may be referred to by other arbitrary names, such as grid points.

[0050] In this embodiment, the primary candidate point represents a candidate point used in the first stage of a two-stage search, and may also be used in the second stage. Furthermore, in this embodiment, secondary candidate points represent candidate points that are not used in the first stage of the two-stage search, but can be used in the second stage.

[0051] Figure 5 shows four primary candidate points G1-G4 and thirteen secondary candidate points F1-F4, F11-F15, and F21-F24, each labeled with an index. The primary candidate point G2, secondary candidate point F11, and primary candidate point G1 are arranged in the order listed, from negative to positive on the X-axis. The primary candidate point G3, secondary candidate point F16, and primary candidate point G4 are arranged in the order listed, from negative to positive on the X-axis. The primary candidate point G1, secondary candidate point F1, secondary candidate point F2, secondary candidate point F3, secondary candidate point F4, and primary candidate point G4 are arranged in the order listed, from negative to positive on the Y-axis. The secondary candidate points F11, F12, F13, F14, F15, and F16 are listed in the order they are written, from negative to positive on the Y-axis. The primary candidate point G2, secondary candidate points F21, F22, F23, F24, and primary candidate point G3 are listed in the order they are written, from negative to positive on the Y-axis. Note that in the example in Figure 5, although the signs are omitted, there are also multiple candidate points in the direction parallel to the Z-axis.

[0052] <Calculation of theoretical signal values> Figure 6 is a schematic diagram showing an example of the signal theoretical value group H1 according to the embodiment. Figure 6 schematically shows the theoretical signal values ​​corresponding to each candidate point shown in Figure 5. Here, the theoretical signal value corresponding to one candidate point represents the theoretical signal value measured by the measurement sensor unit 21 (measurement sensors A1 to An) assuming that a signal source 31 exists at that candidate point. In this embodiment, each theoretical signal value represents a set of values ​​equal to the number of measurement sensors A1 to An (n values ​​in this embodiment). In other words, in this embodiment, the space of theoretical signal values ​​is an n-dimensional space, but it is shown schematically in the diagram. Specifically, the signal pattern of the theoretical signal value is represented by a straight line if it is a scalar field, and by a two-dimensional or three-dimensional subspace if it is a vector field. In this embodiment, for the sake of explanation, the space of theoretical signal values ​​will be referred to as the theoretical signal value space, but it may also be called by other names, such as the signal space.

[0053] In the example in Figure 6, for the sake of explanation, only some of the theoretical signal values ​​corresponding to the candidate points that were labeled in Figure 5 are labeled and explained. Figure 6 shows the four theoretical signal values ​​g1 to g4 corresponding to the four main candidate points G1 to G4, and the thirteen theoretical signal values ​​f1 to f4, f11 to f15, and f21 to f24 corresponding to the thirteen secondary candidate points F1 to F4, F11 to F15, and F21 to F24, with signs indicated.

[0054] In the example in Figure 6, for the sake of explanation, a hypothetical line connecting adjacent theoretical signal values ​​is shown. However, such a line does not necessarily have to be set up; for example, it should only be set up when a situation arises in which such a line is used in calculations or other operations in various information processing. The same applies to similar diagrams that follow (i.e., diagrams showing multiple theoretical signal values). Furthermore, the illustration of multiple theoretical signal values ​​in Figure 6 is for illustrative purposes only and is not necessarily rigorous. This also applies to similar figures that follow (i.e., figures showing multiple theoretical signal values).

[0055] [Explanation of multi-stage search techniques and specific examples of potential problems] Refer to Figures 13A to 13D and 14A to 14D to explain multi-stage search techniques and provide specific examples of problems that may arise. Note that this explanation will use a two-stage search as an example. Furthermore, in this explanation, we will also show a one-stage search technique with reference to Figures 15A and 15B. Below, we will explain multi-stage search techniques with reference to Figures 13A to 13D, and then provide specific examples of problems that may occur with reference to Figures 14A to 14D and Figures 15A to 15B.

[0056] For the sake of explanation, this example will use a portion of the candidate point group shown in Figure 5 and a portion of the theoretical signal value group shown in Figure 6. Furthermore, in this example, for the sake of simplicity, we will explain the case where the point to be measured lies on a two-dimensional plane (a plane parallel to the XY plane) formed by multiple candidate points. However, the same applies when the point to be measured lies at an arbitrary position in three-dimensional space (any position in the XYZ Cartesian coordinate system).

[0057] In this example, the measurement target point represents the point (location) where the object whose position is to be estimated exists. The term "measurement target point" is merely a convenient term for explanation purposes; it may be referred to by other names, such as "target point" or "object."

[0058] <Two-stage search technology> In the two-stage search technique, the first stage of the search involves comparing the acquired measured values ​​with the theoretical signal values ​​corresponding to each primary candidate point within the first-stage search range to calculate the similarity between the two. Then, the single primary candidate point with the highest similarity is identified, and the position of this identified candidate point is determined as the reference point (also called the "spot point" for convenience of explanation) for the next stage of the search. The search range in the first stage may, for example, be the range that includes all pre-defined primary candidate points, or it may be the range of the entire space. Furthermore, the determination of candidate points (or the locations of candidate points) may be called, for example, selection.

[0059] Next, in the two-stage search technique, the second stage of the search process involves comparing the measured value with the theoretical signal values ​​corresponding to each candidate point (primary candidate point and secondary candidate point) within the second-stage search range corresponding to the target point, and calculating the similarity between the two. Then, the single candidate point (primary candidate point or secondary candidate point) with the highest similarity is identified, and the position of the identified candidate point is determined as the final search result position. Here, the similarity between the measured value and the theoretical signal value is a value that represents the degree to which the signal patterns of the two are similar, and in this embodiment, the higher this value, the more similar the signal patterns of the two are. The second stage of the search range may, for example, be a range that includes pre-defined candidate points for each primary candidate point that serves as a point of interest, and one example is a range that includes the primary candidate point and the secondary candidate points in its vicinity. In other words, in the second stage of the search, the search range is narrowed to the area surrounding the spot point determined in the first stage of the search, and not only rough primary candidate points but also finer secondary candidate points are used.

[0060] Here, for example, the search range for the second stage may be set to a range within a predetermined distance from the spot point. The predetermined distance may, for example, be ±1 [m], ±1 [cm], or ±1 [mm] in the direction parallel to the X-axis, the Y-axis, and the Z-axis, respectively. The larger the distance between adjacent candidate points (primary and secondary candidate points), the fewer candidate points tend to be within the same search range. Conversely, the smaller the distance, the more candidate points tend to be within the same search range.

[0061] <Examples of cases where no problems occur with two-stage search technology> First, referring to Figures 13A to 13D, we will show an example of a two-stage search technique, and then an example where no problems occur. In the first stage of the search, a rough search is performed using the primary candidate points.

[0062] Figure 13A shows an example of the primary candidate points G1-G4 and the measurement target point D1 in the first stage of a two-stage search. Figure 13A shows an XYZ Cartesian coordinate system similar to that shown in Figure 5. Figure 13B shows an example of theoretical signal values ​​g1-g4 and measured value d1 in the first stage of a two-stage search. Here, the measured value d1 represents the value (measured value) measured by the measurement sensor unit 21 (measurement sensors A1 to An) when a signal source 31 is present at the measurement target point D1.

[0063] As shown in Figure 13B, in the first stage of the search, the theoretical signal value g2 with the highest similarity to the measured value d1 is identified, and as shown in Figure 13A, the primary candidate point G2 corresponding to that theoretical signal value g2 is determined as the target point. In the second stage of the search, the spotted point is used as a reference point, and a more detailed search is conducted using primary and secondary candidate points.

[0064] Figure 13C shows an example of candidate points (primary candidate point G2 and secondary candidate points F11-F13, F21-F22) and measurement target point D1 in the second stage of a two-stage search. Figure 13C shows an XYZ Cartesian coordinate system similar to that shown in Figure 5. Figure 13D shows an example of the theoretical signal values ​​g2, f11-f13, f21-f22, and measured value d1 in the second stage of a two-stage search.

[0065] As shown in Figure 13D, in the second stage of the search, the theoretical signal value f12 with the highest similarity to the measured value d1 is identified, and as shown in Figure 13C, the secondary candidate point F12 corresponding to that theoretical signal value f12 is determined as the final estimated position. In the examples shown in Figures 13A to 13D, the final estimated position result is shown to be reasonable.

[0066] <Examples of problems that may occur with two-stage search techniques> Next, we will show examples of when the problem occurs, referring to Figures 14A to 14D. In the first stage of the search, a rough search is performed using the primary candidate points.

[0067] Figure 14A shows an example of the primary candidate points G1-G4 and the measurement target point D11 in the first stage of a two-stage search. Figure 14A shows an XYZ Cartesian coordinate system similar to that shown in Figure 5. Figure 14B shows an example of theoretical signal values ​​g1-g4 and measured value d11 in the first stage of a two-stage search. Here, the measured value d11 represents the value (measured value) measured by the measurement sensor unit 21 (measurement sensors A1 to An) when a signal source 31 is present at the measurement target point D11.

[0068] As shown in Figure 14B, in the first stage of the search, the theoretical signal value g2 with the highest similarity to the measured value d11 is identified, and as shown in Figure 14A, the primary candidate point G2 corresponding to that theoretical signal value g2 is determined as the target point. In the second stage of the search, the spotted point is used as a reference point, and a more detailed search is conducted using primary and secondary candidate points.

[0069] Here, the search range used in the second stage of the search is predetermined for each candidate point (in this example, the primary candidate point). In this example, the second-stage search range defined for each candidate point (in this example, the primary candidate point) does not overlap with each other.

[0070] Figure 14C shows an example of candidate points (primary candidate point G2 and secondary candidate points F11-F13, F21-F22) and measurement target point D11 in the second stage of a two-stage search. Figure 14C shows an XYZ Cartesian coordinate system similar to that shown in Figure 5. Figure 14D shows an example of the theoretical signal values ​​g2, f11-f13, f21-f22, and measured value d11 in the second stage of a two-stage search.

[0071] As shown in Figure 14D, in the second stage of the search, the theoretical signal value f13 with the highest similarity to the measured value d11 is identified, and as shown in Figure 14C, the secondary candidate point F13 corresponding to that theoretical signal value f13 is determined as the final estimated position. However, in the examples in Figures 14A to 14D, the final estimated position result is not valid. Thus, as in the examples in Figures 14A to 14D, in a two-stage search configuration that searches for the signal theoretical value with the highest similarity to the measured value, the most valid candidate point may not be identified. This is because, as a result of the coarse search in the first stage, the true correct position (the real position corresponding to the measured value) may fall outside the search range of the second stage.

[0072] <Explanation that no problems occur with one-stage search technology> Furthermore, referring to Figures 15A to 15B, we will explain that even if a signal source 31 is present at measurement point D11, similar to the example in Figures 14A to 14D, no problems will occur with the one-stage search technique. In a single-stage search, the location of the signal source 31 is estimated only once, using all candidate points (primary candidate points and secondary candidate points).

[0073] Figure 15A shows an example of candidate points (primary candidate points G1-G4, secondary candidate points F1-F4, F11-F16, F21-F24) and measurement target point D11 in a one-stage search. Figure 15A shows an XYZ Cartesian coordinate system similar to that shown in Figure 5. Figure 15B shows an example of theoretical signal values ​​g1-g4, f1-f4, f11-f16, f21-f24, and measured value d11 in a one-step search. Here, the measured value d11 and the measurement target point D11 are the same as in the examples in Figures 14A to 14D.

[0074] As shown in Figure 15B, in the one-step search, the theoretical signal value f14 with the highest similarity to the measured value d11 is identified, and as shown in Figure 15A, the position of the secondary candidate point F14 corresponding to that theoretical signal value f14 is determined as the final estimated position. Thus, the final estimated position result is reasonable in a one-stage search. However, while one-stage search offers good accuracy in position estimation because it uses detailed candidate points from the start, it suffers from the problem of requiring a large amount of computation and taking a long processing time.

[0075] [Processing of a two-stage search according to the first embodiment] In this embodiment, compared to the first-stage search process (referred to as the first-stage search process related to the base technology for convenience of explanation) and the second-stage search process (referred to as the second-stage search process related to the base technology for convenience of explanation) in the two-stage search described with reference to Figures 13A to 13D and Figures 14A to 14D, the first-stage search performs a different process (in this embodiment, a process using boundary point walls) than the first-stage search process related to the base technology, while the second-stage search performs a process similar to the second-stage search process related to the base technology.

[0076] For the sake of explanation, in this example, we will use a portion of the candidate point group shown in Figure 5 and a portion of the theoretical signal value group shown in Figure 6 to explain the two-stage search process according to the first embodiment. Furthermore, in this example, for the sake of simplicity, we will explain the case where the point to be measured lies on a two-dimensional plane (a plane parallel to the XY plane) formed by multiple candidate points. However, the same applies when the point to be measured lies at an arbitrary position in three-dimensional space (any position in the XYZ Cartesian coordinate system).

[0077] Referring to Figures 7A to 7E, the processing of the first stage of the two-stage search according to the first embodiment will be explained. Figure 7A shows an example of boundary points set in the candidate point group in the first stage according to the first embodiment. Figure 7A shows an XYZ Cartesian coordinate system similar to that shown in Figure 5. Figure 7B shows an example of a boundary point set in the signal theoretical value group in the first stage according to the first embodiment.

[0078] Figure 7A shows the main candidate points G1 to G4, the measurement target point D2, the two boundary points J1 to J2, and two lines (referred to as boundary normals K1 to K2 for ease of explanation). Note that Figure 7A shows a hypothetical line (parallel to the XY plane) perpendicular to the line connecting primary candidate point G3 and primary candidate point G4 and passing through boundary point J1, and a hypothetical line (parallel to the XY plane) perpendicular to the line connecting primary candidate point G2 and primary candidate point G3 and passing through boundary point J2. However, these lines do not necessarily have to be defined.

[0079] Figure 7B shows the theoretical signal values ​​g1 to g4, the measured value d2, the two boundary points j1 to j2, and the two lines (referred to as boundary normals k1 to k2 for convenience of explanation). Furthermore, Figure 7B shows axes p1 and q1 for boundary point j1, and axes p2 and q2 for boundary point j2. Here, the measured value d2 represents the value (measured value) measured by the measurement sensor unit 21 (measurement sensors A1 to An) when the signal generation source 31 is present at the measurement target point D2.

[0080] In the example in Figure 7A, boundary point J1 is the midpoint between primary candidate point G3 and primary candidate point G4, and boundary point J2 is the midpoint between primary candidate point G2 and primary candidate point G3. In the example shown in Figure 7B, boundary point j1 in the signal theoretical value space is the signal theoretical value corresponding to boundary point J1 in the three-dimensional space, and boundary point j2 in the signal theoretical value space is the signal theoretical value corresponding to boundary point J2 in the three-dimensional space. In this embodiment, the signal theoretical value calculation unit 172 calculates the signal theoretical value corresponding to boundary point J1 and the signal theoretical value corresponding to boundary point J2. Furthermore, for boundary point j1, two axes (axis p1 and axis q1) are shown that span a separating hyperplane, which is a plane perpendicular to its boundary normal k1. Similarly, for boundary point j2, two axes (axis p2 and axis q2) are shown that span a separating hyperplane, which is a plane perpendicular to its boundary normal k2.

[0081] In the example in Figure 7A, the boundary normal K1 in three-dimensional space corresponds to the boundary normal k1 in the signal theoretical value space, and the boundary normal K2 in three-dimensional space corresponds to the boundary normal k2 in the signal theoretical value space.

[0082] In the example shown in Figure 7A, two boundary points (boundary points J1 and J2) are shown with respect to the main candidate point G3, but there are four other boundary points (not shown). In other words, in this embodiment, for each primary candidate point, there are boundary points located at predetermined distances in the positive X-axis direction, the negative X-axis direction, the positive Y-axis direction, the negative Y-axis direction, the positive Z-axis direction, and the negative Z-axis direction, resulting in a total of six boundary points. The predetermined distance is, for example, half the distance between the primary candidate point and other primary candidate points adjacent to it in each direction.

[0083] Figure 7C shows an example of auxiliary points set in the candidate point group in the first stage according to the first embodiment. Figure 7D shows an example of auxiliary points set in the signal theoretical value group in the first stage according to the first embodiment.

[0084] Figure 7C shows the main candidate points G3-G4, the measurement target point D2, two boundary points J1-J2, two boundary normals K1-K2, and two points related to boundary point J1 (referred to as auxiliary points I1-I2 for convenience of explanation) in relation to the state shown in Figure 7A. Figure 7D shows the theoretical signal values ​​g1 to g4, the measured value d2, the two boundary points j1 to j2, the two boundary normals k1 to k2, the axes p1 and q1 for boundary point j1, and the axes p2 and q2 for boundary point j2, relating to the state shown in Figure 7B. Figure 7D also shows two points related to the boundary point j1 (referred to as auxiliary points i1 and i2 for convenience of explanation), the origin O1 in the signal theoretical value space, the direction c1 from the origin O1 toward the measured value d2, and the direction c2 from the origin O1 toward the boundary point j1. Here, auxiliary point i1 in the signal theoretical value space is the signal theoretical value corresponding to auxiliary point I1 in three-dimensional space, and auxiliary point i2 in the signal theoretical value space is the signal theoretical value corresponding to auxiliary point I2 in three-dimensional space. In this embodiment, the signal theoretical value calculation unit 172 calculates the signal theoretical value corresponding to auxiliary point I1 and the signal theoretical value corresponding to auxiliary point I2.

[0085] <Auxiliary points and boundary normals> In this example, auxiliary points and boundary normals are explained using boundary point J1 in three-dimensional space and boundary point j1 in the signal theoretical value space as examples, but the same applies to other boundary points. For the boundary point J1 between the primary candidate point G3 and the primary candidate point G4, auxiliary point I1 is a point shifted by a predetermined value from the boundary point J1 in the direction of primary candidate point G4 from primary candidate point G3, and auxiliary point I2 is a point shifted by the same predetermined value from the boundary point J1 in the direction of primary candidate point G3 from primary candidate point G4. Here, the predetermined value may be a value smaller than, for example, the distance between the boundary point J1 and the primary candidate point G3 (the same applies to the distance between the boundary point J1 and the primary candidate point G4).

[0086] In the example shown in Figure 7D, for boundary point j1, the boundary normal k1 with respect to the theoretical signal value g3 is a straight line that points from auxiliary point i1 to auxiliary point i2. Furthermore, for boundary point j1, the boundary normal with respect to the theoretical signal value g4 is a straight line in the opposite direction to the boundary normal k1 with respect to the theoretical signal value g3.

[0087] Figure 7E shows an example of a separated hyperplane set in the signal theoretical value group in the first stage according to the first embodiment. Figure 7E shows the boundary normal k1 and the separating hyperplane 311, which is the plane spanned by the two axes (axis p1 and axis q1) at the boundary point j1.

[0088] <An example of the procedure for the first stage of the search> Referring to the examples in Figures 7A to 7E, (Steps Q1) to (Steps Q4) are shown as an example of the procedure for the first stage of the search.

[0089] (Procedure Q1) From the set of candidate points, a group of coarse candidate points (primary candidate points in this embodiment) are selected to be used as the search range. Furthermore, for each of these coarse candidate points, boundary points are set between it and other adjacent coarse candidate points in the directions parallel to the X-axis, Y-axis, and Z-axis. In this embodiment, the boundary points are set as the midpoint between two adjacent coarse candidate points.

[0090] In the example shown in Figure 7A, the main candidate points G1 to G4 are shown as rough candidate points for the search range. Furthermore, for the primary candidate point G3, two boundary points, boundary point J1 and boundary point J2, are shown. In the example shown in Figure 7A, although not shown in the illustration, six boundary points are set for the primary candidate point G3. Similarly, six boundary points are set for each of the other primary candidate points G1, G2, and G4.

[0091] (Procedure Q2) In the signal theory space, a predetermined wall (which we will also call a boundary wall for convenience of explanation) is drawn between the boundary points in the signal theory space that correspond to each boundary point in the three-dimensional space. In other words, we define the boundary wall as a separating hyperplane that passes through the signal points generated by each point (boundary point in three-dimensional space).

[0092] In the example shown in Figure 7B, in the signal theoretical value space, the boundary wall for boundary point j1 is composed of a plane (separating hyperplane) containing axes p1 and q1, and the boundary wall for boundary point j2 is composed of a plane (separating hyperplane) containing axes p2 and q2.

[0093] (Procedure Q3) In the signal theory space, based on the positional relationship between the measured value d2 and each boundary wall, it is determined whether the measured value d2 lies inside all the boundary walls (in this example, 6 boundary walls) surrounding any given signal theory value (the signal theory value corresponding to the primary candidate point). In this process, for each boundary wall, it is determined which side of the boundary wall the measured value d2 lies on. Here, for each primary candidate point, six boundary walls are generated in the signal theoretical value space from the six boundary points that exist three-dimensionally around that primary candidate point, and an inner region (subspace) enclosed by these six boundary walls is generated.

[0094] In the example in Figure 7B, for both the boundary wall at boundary point j1 and the boundary wall at boundary point j2, it is determined that the measured value d2 is on the side of the theoretical signal value g3 (the theoretical signal value g3 corresponding to the main candidate point G3).

[0095] (Procedure Q4) In the signal theory space, if it is determined that the measured value d2 lies inside all the boundary walls (in this example, 6 boundary walls) surrounding a single signal theory value (a signal theory value corresponding to a primary candidate point), then the primary candidate point corresponding to that signal theory value is determined as the target point.

[0096] In the examples shown in Figures 7A and 7B, it is determined that the measured value d2 lies inside all the boundary walls surrounding the theoretical signal value g3 (six boundary walls in this example), and the primary candidate point G3 corresponding to the theoretical signal value g3 is determined as the target point. Furthermore, the fact that the measured value d2 lies inside all the boundary walls surrounding the theoretical signal value g3 (in this example, 6 boundary walls) corresponds to the measured value d2 lies inside a predetermined region surrounding the theoretical signal value g3.

[0097] Thus, in the first stage of the search process according to this embodiment, a boundary wall is set in the signal theoretical value space for each of the coarse candidate points (primary candidate points), and a target point is set based on the positional relationship between the boundary wall and the measured value d2. This determines whether the measured value d2 belongs to the area assigned to each coarse candidate point (primary candidate point) (in this example, the area inside the six boundary walls), thereby suppressing (ideally eliminating) the oversight of the correct point. In other words, in this embodiment, during coarse estimation, instead of using only information from sparse candidate points (primary candidate points), information from the vicinity of the sparse candidate points (primary candidate points) is reflected to determine the correct target point.

[0098] In step Q4, if the condition that the measured value d2 is surrounded by all boundary walls (six boundary walls in this example) around a single theoretical signal value is not met (i.e., such a theoretical signal value does not exist), then, for example, a coarse candidate point with the highest similarity to the measured value d2 (the primary candidate point in this embodiment) is used as the target point. In other words, in this case, the first stage of the search is performed in the same way as the first stage of the search related to the base technology.

[0099] [Supplementary explanation for calculating similarity and determining boundary walls] Up to this point, in order to simplify the explanation, we have assumed that the field where the signal source 31 is generated is a scalar (the only variations in activity are position and magnitude), and have described the signal theoretical value space using a three-dimensional schematic diagram. However, let me provide some supplementary explanation regarding the generalized case. In other words, the signal source 31 may actually have a directional parameter, and the theoretical signal space has the same number of dimensions as the number of measurement sensors A1 to An (n dimensions in this embodiment).

[0100] Here, we will explain a case in which an object being tracked (in this embodiment, the signal source 31) may generate different signal patterns even if its position is the same. For example, when the signal source 31 is a coil such as a circular one, even if the position of the signal source 31 is determined, the pattern of the signal generated from the signal source 31 may not be uniquely determined depending on the orientation of the signal source 31. In other words, if the signal source 31 is a coil, even if the position of the signal source 31 is determined, the pattern of the signal generated from the signal source 31 (in this embodiment, the spatial pattern of the magnetic field generated from the coil) may change depending on the orientation of the coil (for example, the orientation of the normal to the coil).

[0101] Although the magnitude of the current flowing through the coil can also be a parameter, in this embodiment, we will assume that the magnitude of the current flowing through the coil is fixed to a predetermined value. As another example, the magnitude of the current flowing through the coil may also be considered as a parameter.

[0102] <Supplementary explanation of similarity calculation> This section describes the similarity score of a vector field source (when the signal source 31 has a direction parameter). Let lx be the signal (column vector) generated when the signal source 31 is active in the direction (X,Y,Z)=(1,0,0) at the point of interest (point p, which is one candidate point in this embodiment), let ly be the signal (column vector) generated when the signal source 31 is active in the direction (X,Y,Z)=(0,1,0), and let lz be the signal (column vector) generated when the signal source 31 is active in the direction (x,y,z)=(0,0,1). Let Lp = [lx, ly, lz] be the matrix formed by arranging these three column vectors horizontally.

[0103] Given a measured value w, the estimated value of the three-dimensional activity vector s of the signal source 31 at the point of interest can be obtained by multiplying it by the pseudoinverse (sometimes called the pseudo-inverse) of the matrix Lp, so s = (pseudoinverse of Lp)w. The pseudoinverse of matrix Lp may, for example, be calculated in advance.

[0104] (Lps), which is the result of multiplying the three-dimensional action vector s by the matrix Lp, is the signal pattern that is most similar to the measured value pattern among the signal patterns that point p can generate. (Lps) is, in linear algebra terms, the orthogonal projection of the measured value w onto the subspace of signals that point p can generate in signal space (LpLp + w) is the case here (Lp + The "+" in ) indicates that it is a pseudoinverse matrix. In this embodiment, the evaluation is performed based on the similarity between "the signal that point p can produce that is closest to w" and "w".

[0105] The absolute value of the angle between the measured value w and (Lps) is used as the similarity score. In this case, the smaller the absolute value of the angle, the higher the similarity. Here, any quantity (value) related to such angles may be used as the degree of similarity. For example, the angle between the measured value w and the orthogonal projection of the signal from the measured value w to the subspace from which the point of interest (point p) may be generated may be used as the similarity score. As another example, the magnitude of the orthogonal projection (=Lps / |w|) of the normalized measurement w (w / |w|) may be used as the similarity score. In this case, the larger the magnitude, the higher the similarity. As another example, the inner product of (w / |w|) and its orthogonal projection (=Lps / |w|) may be used as the similarity score. In this case, the larger the inner product, the higher the similarity.

[0106] As another example, the difference between the measured value w and (Lps) may be used as the similarity score. In this case, the larger the difference, the lower the similarity. Furthermore, using such differences as a measure of similarity may be done in all of the above examples. However, when using such differences, normalization is necessary first.

[0107] In general, calculating the pseudoinverse matrix of (Lp) is computationally expensive, but in this embodiment, it is sufficient to calculate it only once for all candidate points before tracking begins. For example, even when tracking is performed in real time, it is not necessary to calculate the pseudoinverse matrix in real time each time.

[0108] The size of matrix Lp is such that the number of rows (vertical) is the same as the number of measurement sensors A1 to An (n), and the number of columns (horizontal) is 3. The size of the (pseudoinverse matrix of matrix Lp) is 3 rows (vertical) and the same number of columns (horizontal) as the number of measurement sensors A1 to An (n). s is a three-dimensional vector. The measured value w is a vector with the same number of dimensions (n) as the number of measurement sensors A1 to An.

[0109] <Supplementary explanation regarding boundary wall determination> In this embodiment, for the sake of explanation, in the three-dimensional space shown in Figure 5, points (auxiliary points in this embodiment) are set before and after a certain direction relative to a single boundary point. In this embodiment, the predetermined directions are the directions parallel to the X-axis, the directions parallel to the Y-axis, and the directions parallel to the Z-axis, respectively. These auxiliary points may be set from among the candidate point groups, as shown in the example in Figure 5, or they may be points other than those in the candidate point groups.

[0110] In this example, we will explain the case where the predetermined direction is parallel to the X-axis. The same applies when the predetermined direction is any other direction. In other words, in this example, if the normal of the boundary wall is parallel to the X-axis, auxiliary points are set at a point slightly in the positive direction of the X-axis from the boundary point, and at a point slightly in the negative direction of the X-axis from that boundary point.

[0111] Here, let ax be the signal (column vector) generated when the signal source 31 is active at (X,Y,Z)=(1,0,0) at the boundary point, let ay be the signal (column vector) generated when the signal source 31 is active at (X,Y,Z)=(0,1,0), and let az be the signal (column vector) generated when the signal source 31 is active at (X,Y,Z)=(0,0,1). Let Ab = [ax, ay, az] be the matrix formed by arranging these three column vectors horizontally.

[0112] Furthermore, a similar matrix AP is used for auxiliary points located in the positive direction of the X-axis relative to the boundary point, and a similar matrix AM is used for auxiliary points located in the negative direction of the X-axis relative to the boundary point. Then, AP-AM=Ad is defined.

[0113] Given a measured value w, the value of the three-dimensional activity vector sb of the signal source 31 at the boundary point that best approximates the measured value w can be obtained by multiplying by the pseudoinverse of matrix Ab, so sb = (pseudoinverse of Ab)w. The pseudo-inverse of matrix Ab may, for example, be calculated in advance.

[0114] Furthermore, if the dot product of (Ad sb) and {w-(Ad sb)} is positive, it is determined that the signal source 31 is located on the positive side of the X-axis relative to the boundary wall. On the other hand, if the dot product is negative, it is determined that the signal source 31 is located on the negative side of the X-axis relative to the boundary wall.

[0115] The sizes of matrices Ab, AP, AM, and Ad are such that the number of rows (vertical) is the same as the number of measurement sensors A1 to An (n), and the number of columns (horizontal) is 3. The size of the pseudo-inverse matrix of matrix Ab is 3 rows (vertical) and the number of columns (horizontal) is the same as the number of measurement sensors A1 to An (n). sb is a three-dimensional vector. (Ad sb) is a vector with the same number of dimensions (n) as the number of measurement sensors A1 to An. The measured value w is a vector with the same number of dimensions (n) as the number of measurement sensors A1 to An.

[0116] [Example of a multi-stage processing procedure] Figure 8 shows an example of the procedure for a multi-stage search according to this embodiment. An example of the procedure for a multi-stage search performed by the information processing device 11 is shown. In this example, we will explain both two-step search processes and three-step or more search processes together.

[0117] In this example, it is assumed that in the information processing device 11, a group of candidate points is set and stored in advance by the candidate point setting unit 171, and a group of theoretical signal values ​​is calculated and stored by the theoretical signal value calculation unit 172. Furthermore, the setting of candidate point groups and the calculation of signal theoretical values ​​only need to be performed once before the processing flow shown in Figure 8 occurs, for example, if there are no changes to these. On the other hand, if there are changes to these, the setting of candidate point groups and the calculation of signal theoretical values ​​may be performed at any time to update the previously set candidate point groups and signal theoretical values.

[0118] The processes (steps S1) to (steps S4) performed in the information processing device 11 will be explained. (Step S1) The position calculation unit 173 performs the first stage of a multi-stage search based on the measured value acquired by the measurement sensor unit 21, the information of a pre-set candidate point group, and the information of a pre-calculated signal theoretical value, thereby estimating the rough position of the measured value. Then, the position calculation unit 173 proceeds to the processing of step S2.

[0119] (Step S2) The position calculation unit 173 performs the next stage of the multi-stage search (the second stage in the initial step S2) to estimate the precise position of the measured value. Then, the position calculation unit 173 proceeds to the processing of step S3.

[0120] (Step S3) The position calculation unit 173 determines whether or not to terminate the multi-stage search. If the position calculation unit 173 determines, based on this determination, that it has terminated the multi-stage search (step S3: YES), it proceeds to the process in step S4. On the other hand, if the position calculation unit 173 determines, as a result of this determination, that the multi-stage search will not be terminated (i.e., will continue) (step S3: NO), it proceeds to the process of step S2.

[0121] (Step S4) When the position calculation unit 173 terminates the multi-stage search, it determines the position that has been finally determined at that point. Then, the processing of this flow is completed.

[0122] In this case, if a two-stage search is performed as a multi-stage search, the combination of the processes in step S1 and step S2 is performed once, and then the process moves on to step S4. In a two-stage search, during the processing of step S1 (the first stage of the search), one main candidate point is determined from among the main candidate points by a determination based on the positional relationship between the boundary wall and the measured value in the signal theoretical value space. Furthermore, in the process of step S2 (the second stage of the search), using the search range based on the primary candidate point determined in the first stage of the search, one candidate point is determined among the candidate points (primary candidate point and secondary candidate point) that corresponds to the single signal theoretical value with the highest similarity to the measured value.

[0123] On the other hand, if a third or subsequent stage of searching is performed, the process in step S2 is performed two or more times. In this case, in the second and subsequent steps of step S2, the next spot point is determined using the search range corresponding to the spot point determined in the previous stage, and in this case, for example, a finer search than in the previous stage may be performed.

[0124] For example, in a multi-stage search with three or more stages, the process may start with an initial search range in the first stage, then repeatedly perform the following steps: determine the positional relationship between the boundary wall and the measured value, determine candidate points (spot points) based on the determination result, and set a narrower search range than the previous stage, until the final stage determines the final position (candidate point) using similarity. In this example, we have shown a multi-stage search with three or more stages in which boundary wall-based determination is performed at all stages except the final stage. However, as another example, a configuration in which boundary wall-based determination is performed at some stages (at least one stage) other than the final stage, and similarity-based determination is performed at the other stages in the same way as at the final stage, is also possible.

[0125] In this case, if a two-stage search is performed, for example, in the first stage of the search, a search may be performed using only candidate points (primary candidate points in this embodiment) at predetermined intervals from all candidate points, and in the second stage of the search, a search may be performed using all candidate points (primary candidate points and secondary candidate points in this embodiment).

[0126] Furthermore, if a search is performed in three or more stages, the configuration may be such that, for example, the search is performed using the fewest candidate points in the first stage, and as the number of stages increases, candidate points are gradually added to perform the search using more candidate points, and in the final stage, the search is performed using the most candidate points (for example, all candidate points). In general terms, in multi-stage search, a broad (coarse) search range is used for searches with fewer stages, while a narrower (more detailed) search range may be used as the number of stages increases.

[0127] Furthermore, the number of steps (number of times) in a multi-stage search may be predetermined, for example, two steps or three steps. As an example, in the information processing device 11, the user may set a desired number of steps by operating the operation unit of the input unit 111, and this information may be stored in the storage unit 113 and referenced by the processing unit 151 when a multi-stage search is performed.

[0128] As another example, the number of steps (times) in a multi-step search may be determined after the multi-step search has started. For example, the processing unit 151 may terminate the multi-step search process if it determines that a predetermined condition has been met while executing the multi-step search process. As the predetermined conditions, for example, the condition that the similarity between the measured value and the determined theoretical signal value is equal to or greater than a predetermined value may be used, or the condition that the similarity in the current stage has improved by a predetermined value or more compared to the similarity in the previous stage.

[0129] Furthermore, although this embodiment shows a case where multiple candidate points are distinguished into two stages, such as primary candidate points and secondary candidate points, for example, multiple candidate points may be distinguished into three or more stages, and the candidate points of each stage may be used in stages corresponding to and beyond.

[0130] (Regarding the first embodiment) As described above, the information processing device 11 in the information processing system 1 according to this embodiment can suppress the final position estimation result from deviating from the correct value in multi-stage searches such as two-stage searches.

[0131] In the information processing system 1 according to this embodiment, when tracking the position of an object, the multi-stage search is divided into two stages (or three or more stages) of estimation: a coarse estimation and a fine estimation. This makes it possible to improve the accuracy of the estimated position (e.g., resolution accuracy) with the same or similar computational cost as conventional methods. In this embodiment, when making a rough estimate, instead of using only information on sparse candidate points (primary candidate points in this embodiment), information on neighboring points is incorporated to enable the determination of the correct target point. This makes it possible to suppress (ideally eliminate) the occurrence of determining a spot point different from the correct value in the rough estimation stage of a multi-stage search (e.g., the first stage).

[0132] Thus, in this embodiment, when performing a coarse estimation in a multi-stage search, by using not only the theoretical signal value at the coarsely chosen position but also the theoretical signal value in the surrounding area, it is possible to eliminate the cause of failure in the final estimation. As a result, for example, it is possible to improve the accuracy of position estimation and resolution without compromising computation speed compared to conventional methods. The information processing system 1 according to this embodiment may be applied, for example, when tracking a location in real time, and can improve the accuracy of the tracking resolution while speeding up the calculations for tracking the location. In this embodiment, when tracking a location in real time, for example, it is possible to obtain a result in which the estimated location follows a path close to the correct location over time.

[0133] Now, let me explain the background to this disclosure. For example, in order to perform real-time position tracking with high position resolution accuracy and minimal delay, the position calculation algorithm is crucial. Typically, achieving high positional resolution requires large memory capacity and long computation times. For example, if the degree of matching between each candidate point and sensor measurements is determined, and the candidate point with the best degree of matching is used as the estimated position, increasing the positional resolution in three dimensions (space) by a factor of ten requires a factor of 1000 (for example, the number of candidate points would increase by a factor of 1000).

[0134] Therefore, as a method to improve positional resolution while keeping the computational load low, a two-stage search (two-stage estimation method) has been proposed, in which a rough estimate is used to determine a target point, and then a finer estimate is performed around that target point. As a concrete example, in a two-stage search, the process involves first estimating the position using candidate points spaced 1 cm apart, and then estimating the position again using candidate points spaced 1 mm apart within a 1 cm square area surrounding the determined candidate point. Here, 1 cm and 1 mm are just examples, and other arbitrary values ​​may be used. However, in such a two-stage search, the spot location determined in the first stage may deviate from a reasonable candidate location, and the correct location may not be included in the search range of the second stage.

[0135] In light of these circumstances, it was necessary to implement measures in the two-stage search to ensure that the correct location was included in the search range of the second stage. This embodiment provides an example of such countermeasures.

[0136] <Regarding Non-Patent Document 1> Non-patent document 1 describes an approach to estimating the distributed signal sources of electroencephalograms and magnetic fields by narrowing the region while increasing the resolution within that region. In Non-Patent Document 1, the distribution of values ​​is determined (the activity value of each point is determined). On the other hand, in this embodiment, a search for a single point (in this embodiment, one candidate point) is performed. In this embodiment, the calculation can be simplified, and a significant speedup can be achieved by not including complex processes such as elliptic determination as described in Non-Patent Document 1. Furthermore, while Non-Patent Document 1 does not necessarily narrow down the solution region to a single point, in this embodiment, the estimated position is determined to a single point. Furthermore, Non-Patent Document 1 does not address the issue of the correct answer point falling outside the set range of the coarse search, and if the correct answer point falls outside the set range of the coarse search, the correct answer point cannot be ultimately found. On the other hand, in this embodiment, the correct answer point is ultimately found when performing both coarse and fine searches, and speed and stability (reduction of error) are achieved in the search for a single point.

[0137] <Regarding Patent Document 1> Patent Document 1 describes a method for searching for the position and orientation of occurrences within a larger image by sliding and rotating a small template image. Furthermore, Patent Document 1 attempts to avoid missing any points in a coarse search by listing multiple candidate points, and to improve search accuracy by utilizing a parametric space in a finer search. In Patent Document 1, image matching is performed. On the other hand, in this embodiment, the matching is of a signal pattern (in this embodiment, the theoretical value of the signal), and it is also applicable when the signal from which the search point may be generated has degrees of freedom (for example, when the signal is variable). Furthermore, in this embodiment, the correct answer point is ultimately obtained when performing coarse and fine searches, and speed and stability (reduction of error) are achieved in the search for a single point.

[0138] (Second Embodiment) A second embodiment will be described.

[0139] [Information Processing Systems] In this embodiment, the configuration shown in Figures 1 to 6 according to the first embodiment is generally the same as in the first embodiment, and for convenience of explanation, the reference numerals shown in Figures 1 to 6 will be used for description. In this embodiment, instead of the configuration and operation described with reference to Figures 7A to 7E according to the first embodiment, the configuration and operation described with reference to Figures 9A to 9B are adopted.

[0140] [Processing of a two-stage search according to the second embodiment] In this embodiment, compared to the processing of the first stage of the two-stage search (processing of the first stage of the search relating to the base technology) and the processing of the second stage of the search (processing of the second stage of the search relating to the base technology) described with reference to Figures 13A to 13D and Figures 14A to 14D, the first stage of the search performs processing similar to the processing of the first stage of the search relating to the base technology, while the second stage of the search performs processing different from the processing of the second stage of the search relating to the base technology (in this embodiment, processing using a different search range).

[0141] For the sake of explanation, in this example, we will use a portion of the candidate point group shown in Figure 5 and a portion of the theoretical signal value group shown in Figure 6 to explain the two-stage search process according to the second embodiment. Furthermore, in this example, for the sake of simplicity, we will explain the case where the point to be measured lies on a two-dimensional plane (a plane parallel to the XY plane) formed by multiple candidate points. However, the same applies when the point to be measured lies at an arbitrary position in three-dimensional space (any position in the XYZ Cartesian coordinate system).

[0142] Referring to Figures 9A and 9B, the process of the second stage of the two-stage search according to the second embodiment will be explained. In this example, we assume that in the first stage of the search, the primary candidate point G2 (theoretical signal value g2) was determined as the target point. Figure 9A shows an example of the search range set for the candidate point group in the second stage according to the second embodiment. Figure 9A shows an XYZ Cartesian coordinate system similar to that shown in Figure 5. Figure 9B shows an example of the search range set for the signal theoretical value group in the second stage according to the second embodiment.

[0143] Figure 9A shows the measurement target point D3 and the candidate points that constitute the search range when the primary candidate point G2 is the target point (primary candidate point G2, secondary candidate points F11-F15, F21-F24). Figure 9B shows the measured value d3 and the theoretical signal values ​​g2, f11-f15, and f21-f24 that constitute the search range. Here, the measured value d3 represents the value (measured value) measured by the measurement sensor unit 21 (measurement sensors A1 to An) when the signal generation source 31 is present at the measurement target point D3.

[0144] Thus, in this embodiment, the search range used in the second stage of the search is predetermined for each candidate point (in this example, the primary candidate point). In this embodiment, the second-stage search range defined for each candidate point (in this example, the primary candidate point) has an overlapping portion with the second-stage search range of other candidate points adjacent to that candidate point. For example, the second-stage search range defined for each candidate point (in this example, the primary candidate point) may be such that the range from that candidate point toward other adjacent candidate points is before (for example, immediately before) those other candidate points.

[0145] Thus, in this embodiment, by widening the search range during the detailed estimation in the second stage of the search, even if the spot point deviates from a reasonable point in the first stage of the search, for example, the correct point can be included in the search range in the second stage of the search.

[0146] In the coarse search, it is not a problem if the search range for two adjacent candidate points (primary candidate points in this example) overlaps when one candidate point becomes a spot point. This is because the search range in the fine search is used after the spot points have been determined from the coarse candidate points (primary candidate points in this example) in the coarse search. For example, in a coarse search, even if one of two adjacent coarse candidate points (in this example, the primary candidate point) is determined to be the target point, all of the smaller candidate points (in this example, the secondary candidate points) that exist between these two coarse candidate points will be included in the search range, thereby reducing the chance of missing (ideally eliminating) the chance of missing the correct point.

[0147] Here, when setting the search range in a fine-grained search, it may be considered that, for example, a wider search range reduces the chance of missing a correct solution, but tends to increase the computational complexity. For example, in a detailed search using one candidate point (in this example, the primary candidate point) as the target point, the search range may not be limited to a range of less than half the distance to other adjacent candidate points (in this example, the primary candidate point), but rather to a range slightly shorter than the distance to those other candidate points (in this example, the primary candidate point). In other words, the search range in a detailed search using one candidate point as the target point may not be limited to less than half the distance from that candidate point to those other candidate points, but may be set to include minor candidate points (in this example, secondary candidate points) that are immediately before the distance from that candidate point to those other candidate points. As another example, in a fine-tuned search with one candidate point as the target point, the search range may be set to include the fine-tuned candidate points (in this example, secondary candidate points) that are immediately before the distance from that one candidate point to the next adjacent coarse candidate point (the candidate point two doors down, which in this example is the primary candidate point), beyond the other candidate points.

[0148] [Example of a multi-stage processing procedure] In this embodiment, an example of the procedure for the multi-stage search process performed by the information processing device 11 is, in general terms, the same as the example in Figure 8.

[0149] In this case, if a two-stage search is performed as a multi-stage search, the combination of the processes in step S1 and step S2 is performed once, and then the process moves on to step S4. In a two-stage search, in step S1 (the first stage of the search), one candidate point is determined that corresponds to the single signal theoretical value with the highest similarity to the measured value among the candidate points (primary candidate points). Furthermore, in the process of step S2 (the second stage of the search), using the search range based on the primary candidate point determined in the first stage of the search, one candidate point is determined among the candidate points (primary candidate point and secondary candidate point) that corresponds to the single signal theoretical value with the highest similarity to the measured value.

[0150] On the other hand, if a third or subsequent stage of searching is performed, the process in step S2 is performed two or more times. In this case, in the second and subsequent steps of step S2, the next spot point is determined using the search range corresponding to the spot point determined in the previous stage, and in this case, for example, a finer search than in the previous stage may be performed.

[0151] In addition, in multi-stage searches of three or more stages, a broader search range, as explained with reference to Figures 9A to 9B, may be used in some of the stages other than the first stage (at least one stage), or a broader search range, as explained with reference to Figures 9A to 9B, may be used in all stages other than the first stage.

[0152] (Regarding the second embodiment) As described above, the information processing system 1 according to this embodiment can suppress the occurrence of the final position estimation result deviating from the correct value in multi-stage searches such as two-stage searches.

[0153] In the information processing system 1 according to this embodiment, when tracking the position of an object, the multi-stage search is divided into two stages (or three or more stages) of estimation: a coarse estimation and a fine estimation. This makes it possible to improve the accuracy of the estimated position (e.g., resolution accuracy) with the same or similar computational cost as conventional methods. In this embodiment, by using a wider search range during detailed estimation, it is possible to include the correct answer within the search range even if, for example, a different spot point from the correct answer is determined in the previous stage (e.g., the first stage). This makes it possible to suppress (ideally eliminate) deviations in the final estimated position from the correct value, even if, for example, a spot point different from the correct value is determined in a rough estimation stage (e.g., the first stage) in a multi-stage search.

[0154] Thus, in this embodiment, when performing fine estimation in a multi-stage search, the search range can be overlapped with adjacent candidate points (primary candidate points in this embodiment) in the coarse estimation, thereby eliminating the cause of failure in the final estimation. As a result, for example, the accuracy of position estimation and resolution can be improved without significantly reducing the computation speed compared to conventional methods. The information processing system 1 according to this embodiment may be applied, for example, when tracking a location in real time, and can improve the accuracy of the tracking resolution while speeding up the calculations for tracking the location. In this embodiment, when tracking a location in real time, for example, it is possible to obtain a result in which the estimated location follows a path close to the correct location over time.

[0155] As explained in the background of the first embodiment, in the two-stage search, measures were needed to ensure that the location of the correct answer was included in the search range of the second stage. This embodiment provides an example of such countermeasures.

[0156] (Combination of the first and second embodiments) For example, as a two-stage search process, an information processing device 11 may be implemented that performs the first-stage search process according to the first embodiment and the second-stage search process according to the second embodiment.

[0157] With this configuration, the information processing system 1 can suppress the occurrence of the final position estimation result deviating from the correct value in multi-stage searches such as two-stage searches. In this example, measures are taken to prevent the final position estimation result from deviating from the correct value in both coarse and fine searches.

[0158] (Third embodiment) A third embodiment will be described.

[0159] [Information Processing Systems] Figure 10 shows an example configuration of an information processing system 501 including an information processing device 511 according to an embodiment. The information processing system 501 comprises an information processing device 511 and a measurement sensor unit 521. Figure 10 also shows multiple signal sources B1 to Bm (where m is an integer greater than or equal to 2), and a communication channel C2. In this embodiment, the number of signal sources B1 to Bm (m) may be referred to as, for example, the number of channels. Furthermore, the information processing system 501 may be considered to include either or both of the signal sources B1 to Bm and the communication channel C2.

[0160] <Signal source of the signal generation unit> Figure 10 shows the signal generation unit 522 for the sake of explanation. In this embodiment, the signal generation unit 522 includes m signal sources B1 to Bm. Note that the m signal sources B1 to Bm do not necessarily have to be a single entity, and may exist in a dispersed manner.

[0161] Each signal source B1 to Bm generates a predetermined signal. In this embodiment, the case is shown where each signal source B1 to Bm generates a signal of a different frequency. Here, the number (m in this embodiment) and arrangement of the multiple signal sources are not limited to the example in Figure 10, and any configuration may be used. For example, the number of signal sources may be two, or it may be three or more. Furthermore, in the example shown in Figure 10, the multiple signal sources B1 to Bm are arranged in an array, but the arrangement of the multiple signal sources can be arbitrary. The array-like structure may also be referred to as a matrix-like structure, for example.

[0162] In this embodiment, the multiple signal sources B1 to Bm are located in fixed positions without movement, but in other examples, they may be movable. Furthermore, if multiple signal sources B1 to Bm are movable, the information processing device 511 is configured to be able to grasp the movement status (change in position) of the multiple signal sources B1 to Bm, and the information processing device 511 corrects the measurement results from the measurement sensor unit 521 so that the multiple signal sources B1 to Bm are considered to be in fixed positions.

[0163] <Measurement Sensor Unit> The measurement sensor unit 521 has one measurement sensor 531. In this embodiment, the measurement sensor unit 521 and the single measurement sensor 531 may be considered equivalent.

[0164] In this embodiment, the measurement sensor unit 521 is movable. Here, for example, the measurement sensor unit 521 may have a movable drive unit, or the measurement sensor unit 521 may be mounted on a predetermined mobile body, and the measurement sensor unit 521 may move as the mobile body moves. The moving object may be any living or non-living object, such as a person, animal, robot, vehicle, ship, or airplane.

[0165] In this embodiment, the information processing device 511 and the measurement sensor 531 are connected to each other via a wired or wireless communication channel C2. The information processing device 511 is then capable of acquiring measurement results from the measurement sensor 531. In this embodiment, the information processing device 511 is shown to acquire the measurement results from the measurement sensor 531 by communicating with the measurement sensor 531. However, as another example, a configuration may be used in which the measurement results from the measurement sensor 531 are first stored in a portable storage medium, and then the measurement results are output from the storage medium to the information processing device 511, thereby allowing the information processing device 511 to acquire the measurement results.

[0166] In this embodiment, the information processing device 511 and the measurement sensor unit 521 are shown as separate components. However, in other examples, the information processing device 511 and the measurement sensor unit 521 may be integrated, in which case the communication channel C2 may not be provided. For example, the example in Figure 10 shows the case where the measurement sensor unit 521 is located outside the information processing device 511, but the measurement sensor unit 521 and the information processing device 511 do not necessarily have to be clearly distinguished as being inside or outside.

[0167] The measurement sensor 531 measures the signals generated from each of the signal sources B1 to Bm. In this embodiment, the measurement sensor 531 measures the signals generated from each of the signal sources B1 to Bm in a non-contact manner.

[0168] <Information Processing Device> Figure 11 shows an example of the configuration of an information processing device 511 according to an embodiment. Here, the configuration example of the information processing device 511 according to this embodiment is, in general terms, the same as the configuration example shown in Figure 2, except that the processing unit 151 included in the control unit 114 shown in Figure 2 according to the first embodiment is the same as the configuration example shown in Figure 2. In this embodiment, for the sake of convenience of explanation, components similar to those shown in Figure 2 will be denoted by the same reference numerals as in Figure 2.

[0169] The information processing device 511 comprises an input unit 111 having an acquisition unit 131, an output unit 112 having a display unit 141, a storage unit 113, and a control unit 514. The control unit 514 comprises a processing unit 551 and a display control unit 152.

[0170] <Processing unit in information processing device> The processing unit 551 includes a candidate point setting unit 571, a signal theoretical value calculation unit 572, a position calculation unit 573, and a signal separation unit 574.

[0171] The signal separation unit 574 separates the measured signal (signal of the measured value) into m signals (for convenience of explanation, also called separated signals) based on the information of the measurement result from the measurement sensor unit 521 (measurement sensor 531). In this embodiment, each of the m separated signals corresponds to each of the m signal sources B1 to Bm, and each signal has a frequency corresponding to the signal generated by the respective signal source B1 to Bm. In other words, in this embodiment, the signal separation unit 574 separates the measured signal (measured value signal) into m signals of different frequencies (separated signals). The separation of signals may also be called signal splitting or signal conversion, for example.

[0172] The candidate point setting unit 571 sets a group of candidate points that includes multiple candidate points. The candidate point setting unit 571 sets multiple candidate points to be placed in the three-dimensional space where the measurement sensor 531 and signal sources B1 to Bm exist. In this embodiment, the multiple candidate points include multiple primary candidate points and multiple secondary candidate points.

[0173] The signal theoretical value calculation unit 572 calculates the estimated signal theoretical value that would be obtained if the measurement sensor 531 were located at one of several different locations (which, for convenience of explanation, will also be called a temporary location). In this embodiment, the theoretical signal value is the theoretical signal value obtained by estimating the values ​​of multiple separated signals (time-series multidimensional signals) acquired by the signal separation unit 574, and is a set of time-series measurement results corresponding to each signal source B1 to Bm (each frequency in this embodiment). Thus, in this embodiment, the signal theoretical value calculation unit 572 calculates m sets of signal theoretical values ​​for one provisional position.

[0174] The position calculation unit 573 calculates the position of the measurement sensor 531 based on the information of the candidate point group set by the candidate point setting unit 571, the information of the calculation result by the signal theoretical value calculation unit 572, and the information of m separated signals acquired by the signal separation unit 574. In this embodiment, the position calculation unit 573 calculates the position of the measurement sensor 531 by performing a multi-stage search (for example, a two-stage search). As a result, the position calculation unit 573 may, for example, track the position of the moving measurement sensor 531. In this embodiment, the position may be calculated, for example, by estimating the position.

[0175] <Overview of processing in information processing systems> Figure 12 is a diagram showing an example of the general processing in the information processing system 501 according to this embodiment. The candidate point setting unit 571 provides candidate point group information (candidate point group information a11) to the signal theoretical value calculation unit 572 and the position calculation unit 573. In this embodiment, the candidate point group information a11 is information representing the position of each candidate point. The signal theoretical value calculation unit 572 calculates signal theoretical value information (signal theoretical value group information a12) based on the candidate point cloud information a11, and provides the obtained signal theoretical value group information a12 to the position calculation unit 573.

[0176] The measurement sensor unit 521 provides the signal separation unit 574 with information (measurement result information a13) of the measurement result from the measurement sensor 531. Here, in the present embodiment, the measurement result information a13 is information of a one-dimensional signal in time series. Based on the measurement result information a13, the signal separation unit 574 performs signal separation on the measured signal to obtain m separated signals, and provides the information (separation result information a15) of the m obtained separated signals to the position calculation unit 573. Here, in the present embodiment, the separation result information a15 is information of a multi-dimensional (in this embodiment, m-dimensional) signal in time series. Based on the candidate point group information a11, the signal theoretical value group information a12, and the separation result information a15, the position calculation unit 573 obtains the information (position information a14) of the position of the measurement sensor unit 521.

[0177] As another example, in the information processing device 511, a first A signal processing unit that performs a predetermined process on the measurement result information a13 output from the measurement sensor unit 521 may be provided. The predetermined process may be, for example, a process of reducing noise included in the measurement result. In the example of FIG. 12, the first A signal processing unit is provided between the measurement sensor unit 521 and the signal separation unit 574, performs a predetermined process on the measurement result information a13 output from the measurement sensor unit 521, and outputs the information of the result of the predetermined process to the signal separation unit 574. The signal separation unit 574 performs signal separation based on the measurement result information a13 after the predetermined process is performed. As another example, in the information processing device 511, a second B signal processing unit that performs a predetermined process on the separation result information a15 output from the signal separation unit 574 may be provided. The predetermined process may be, for example, a process of reducing noise included in the signal separation result. In the example of FIG. 12, the second B signal processing unit is provided between the signal separation unit 574 and the position calculation unit 573, performs a predetermined process on the separation result information a15 output from the signal separation unit 574, and outputs the information of the result of the predetermined process to the position calculation unit 573. The position calculation unit 573 calculates the position based on the separation result information a15 after the predetermined process is performed. Also, a configuration including both the first A signal processing unit and the second B signal processing unit may be used.

[0178] Here, in this embodiment, the m signal sources B1 to Bm, the m separated signals corresponding to the m signal sources B1 to Bm, one measurement sensor 531, the position of the one measurement sensor 531, the candidate point group, and the signal theoretical value group respectively correspond to the n measurement sensors A1 to An, the signals of the n measurement results by the n measurement sensors A1 to An, one signal source 31, the position of the one signal source 31, the candidate point group, and the signal theoretical value group in the first embodiment. Therefore, in this embodiment, the first-stage search in the two-stage search can be performed according to the same theory as the theory described by referring to FIGS. 7A to 7E according to the first embodiment. For example, as the algorithm of the two-stage search process executed by the position calculation unit 573, an algorithm equivalent to or similar to the algorithm of the two-stage search process executed by the position calculation unit 173 according to the first embodiment can be used.

[0179] [Supplementary Explanation of Calculation of Similarity and Judgment of Boundary Wall] Similar to the case of the first embodiment, for simplicity of explanation, the field measured by the measurement sensor 531 is scalar (the variations of the activity are only position and magnitude), and the signal theoretical value space is described by referring to a three-dimensional schematic diagram. Supplementary explanation will be given for the generalized case. That is, actually, the measurement sensor 531 may have a direction parameter, and the signal theoretical value space has, for example, the same number of dimensions (in this embodiment, m dimensions) as the number of signal sources B1 to Bm.

[0180] Here, a case where even if the position of the object to be tracked (in this embodiment, the measurement sensor 531) is the same, the object can measure different pattern signals will be described. For example, when the measurement sensor 531 is a magnetic sensor and measures a magnetic field, even if the position of the measurement sensor 531 is determined, the pattern of the signal measured by the measurement sensor 531 may not be uniquely determined depending on the direction of the measurement sensor 531. For example, if the measurement sensor 531 is a coil, even if the position of the measurement sensor 531 is determined, the pattern of the signal measured by the measurement sensor 531 (the signal vector captured by the coil) may change depending on the orientation of the coil (for example, the orientation of the normal to the coil). The same thing can happen even if the measurement sensor 531 is something other than a coil.

[0181] In such cases, methods for calculating similarity and methods for determining boundary walls can be similar to, for example, theoretical methods similar to those described in the first embodiment.

[0182] (Regarding the third embodiment) As described above, in the information processing system 501 according to this embodiment, for example, similar to the first embodiment, it is possible to suppress the final position estimation result from deviating from the correct value in multi-stage searches such as two-stage searches.

[0183] In this embodiment, we have shown a case where multiple signal sources B1 to Bm each generate signals of different frequencies, and the signal separation unit 574 separates the measured signal into multiple signals (separated signals) according to frequency. However, the method for separating the signals is not necessarily limited to a frequency-based method, and various methods may be used. As another example, a method may be used in which multiple signal sources B1 to Bm each generate signals corresponding to different codes, and the signal separation unit 574 separates the measured signal into multiple signals (separated signals) by separating it according to the corresponding code. Specifically, a method may be used in which multiple signal sources B1 to Bm each generate signals modulated with different codes, and the signal separation unit 574 separates the measured signal into multiple signals (separated signals) by demodulating it according to the corresponding code. For example, a code composed of pseudorandom numbers may be used as such a code.

[0184] (Fourth Embodiment) A fourth embodiment will be described.

[0185] [Information Processing Systems] In this embodiment, the configuration shown in Figures 10 to 12 of the third embodiment is generally the same as in the third embodiment, and for convenience of explanation, the reference numerals shown in Figures 10 to 12 will be used for explanation.

[0186] In this embodiment, instead of the configuration and operation described with reference to Figures 7A to 7E of the first embodiment applicable to the third embodiment, the configuration and operation described with reference to Figures 9A to 9B of the second embodiment are adopted. In other words, in this embodiment, the configuration and operation described with reference to Figures 9A to 9B of the second embodiment are applied to the configuration shown in Figures 10 to 12 of the third embodiment. Therefore, in this embodiment, the second stage of the two-stage search can be performed using a theory similar to the theory described with reference to Figures 9A to 9B in the second embodiment. For example, as the algorithm for the two-stage search process executed by the position calculation unit 573, it is possible to use an algorithm equivalent to or similar to the algorithm for the two-stage search process executed by the position calculation unit 173 in the second embodiment.

[0187] (Regarding the fourth embodiment) As described above, in the information processing system 501 according to this embodiment, for example, similar to the case of the second embodiment, it is possible to suppress the final position estimation result from deviating from the correct value in multi-stage searches such as two-stage searches.

[0188] (Combination of the third and fourth embodiments) For example, as a two-stage search process, an information processing device 511 may be implemented that performs the first-stage search process according to the third embodiment and the second-stage search process according to the fourth embodiment.

[0189] With such a configuration, in the information processing system 501, it is possible to suppress the final position estimation result from deviating from the correct value in multi-stage searches such as two-stage searches. In this example, measures are taken to suppress the final position estimation result from deviating from the correct value in both the rough search and the detailed search.

[0190] (Alternative Example) A case where there are a plurality of measurement sensors as in the first embodiment and the second embodiment, and there are a plurality of signal sources as in the third embodiment and the fourth embodiment will be described. Here, it is assumed that even if the signals generated from each of the plurality of signal sources are mixed in the measurement result signal, the signals can be separated from the measurement result signal respectively by, for example, frequency or code. For such a case, <First Alternative Example> and <Second Alternative Example> are shown.

[0191] <First Alternative Example> An information processing apparatus including a signal separation unit is configured as in the third embodiment and the fourth embodiment. For the signal of the measurement result by each measurement sensor, the signal separation unit separates the signal of the measurement result into a plurality of signals (signals for each signal source). In this case, when focusing on one separated signal corresponding to a specific single signal source and a plurality of measurement sensors, the same processing as in the first embodiment and the second embodiment can be applied.

[0192] <Second Alternative Example> An information processing apparatus including a signal separation unit is configured as in the third embodiment and the fourth embodiment. In this case, when focusing on a plurality of signal sources and a specific single measurement sensor, the same processing as in the third embodiment and the fourth embodiment can be applied.

[0193] (Regarding the above embodiments) Configuration examples according to the above embodiments are shown. As an example configuration, the information processing device has the following configuration. The information processing device comprises a candidate point setting unit, a signal theoretical value calculation unit, and a position calculation unit. The candidate point setting unit sets a group of candidate points that includes multiple candidate points. The signal theoretical value calculation unit calculates a signal theoretical value for at least each candidate point's location that corresponds to the signal measured by the measurement sensor unit when the object, which is either a measurement sensor unit or a signal generating unit, is present at that location. The position calculation unit performs a first determination process, based on the measured value, candidate point group, and signal theoretical value corresponding to the result of the measurement sensor unit measuring the signal generated from the signal generation unit, in which it determines a first candidate point corresponding to the signal theoretical value corresponding to the measured value from among a plurality of first candidate points included in the candidate point group as the first point of interest, and a second determination process, in which it determines a second candidate point corresponding to the signal theoretical value corresponding to the measured value from among a plurality of second candidate points included in the candidate point group that correspond to the first point of interest. Therefore, in multi-stage searches such as two-stage searches, the information processing device can suppress the possibility that the final position estimation result deviates from the correct value.

[0194] Here, in the examples of the first and second embodiments, the information processing device 11, candidate point setting unit 171, signal theoretical value calculation unit 172, position calculation unit 173, measurement sensor unit 21, and signal generation unit 22 are examples of an information processing device, an example of a candidate point setting unit, an example of a signal theoretical value calculation unit, an example of a position calculation unit, an example of a measurement sensor unit, and an example of a signal generation unit, respectively. Furthermore, in the examples of the third and fourth embodiments, the information processing device 511, candidate point setting unit 571, signal theoretical value calculation unit 572, position calculation unit 573, measurement sensor unit 521, and signal generation unit 522 are examples of an information processing device, an example of a candidate point setting unit, an example of a signal theoretical value calculation unit, an example of a position calculation unit, an example of a measurement sensor unit, and an example of a signal generation unit, respectively.

[0195] The multiple first candidate points in the first decision process are candidate points (primary candidate points in the embodiment) that are included in the search range in the first decision process. The primary point of focus in the first decision process is the candidate points determined by the first decision process. The multiple second candidate points in the second decision process are candidate points (primary candidate points and secondary candidate points in the embodiment) that are included in the search range in the second decision process. The second point of focus in the second decision process is the candidate points determined by the second decision process.

[0196] The first decision process and the second decision process are two consecutive search processes in a multi-stage search. For example, in a two-stage search, the first decision process and the second decision process are the first-stage search process and the second-stage search process, respectively. Furthermore, in multi-stage searches with three or more stages, the first decision process and the second decision process may be two consecutive search processes for any given stage.

[0197] As an example configuration, the information processing device has the following configuration. In the second decision process, the position calculation unit uses a search range that overlaps with the search range corresponding to the first candidate point adjacent to the first point of interest, as a search range that includes multiple second candidate points corresponding to the first point of interest. Therefore, in multi-stage searches such as two-stage searches, the information processing device can suppress the final position estimation result from deviating from the correct value, even if, for example, the first point of interest determined by the first decision process is not valid. Such configurations correspond, for example, to the second and fourth embodiments.

[0198] As an example configuration, the information processing device has the following configuration. In the second decision process, the position calculation unit uses a search range that includes multiple second candidate points corresponding to the first point of interest, and includes the second candidate point that is located immediately before the distance to the next adjacent first point of interest, beyond the first candidate point adjacent to the first point of interest. Therefore, in multi-stage searches such as two-stage searches, information processing devices can prevent the final position estimation result from deviating from the correct value by using a wider search range in the second decision process, even if, for example, the first point of interest determined in the first decision process is not valid. Such configurations correspond, for example, to the second and fourth embodiments.

[0199] As an example configuration, the information processing device has the following configuration. In the first determination process, the position calculation unit determines the first candidate point as the first point of interest, which corresponds to the signal theoretical value with the highest similarity to the measured value. Therefore, the information processing device can determine a reasonable first point of interest based on the similarity between the measured value and the theoretical signal value. Such configurations correspond, for example, to the second and fourth embodiments.

[0200] As an example configuration, the information processing device has the following configuration. In the first determination process, the position calculation unit sets candidate point A (the name "candidate point A" is used for explanatory purposes only) as the first point of interest if the measured value is included in the region enclosed by multiple boundary walls (also called the boundary wall region for explanatory purposes) based on the theoretical signal values ​​of multiple boundary points set between the first candidate point A and other adjacent first candidate points. Therefore, in multi-stage searches such as two-stage searches, the information processing device can ensure the validity of the first point of interest determined by the first decision process, for example, thereby suppressing the final position estimation result from deviating from the correct value. Such configurations correspond, for example, to the first and third embodiments.

[0201] As an example configuration, the information processing device has the following configuration. In the first determination process, the position calculation unit determines the positional relationship of the measured value relative to the boundary wall in the signal theoretical value space (signal theoretical value space) based on the signal theoretical value of the boundary point and the signal theoretical value of the auxiliary point set in the vicinity of the boundary point, and determines whether or not the measured value is included in the boundary wall region based on the result of this determination. Therefore, the information processing device can determine a valid first point of interest based on the determination of the positional relationship of the measured value with respect to the boundary wall. Such configurations correspond, for example, to the first and third embodiments.

[0202] As an example configuration, the information processing device has the following configuration. The position calculation unit performs a determination process to select the α-point of interest from among multiple α-point candidates included in the candidate point group, where α=1 (α is a parameter for explanatory purposes) up to a value of 3 or more specified by the user, and which corresponds to the theoretical signal value corresponding to the measured value. If α is 2 or greater, the α candidate point is a candidate point corresponding to the (α-1) point of interest. Therefore, in multi-stage searches involving three or more stages, the information processing device can suppress the occurrence of the final position estimation result deviating from the correct value.

[0203] As an example configuration, the information processing device has the following configuration. In the second determination process, the position calculation unit determines a second candidate point as the second point of interest that corresponds to the signal theoretical value with the highest similarity to the measured value. Therefore, the information processing device can determine a reasonable second point of interest based on the similarity between the measured value and the theoretical signal value. Such configurations correspond, for example, to the first to fourth embodiments.

[0204] As an example configuration, the information processing device has the following configuration. The similarity calculation is based on the relationship between the measured value and the subspace formed by the theoretical signal value corresponding to the second candidate point. Therefore, an information processing device can determine a suitable second point of interest based on this similarity. Such configurations correspond, for example, to the first to fourth embodiments.

[0205] As an example configuration, the information processing device has the following configuration. The measurement sensor unit has multiple measurement sensors. The measurement sensor unit outputs multidimensional time-series signal values ​​as measured values, representing a fraction of the measurement sensor's time. The object in question is the signal source of the signal generating unit. Therefore, the information processing device can estimate (determine) the location of the signal source, which is the object of the investigation, and can, for example, track the signal source. Such configurations correspond, for example, to the first and second embodiments.

[0206] In the example shown in Figure 1, the multiple measurement sensors A1 to An and the single signal source 31 are examples of multiple measurement sensors and an example of a signal source, respectively.

[0207] As an example configuration, the information processing device has the following configuration. The information processing device further includes a signal separation unit. The signal generation unit has multiple signal sources. The measurement sensor unit has one measurement sensor that measures signals generated from multiple signal sources. The measurement sensor unit outputs a one-dimensional time-series signal value as the measured value. The signal separation unit performs signal separation on the signal values, outputting multidimensional time-series signal values ​​corresponding to each signal source. The object in question is a measuring sensor. Therefore, the information processing device can estimate (determine) the position of the measurement sensor, which is the object of the investigation, and can, for example, track the measurement sensor. Such configurations correspond, for example, to the third and fourth embodiments.

[0208] In the example shown in Figure 10, one measuring sensor 531 and multiple signal sources B1 to Bm are examples of a measuring sensor and multiple signal sources, respectively. Furthermore, in the example shown in Figure 11, the signal separation unit 574 is an example of a signal separation unit.

[0209] As an example configuration, the information processing device has the following configuration. The measuring sensor is a magnetic sensor. The signal source is a coil. Therefore, in an information processing device, when the measurement sensor is a magnetic sensor and the signal source is a coil, it is possible to suppress the final position estimation result from deviating from the correct value in multi-stage searches such as two-stage searches.

[0210] As an example configuration, the information processing device has the following configuration. The measuring sensor is a microphone. The signal source is the sound source. Therefore, in information processing devices, when the measurement sensor is a microphone and the signal source is a sound source, it is possible to suppress the final position estimation result from deviating from the correct value in multi-stage searches such as two-stage searches.

[0211] As an example configuration, the information processing device has the following configuration. The measuring sensor is a radio wave receiver. The signal source is a radio wave source. Therefore, in information processing devices, when the measurement sensor is a radio wave receiver and the signal source is a radio wave source, it is possible to suppress the final position estimation result from deviating from the correct value in multi-stage searches such as two-stage searches.

[0212] As an example configuration, information processing systems corresponding to each of the first to fourth embodiments can be provided. For example, an information processing system 1 as shown in Figure 1 of the first and second embodiments may be provided. For example, an information processing system 501 such as the one shown in Figure 10 according to the third and fourth embodiments may be provided.

[0213] As an example configuration, we can provide information processing methods corresponding to each of the first to fourth embodiments. For example, an information processing method performed in an information processing system 1 as shown in Figure 1 of the first and second embodiments may be provided. For example, an information processing method performed in an information processing system 501 as shown in Figure 10 of the third and fourth embodiments may be provided.

[0214] Furthermore, a program to realize the function of any component in any of the devices described above may be recorded on a computer-readable recording medium, and that program may be loaded into a computer system and executed. Here, "computer system" includes hardware such as operating systems and peripheral devices. "Computer-readable recording medium" refers to portable media such as flexible disks, magneto-optical disks, ROMs, CD (Compact Disc)-ROMs (Read Only Memory), and storage devices such as hard disks built into computer systems. Moreover, "computer-readable recording medium" also includes volatile memory within a computer system that retains a program for a certain period of time, such as volatile memory within a computer system that acts as a server or client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line. Such volatile memory may be, for example, RAM (Random Access Memory). The recording medium may be, for example, a non-temporary recording medium.

[0215] Furthermore, the above program may be transmitted from a computer system that stores the program in a memory device or the like to another computer system via a transmission medium or by transmission waves within the transmission medium. Here, the "transmission medium" used to transmit the program refers to a medium that has the function of transmitting information, such as a network like the Internet or a communication line like a telephone line. Furthermore, the above program may be intended to implement only a portion of the functions described above. Moreover, the above program may be a so-called differential file, capable of implementing the aforementioned functions in combination with programs already recorded in the computer system. A differential file may also be called a differential program.

[0216] Furthermore, the functions of any component in any device described above may be implemented by a processor. For example, each process in the embodiment may be implemented by a processor that operates based on information such as a program, and a computer-readable recording medium that stores information such as a program. Here, the processor may be implemented by having the functions of each part implemented by separate hardware, or by having the functions of each part implemented by integrated hardware. For example, the processor includes hardware, and the hardware may include at least one of a circuit that processes digital signals and a circuit that processes analog signals. For example, the processor may be configured using one or more circuit devices or one or both of one or more circuit elements mounted on a circuit board. An IC (Integrated Circuit) may be used as the circuit device, and a resistor or capacitor may be used as the circuit element.

[0217] Here, the processor may be, for example, a CPU. However, the processor is not limited to a CPU; various types of processors may be used, such as a GPU (Graphics Processing Unit) or a DSP (Digital Signal Processor). The processor may also be, for example, a hardware circuit using an ASIC (Application Specific Integrated Circuit). Furthermore, the processor may be composed of, for example, multiple CPUs, or multiple hardware circuits using ASICs. The processor may also be composed of, for example, a combination of multiple CPUs and multiple hardware circuits using ASICs. The processor may also include, for example, one or more amplifier circuits or filter circuits that process analog signals.

[0218] While embodiments of this disclosure have been described in detail above with reference to the drawings, the specific configuration is not limited to these embodiments and may include designs and other elements that do not depart from the gist of this disclosure.

[0219] [Note] Examples of configurations (1) to (17) are shown.

[0220] (Configuration Example 1) A candidate point setting unit sets a group of candidate points that include multiple candidate points, A signal theoretical value calculation unit calculates a signal theoretical value corresponding to the signal measured by the measurement sensor unit when an object which is a measurement sensor unit or a signal generating unit is present at the position of at least each of the candidate points, A position calculation unit performs the following: a first determination process, based on the measured value corresponding to the result of the measurement sensor unit measuring the signal generated from the signal generation unit, the candidate point group, and the signal theoretical value, to determine a first candidate point corresponding to the signal theoretical value corresponding to the measured value from among a plurality of first candidate points included in the candidate point group as a first point of interest; and a second determination process, to determine a second candidate point corresponding to the signal theoretical value corresponding to the measured value from among a plurality of second candidate points included in the candidate point group and corresponding to the first point of interest as a second point of interest; An information processing device equipped with the following features.

[0221] (Configuration example 2) In the second determination process, the position calculation unit uses a search range that includes a plurality of second candidate points corresponding to the first point of interest, which overlaps with the search range corresponding to the first candidate point adjacent to the first point of interest. The information processing device described in (Configuration Example 1).

[0222] (Configuration Example 3) In the second determination process, the position calculation unit uses a search range that includes a plurality of second candidate points corresponding to the first point of interest, and includes a second candidate point that is located immediately before the distance to the next adjacent first point of interest beyond the first candidate point adjacent to the first point of interest. The information processing device described in (Configuration Example 2).

[0223] (Configuration example 4) In the first determination process, the position calculation unit determines the first candidate point corresponding to the signal theoretical value with the highest similarity to the measured value as the first point of interest. An information processing device as described in (Configuration Example 2) or (Configuration Example 3).

[0224] (Configuration example 5) In the first determination process, the position calculation unit sets candidate point A as the first point of interest if the measured value is included in the boundary wall region enclosed by a plurality of boundary walls based on the signal theoretical values ​​of a plurality of boundary points set between candidate point A, which is the first candidate point, and other adjacent first candidate points. An information processing device as described in any one of (Configuration Example 1) to (Configuration Example 3).

[0225] (Configuration example 6) In the first determination process, the position calculation unit determines the positional relationship of the measured value relative to the boundary wall in the space of the theoretical signal value, based on the theoretical signal value of the boundary point and the theoretical signal value of an auxiliary point set near the boundary point, and determines whether the measured value is included in the boundary wall region based on the result of the determination. The information processing device described in (Configuration Example 5).

[0226] (Configuration example 7) The position calculation unit performs a determination process to select the α candidate point corresponding to the signal theoretical value according to the measured value from among a plurality of α candidate points included in the candidate point group, from α=1 to a value of 3 or more specified by the user, as the α point of interest. If α is 2 or greater, the aforementioned candidate point α is a candidate point corresponding to the (α-1) point of interest. An information processing device as described in any one of (Configuration Example 1) to (Configuration Example 6).

[0227] (Configuration example 8) In the second determination process, the position calculation unit determines the second candidate point corresponding to the signal theoretical value with the highest similarity to the measured value as the second point of interest. An information processing device as described in any one of (Configuration Example 1) to (Configuration Example 7).

[0228] (Configuration example 9) The calculation of the similarity is based on the relationship between the measured value and the signal theoretical value corresponding to the second candidate point in the subspace. The information processing device described in (Configuration Example 8).

[0229] (Configuration example 10) The measurement sensor unit has a plurality of measurement sensors, The measurement sensor unit outputs a multidimensional time-series signal value that is a fraction of the measurement sensor's values ​​as the measured value. The object is a signal source of the signal generating unit. An information processing device as described in any one of (Configuration Example 1) to (Configuration Example 9).

[0230] (Configuration Example 11) Furthermore, it is equipped with a signal separation unit, The signal generation unit has a plurality of signal sources, The measurement sensor unit has one measurement sensor that measures signals generated from a plurality of signal sources. The measurement sensor unit outputs a one-dimensional time-series signal value as the measured value. The signal separation unit performs signal separation on the signal values ​​and outputs multidimensional time-series signal values ​​corresponding to each signal source. The object is the measurement sensor. An information processing device as described in any one of (Configuration Example 1) to (Configuration Example 9).

[0231] (Configuration Example 12) The aforementioned measuring sensor is a magnetic sensor, The signal source is a coil. An information processing device as described in (Configuration Example 10) or (Configuration Example 11).

[0232] (Configuration Example 13) The aforementioned measuring sensor is a microphone, The aforementioned signal source is a sound source. An information processing device as described in (Configuration Example 10) or (Configuration Example 11).

[0233] (Configuration Example 14) The aforementioned measuring sensor is a radio wave receiver, The aforementioned signal source is a radio wave source. An information processing device as described in (Configuration Example 10) or (Configuration Example 11).

[0234] (Configuration example 15) An information processing system comprising an information processing device and a measurement sensor unit, The measurement sensor unit has a plurality of measurement sensors, The measurement sensor unit outputs a multidimensional time-series signal value that is a fraction of the measurement sensor's values ​​as the measured value. The aforementioned information processing device is A candidate point setting unit sets a group of candidate points that include multiple candidate points, A signal theoretical value calculation unit calculates a signal theoretical value corresponding to the signal measured by the measurement sensor unit when an object that is a signal source of the signal generation unit is present at the position of at least each of the candidate points, A position calculation unit performs the following: a first determination process which determines a first candidate point corresponding to the theoretical signal value corresponding to the measured value from among a plurality of first candidate points included in the candidate point group as a first point of interest, based on the measured value, candidate point group, and theoretical signal value corresponding to the result of the measurement sensor unit measuring the signal generated from the signal generation unit; and a second determination process which determines a second candidate point corresponding to the theoretical signal value corresponding to the measured value from among a plurality of second candidate points included in the candidate point group and corresponding to the first point of interest as a second point of interest; Equipped with, Information processing system.

[0235] (Configuration Example 16) An information processing system comprising an information processing device and a measurement sensor unit, The aforementioned measurement sensor unit has one measurement sensor that measures signals generated from multiple signal sources of the signal generation unit. The aforementioned measurement sensor unit outputs a one-dimensional time-series signal value as the measured value. The aforementioned information processing device is A candidate point setting unit sets a group of candidate points that include multiple candidate points, A signal theoretical value calculation unit calculates a signal theoretical value corresponding to the signal measured by the measurement sensor unit when the object, which is the measurement sensor unit, is present at the position of at least each of the candidate points, A signal separation unit outputs multidimensional time-series signal values ​​corresponding to each signal source by performing signal separation on the aforementioned signal values. A position calculation unit performs the following: a first determination process which determines a first candidate point corresponding to the theoretical signal value corresponding to the measured value from among a plurality of first candidate points included in the candidate point group as a first point of interest, based on the measured value, candidate point group, and theoretical signal value corresponding to the result of the measurement sensor unit measuring the signal generated from the signal generation unit; and a second determination process which determines a second candidate point corresponding to the theoretical signal value corresponding to the measured value from among a plurality of second candidate points included in the candidate point group and corresponding to the first point of interest as a second point of interest; Equipped with, Information processing system.

[0236] (Configuration Example 17) The candidate point setting unit sets a group of candidate points that includes multiple candidate points. The signal theoretical value calculation unit calculates a signal theoretical value for at least each of the candidate point positions that corresponds to the signal measured by the measurement sensor unit when the object, which is a measurement sensor unit or a signal generating unit, is present at the position, The position calculation unit performs a first determination process in which, based on the measured value corresponding to the result of the measurement sensor unit measuring the signal generated from the signal generation unit, the candidate point group, and the signal theoretical value, the first candidate point corresponding to the signal theoretical value corresponding to the measured value is selected from among a plurality of first candidate points included in the candidate point group as the first point of interest, and the second determination process in which, from among a plurality of second candidate points included in the candidate point group and corresponding to the first point of interest, the second candidate point corresponding to the signal theoretical value corresponding to the measured value is selected as the second point of interest. Information processing methods. [Explanation of Symbols]

[0237] 1, 501…Information processing system, 11, 511…Information processing device, 21, 521…Measurement sensor unit, 31, B1~Bm…Signal source, 111…Input unit, 112…Output unit, 113…Storage unit, 114, 514…Control unit, 131…Acquisition unit, 141…Display unit, 151…Processing unit, 152…Display control unit, 171, 571…Candidate point setting unit, 172, 572…Signal theoretical value calculation unit, 173, 573…Position calculation unit, 311…Separated hyperplane, 531, A1~An…Measurement sensor, 574…Signal separation unit, a1, a11…Candidate point group information, a2, a12…Signal theoretical value group information a3, a13...Measurement result information, a4, a14...Position information, a15...Separation result information, C1, C2...Communication channel, c1, c2...Direction, D1~D3, D11...Measurement target point, d1~d3, d11...Measured value, E1...Candidate point group, F1~F4, F11~F16, F21~F24...Sub-candidate points, f1~f4, f11~f16, f21~f24, g1~g4...Theoretical signal value, G1~G4...Main candidate point, H1...Theoretical signal value group, I1, I2, i1, i2...Auxiliary points, J1, J2, j1, j2...Boundary points, K1, K2, k1, k2...Boundary normal, p1, p2, q1, q2...Axis

Claims

1. A candidate point setting unit sets a group of candidate points that include multiple candidate points, A signal theoretical value calculation unit calculates a signal theoretical value corresponding to the signal measured by the measurement sensor unit when an object which is a measurement sensor unit or a signal generating unit is present at the position of at least each of the candidate points, A position calculation unit performs the following: a first determination process which determines a first candidate point corresponding to the theoretical signal value corresponding to the measured value from among a plurality of first candidate points included in the candidate point group as a first point of interest, based on the measured value corresponding to the result of the measurement sensor unit measuring the signal generated from the signal generation unit, the candidate point group, and the theoretical signal value; and a second determination process which determines a second candidate point corresponding to the theoretical signal value corresponding to the measured value from among a plurality of second candidate points included in the candidate point group and corresponding to the first point of interest as a second point of interest; An information processing device equipped with the following features.

2. In the second determination process, the position calculation unit uses a search range that includes a plurality of second candidate points corresponding to the first point of interest, which overlaps with the search range corresponding to the first candidate point adjacent to the first point of interest. The information processing apparatus according to claim 1.

3. In the second determination process, the position calculation unit uses a search range that includes a plurality of second candidate points corresponding to the first point of interest, and includes a second candidate point that is located immediately before the distance from the first candidate point adjacent to the first point of interest to the next adjacent first point of interest. The information processing apparatus according to claim 2.

4. In the first determination process, the position calculation unit determines the first candidate point corresponding to the signal theoretical value with the highest similarity to the measured value as the first point of interest. The information processing apparatus according to claim 2.

5. In the first determination process, the position calculation unit sets candidate point A as the first point of interest if the measured value is included in the boundary wall region enclosed by a plurality of boundary walls based on the theoretical signal values ​​of a plurality of boundary points set between candidate point A, which is the first candidate point, and other adjacent first candidate points. The information processing apparatus according to claim 1.

6. In the first determination process, the position calculation unit determines the positional relationship of the measured value relative to the boundary wall in the space of the theoretical signal value, based on the theoretical signal value of the boundary point and the theoretical signal value of an auxiliary point set near the boundary point, and determines whether the measured value is included in the boundary wall region based on the result of the determination. The information processing apparatus according to claim 5.

7. The position calculation unit performs a determination process to select the α candidate point corresponding to the signal theoretical value according to the measured value from among a plurality of α candidate points included in the candidate point group, from α = 1 to a value of 3 or more specified by the user, as the α point of interest. If α is 2 or greater, the aforementioned candidate point α is a candidate point corresponding to the (α-1) point of interest. The information processing apparatus according to any one of claims 1 to 6.

8. In the second determination process, the position calculation unit determines the second candidate point corresponding to the signal theoretical value with the highest similarity to the measured value as the second point of interest. The information processing apparatus according to any one of claims 1 to 6.

9. The calculation of the similarity is based on the relationship between the measured value and the signal theoretical value corresponding to the second candidate point in the subspace. The information processing apparatus according to claim 8.

10. The measurement sensor unit has a plurality of measurement sensors, The measurement sensor unit outputs a multidimensional time-series signal value that is a fraction of the measurement sensor's values ​​as the measured value. The object is a signal source of the signal generating unit. The information processing apparatus according to claim 1.

11. Furthermore, it is equipped with a signal separation unit, The signal generation unit has a plurality of signal sources, The measurement sensor unit has one measurement sensor that measures signals generated from a plurality of signal sources. The measurement sensor unit outputs a one-dimensional time-series signal value as the measured value. The signal separation unit performs signal separation on the signal values ​​and outputs multidimensional time-series signal values ​​corresponding to each signal source. The object is the measurement sensor. The information processing apparatus according to claim 1.

12. The aforementioned measuring sensor is a magnetic sensor, The signal source is a coil. The information processing apparatus according to claim 10 or claim 11.

13. The aforementioned measuring sensor is a microphone, The aforementioned signal source is a sound source. The information processing apparatus according to claim 10 or claim 11.

14. The aforementioned measuring sensor is a radio wave receiver, The aforementioned signal source is a radio wave source. The information processing apparatus according to claim 10 or claim 11.

15. An information processing system comprising an information processing device and a measurement sensor unit, The measurement sensor unit has a plurality of measurement sensors, The measurement sensor unit outputs a multidimensional time-series signal value that is a fraction of the measurement sensor's values ​​as the measured value. The aforementioned information processing device is A candidate point setting unit sets a group of candidate points that include multiple candidate points, A signal theoretical value calculation unit calculates a signal theoretical value corresponding to the signal measured by the measurement sensor unit when an object that is a signal source of the signal generation unit is present at the position of at least each of the candidate points, A position calculation unit performs the following: a first determination process, based on the measured value, candidate point group, and signal theoretical value corresponding to the result of the measurement sensor unit measuring the signal generated from the signal generation unit, a first candidate point corresponding to the signal theoretical value corresponding to the measured value is selected from among a plurality of first candidate points included in the candidate point group as a first point of interest; and a second determination process, based on a plurality of second candidate points included in the candidate point group and corresponding to the first point of interest, a second candidate point corresponding to the signal theoretical value corresponding to the measured value is selected from among a plurality of second candidate points included in the candidate point group as a second point of interest; Equipped with, Information processing system.

16. An information processing system comprising an information processing device and a measurement sensor unit, The measurement sensor unit has one measurement sensor that measures signals generated from multiple signal sources of the signal generation unit. The aforementioned measurement sensor unit outputs a one-dimensional time-series signal value as the measured value. The aforementioned information processing device is A candidate point setting unit sets a group of candidate points that include multiple candidate points, A signal theoretical value calculation unit calculates a signal theoretical value corresponding to the signal measured by the measurement sensor unit when the object, which is the measurement sensor unit, is present at the position of at least each of the candidate points, A signal separation unit outputs multidimensional time-series signal values ​​corresponding to each signal source by performing signal separation on the aforementioned signal values. A position calculation unit performs the following: a first determination process, based on the measured value, candidate point group, and signal theoretical value corresponding to the result of the measurement sensor unit measuring the signal generated from the signal generation unit, a first candidate point corresponding to the signal theoretical value corresponding to the measured value is selected from among a plurality of first candidate points included in the candidate point group as a first point of interest; and a second determination process, based on a plurality of second candidate points included in the candidate point group and corresponding to the first point of interest, a second candidate point corresponding to the signal theoretical value corresponding to the measured value is selected from among a plurality of second candidate points included in the candidate point group as a second point of interest; Equipped with, Information processing system.

17. The candidate point setting unit sets a group of candidate points that includes multiple candidate points. The signal theoretical value calculation unit calculates a signal theoretical value for at least each of the candidate point positions that corresponds to the signal measured by the measurement sensor unit when the object, which is a measurement sensor unit or a signal generating unit, is present at the position, The position calculation unit performs a first determination process in which, based on the measured value corresponding to the result of the measurement sensor unit measuring the signal generated from the signal generation unit, the candidate point group, and the signal theoretical value, it determines a first candidate point corresponding to the signal theoretical value corresponding to the measured value from among a plurality of first candidate points included in the candidate point group as a first point of interest, and a second determination process in which it determines a second candidate point corresponding to the signal theoretical value corresponding to the measured value from among a plurality of second candidate points included in the candidate point group and corresponding to the first point of interest as a second point of interest. Information processing methods.

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