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

Abstract

An information processing device according to an embodiment comprises: 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, for at least the position of each candidate point, a signal theoretical value corresponding to a signal measured by a measurement sensor unit when an object, which is the measurement sensor unit or a signal generation unit, is present at the position; and a position calculation unit that executes, on the basis of the measurement value, the candidate point group, and the signal theoretical value corresponding to a result of measuring the signal generated from the signal generation unit by the measurement sensor unit, a first determination process for determining, as a first point of interest, a first candidate point corresponding to the signal theoretical value according to the measurement value from among a plurality of first candidate points included in the candidate point group, and a second determination process for determining, as a second point of interest, a second candidate point corresponding to the signal theoretical value according 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.

Description

Information Processing Apparatus, Information Processing System, and Information Processing Method

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

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

[0003] Non-Patent Document 1 describes a technique related to the distribution of electroencephalogram (EEG: Electroencephalography) and magnetoencephalogram (MEG: Magnetoencephalography) signals (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).

[0004] Japanese Patent Application Laid-Open No. 9-330403

[0005] Laurence Gavitt, et al., “A Multiresolution Framework to MEG / EEG Source Imaging”, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 48, NO. 10, OCTOBER 2001

[0006] However, in the conventional technology, when the result of position estimation by rough search is not appropriate in multi-stage search such as two-stage search, there are cases where countermeasures against the final position estimation result deviating from the correct value are insufficient.

[0007] [[ID=二十一]] The present disclosure has been made in consideration of such circumstances, and an object thereof is to provide an information processing apparatus, an information processing system, and an information processing method capable of suppressing the final position estimation result from deviating from the correct value in multi-stage search such as two-stage search.

[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 candidate point group including a plurality of candidate points, a signal theoretical value calculation unit that 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, and the signal separation of the signal value, thereby the signal source The information processing system comprises: a signal separation unit that outputs multidimensional time-series signal values ​​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 as a first point of interest, 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, from among a plurality of second candidate points included in the group of candidate points and corresponding to the first point of interest, the second candidate point corresponding to the signal theoretical value corresponding to the measurement value is selected as a second point of interest.

[0012] According to this disclosure, in information processing devices, information processing systems, and information processing methods, 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.

[0013] This figure shows an example configuration of an information processing system including an information processing device according to an embodiment (first embodiment, second embodiment). This figure shows an example configuration of an information processing device according to an embodiment. This figure shows an example configuration of a processing unit in an information processing device according to an embodiment (first embodiment, second embodiment). This figure shows an example of the general outline of processing in an information processing system according to an embodiment (first embodiment, second embodiment). This figure schematically shows an example of a candidate point group according to an embodiment. This figure schematically shows an example of a signal theoretical value group according to an embodiment. This figure shows an example of a boundary point set in the candidate point group in the first stage according to the first embodiment. This figure shows an example of a boundary point set in the signal theoretical value group in the first stage according to the first embodiment. This figure shows an example of an auxiliary point set in the candidate point group in the first stage according to the first embodiment. This figure shows an example of an auxiliary point set in the signal theoretical value group in the first stage according to the first embodiment. This figure shows an example of a separated hyperplane set in the signal theoretical value group in the first stage according to the first embodiment. This figure shows an example of the procedure for multi-stage search according to an embodiment. This figure shows an example of a search range set in the candidate point group in the second stage according to the second embodiment. This figure shows an example of a search range set in the signal theoretical value group in the second stage according to the second embodiment. This figure shows an example configuration of an information processing system including an information processing device according to the embodiments (third and fourth embodiments). This figure shows an example configuration of an information processing device according to the embodiments (third and fourth embodiments). This figure shows an example of the general processing in the information processing system according to the embodiments (third and fourth embodiments). This figure shows an example of the main candidate point and measurement target point in the first stage of a two-stage search. This figure shows an example of the signal theoretical value and measured value in the first stage of a two-stage search. This figure shows an example of candidate points (main candidate point and secondary candidate point) and measurement target point in the second stage of a two-stage search. This figure shows an example of the signal theoretical value and measured value in the second stage of a two-stage search. This figure shows an example of the main candidate point and measurement target point in the first stage of a two-stage search. This figure shows an example of the signal theoretical value and measured value in the first stage of a two-stage search. This figure shows an example of candidate points (main candidate point and secondary candidate point) and measurement target point in the second stage of a two-stage search.This figure shows an example of theoretical and measured signal values ​​in the second stage of a two-stage search. This figure shows an example of candidate points and measurement targets in a one-stage search. This figure shows an example of theoretical and measured signal values ​​in a one-stage search.

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

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

[0016] [Information Processing System] 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 one signal source 31 and a communication channel C1. Note that the information processing system 1 may be considered to include one or both of the signal source 31 and the communication channel C1.

[0017] <Signal Source of the Signal Generating Unit> For the sake of explanation, Figure 1 shows the signal generating unit 22. In this embodiment, the signal generating unit 22 has one signal source 31. In this embodiment, the signal generating unit 22 and the signal 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 mobile body 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 body such as a person.

[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 called, for example, the number of channels. Here, the number of the plurality of measurement sensors (n in this embodiment) and their arrangement are not limited to the example in Figure 1, and any configuration may be used. For example, the number of the plurality of measurement sensors may be two, or it may be three or more. Also, in the example in Figure 1, the plurality of measurement sensors A1 to An are arranged in an array, but the arrangement of the plurality of measurement sensors may be arbitrary. Note that the array configuration may be called, for example, a matrix configuration.

[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 capable of acquiring measurement results from each of the measurement sensors A1 to An. In the example in Figure 1, the details of the connection between the information processing device 11 and each of the measurement sensors A1 to An are omitted from the illustration. In the example in Figure 1, a single communication channel C1 is shown for simplification, but for example, different communication channels may be used for each of the measurement sensors 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 entities. 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, in the example in Figure 1, the case in which multiple measurement sensors A1 to An are located outside the information processing device 11 is shown. However, 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] Note that the term "measurement sensor" is for descriptive purposes only, and it may be called by other names, such as "signal sensor" or simply "sensor." Also, the multiple measurement sensors A1 to An may be called, for example, a measurement sensor group or a measurement sensor unit. In this embodiment, the multiple measurement sensors A1 to An are shown as a single unit (measurement sensor section 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 movement, but in other cases, they may be movable. If the 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 located 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 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 is uniquely determined when the position of the signal source 31 (the coil in this embodiment) 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, and radio waves generated from a radio wave source. Specifically, 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 is a diagram showing an example configuration of an information processing device 11 according to an embodiment. The information processing device 11 is configured using, for example, a computer. The information processing device 11 includes 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 includes a processing unit 151 and a display control unit 152. The processing unit 151 may be called, for example, an 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 the storage device. Furthermore, the input unit 111 may have, for example, an operation unit operated by a user, and may receive information corresponding to the operation performed by the user on the operation 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. Here, if the signal input by the input unit 111 is an analog signal, for example, the acquisition unit 131 may be equipped with an A / D (Analog to Digital) conversion function to convert the signal from an analog signal to a digital signal. Furthermore, if the information processing device 11 is applied to real-time processing, the acquisition unit 131 acquires the signal in real time. Note that even if the information processing device 11 is not applied to real-time processing, the acquisition unit 131 may acquire the signal in real time.

[0031] The output unit 112 outputs to an external source. 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 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 storage unit 113 has a storage device such as memory and stores data. The storage unit 113 stores data such as input signals and the processing results of those signals. The storage unit 113 also stores, for example, control programs. The data may also be called information.

[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 performs various processes and controls by executing a program (control program) stored in the storage unit 113.

[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 displays various information on 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 that are arranged 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 main candidate points and multiple secondary candidate points.

[0037] The signal theoretical value calculation unit 172 calculates a signal theoretical value that is estimated to be obtained when the signal source 31 is present at a given location (for convenience of explanation, also called a temporary location) for a plurality of different locations. In this embodiment, the signal theoretical value is the signal theoretical value obtained by estimating the value of the time-series multidimensional signal acquired from a plurality of 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 temporary location.

[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 result information from the signal theoretical value calculation unit 172, and the measurement result information from the 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 calculation may, for example, be a calculation that estimates the position.

[0039] <Outline of Processing in the Information Processing System> Figure 4 is a diagram showing an example of the outline of processing in the information processing system 1 according to the 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. Here, in this embodiment, the 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 group 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 signal theoretical values ​​corresponding to candidate points (main candidate points, secondary candidate points), and further calculate signal theoretical values ​​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 the candidate point group information a1, the signal theoretical value group information a2, and the measurement result information a3.

[0042] In addition, as another example, the information processing apparatus 11 may include a signal processing unit that performs a predetermined process on the measurement result information a3 output from the measurement sensor unit 21. The predetermined process may be, for example, a process of reducing noise included in the measurement result. In the example of FIG. 4, the signal processing unit is provided between the measurement sensor unit 21 and the position calculation unit 173, performs a predetermined process on the measurement result information a3 output from the measurement sensor unit 21, and outputs the information of the result of the predetermined process to the position calculation unit 173. The position calculation unit 173 calculates the position based on the measurement result information a3 after the predetermined process is performed.

[0043] <Candidate Point Group Setting>FIG. 5 is a diagram schematically showing an example of a candidate point group according to the embodiment. In FIG. 5, for convenience of explanation, an XYZ orthogonal coordinate system, which is a three-dimensional orthogonal coordinate system, is shown.

[0044] The candidate point setting unit 171 sets a candidate point group E1 as shown in FIG. 5. In the present embodiment, a candidate point group E1 including a plurality of candidate points arranged in each of the directions parallel to the X-axis, the Y-axis, and the Z-axis is set. The candidate point group E1 is set so as to include (that is, cover) a space portion that is a search range in the multi-stage search (for example, two-stage search) performed in the present embodiment.

[0045] In the example of FIG. 5, as an arrangement of some candidate points included in the candidate point group E1, an arrangement of three candidate points in the direction parallel to the X-axis, an arrangement of six candidate points in the direction parallel to the Y-axis, and an arrangement of three candidate points in the direction parallel to the Z-axis are shown. As described above, in the present embodiment, the space of the candidate point group is a three-dimensional space.

[0046] In the example of FIG. 5, for convenience of explanation, virtual lines connecting adjacent candidate points are shown, but such lines do not necessarily have to be set. For example, in various information processes, such lines may be set when a situation where such lines are used for operations or the like occurs. The same applies to the following similar drawings (that is, drawings showing a plurality of candidate points).

[0047] Here, in the present embodiment, the intervals at which a plurality of candidate points are arranged in the direction parallel to the X-axis, the intervals at which a plurality of candidate points are arranged in the direction parallel to the Y-axis, and the intervals at which a plurality of candidate points are arranged in the direction parallel to the Z-axis are all shown as the same intervals. However, in the example of FIG. 5, for the sake of clarity of the drawing, for the direction parallel to the Z-axis, the intervals between adjacent candidate points are shown enlarged so that the plurality of candidate points arranged in the direction parallel to the Z-axis do not overlap.

[0048] As another example, the intervals at which a plurality of candidate points are arranged in the direction parallel to the X-axis, the intervals at which a plurality of candidate points are arranged in the direction parallel to the Y-axis, and the intervals at which a plurality of candidate points are arranged in the direction parallel to the Z-axis may all be different, or may be different for only one axis. Also, for one axis (the X-axis, Y-axis, or Z-axis), the intervals at which a plurality of candidate points are arranged in the direction parallel to the axis are, in the present embodiment, at constant intervals, but as another example, may include different intervals.

[0049] In the example of FIG. 5, for the sake of convenience of explanation, only some of the candidate points in one XY plane are labeled and explained. Here, in the present embodiment, for the sake of convenience of explanation, a part of the candidate points is called the main candidate points, and the other part of the candidate points is called the sub-candidate points. Both the main candidate points and the sub-candidate points are candidate points. Note that the main candidate points and the sub-candidate points are names for the purpose of explanation, and may each be called by any other name, such as grid points, for example.

[0050] In the present embodiment, the main candidate points represent the candidate points used in the first stage of the two-stage search and may also be used in the second stage. Also, in the present embodiment, the sub-candidate points represent the candidate points not used in the first stage of the two-stage search and may be used in the second stage.

[0051] Figure 5 shows four primary candidate points G1 to G4 and thirteen secondary candidate points F1 to F4, F11 to F15, and F21 to F24, which are labeled candidate points. 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. 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. 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. Secondary candidate points F11, secondary candidate point F12, secondary candidate point F13, secondary candidate point F14, secondary candidate point F15, and secondary candidate point F16 are arranged in the order listed, 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 arranged in the order listed, from negative to positive along the Y-axis. In the example in Figure 5, although the signs are omitted, there are also multiple candidate points arranged in the direction parallel to the Z-axis.

[0052] <Calculation of the Signal Theoretical Value Group> 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 signal theoretical values ​​corresponding to each candidate point shown in Figure 5. Here, the signal theoretical value corresponding to a candidate point represents the theoretical value of the signal 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 signal theoretical 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 signal theoretical values ​​is an n-dimensional space, but it is shown schematically in the figure. As a specific example, the signal pattern of the signal theoretical 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. Here, in this embodiment, for the sake of explanation, the space of signal theoretical values ​​is called the signal theoretical value space, but it may be called by other names such as 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 labeled in Figure 5 are labeled. In Figure 6, the labeled theoretical signal values ​​are shown as four theoretical signal values ​​g1 to g4 corresponding to the four main candidate points G1 to G4, and 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.

[0054] In the example in Figure 6, for the sake of explanation, a hypothetical line is shown connecting adjacent theoretical signal values. 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 a schematic representation for explanatory purposes and is not necessarily strict. The same applies to similar diagrams that follow (i.e., diagrams showing multiple theoretical signal values).

[0055] [Explanation of Multi-Stage Search Techniques and Specific Examples of Potential Problems] Referring to Figures 13A to 13D and Figures 14A to 14D, we will explain multi-stage search techniques and provide specific examples of potential problems. In this example, we will use two-stage search as an example. In this explanation, we will also show one-stage search techniques with reference to Figures 15A to 15B. Below, we will explain multi-stage search techniques with reference to Figures 13A to 13D, and then provide specific examples of potential problems with reference to Figures 14A to 14D and Figures 15A to 15B.

[0056] For the sake of clarity, this example uses 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, to simplify the explanation, this example uses the case where the measurement target point lies on a two-dimensional plane (a plane parallel to the XY plane) formed by multiple candidate points. However, the same principles apply to cases where the measurement target point 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. Note that the term "measurement target point" is used for explanatory purposes only; it may be referred to by other names, such as "target point" or "object."

[0058] <Two-Stage Search Technique> 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, and calculating 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 a "spot point" for convenience of explanation) in the next stage of the search. The first-stage search range may, for example, include all pre-prepared primary candidate points, or it may be the entire space. The determination of candidate points (or the positions of candidate points) may also be called selection.

[0059] Next, in the two-stage search technique, as the second stage of the search, the measured value is compared 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 the similarity between the two is calculated. 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 the value, the more similar the signal patterns of the two are. The second-stage search range may be, for example, a range that includes candidate points predetermined for each primary candidate point that will be the target point, and as an example, 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 around the target point determined in the first stage of the search, and not only coarse primary candidate points but also finer secondary candidate points are used.

[0060] Here, for example, the second-stage search range may be set to a range within a predetermined distance from the target point. This predetermined distance may be, for example, ±1 [m], ±1 [cm], or ±1 [mm] in the directions parallel to the X-axis, Y-axis, and 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 the two-stage search technique> First, referring to Figures 13A to 13D, we will show examples of the two-stage search technique and examples of cases where no problems occur. In the first stage of the search, a rough search is performed using the main candidate points.

[0062] Figure 13A shows an example of the main candidate points G1 to G4 and the measurement target point D1 in the first stage of the two-stage search. Figure 13A shows an XYZ Cartesian coordinate system similar to that shown in Figure 5. Figure 13B shows an example of the signal theoretical values ​​g1 to g4 and the measured value d1 in the first stage of the two-stage search. Here, the measured value d1 represents the 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 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 target point is used as the reference point, and a more detailed search is performed using the primary candidate point 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 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 signal theoretical 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 signal theoretical value f12 is determined as the final estimated position. Here, the examples in Figures 13A to 13D show cases where the result of the final estimated position is reasonable.

[0066] <Examples of problems occurring with two-stage search techniques> Next, refer to Figures 14A to 14D to show examples of when problems occur. In the first stage of the search, a rough search is performed using the main candidate points.

[0067] Figure 14A shows an example of the main candidate points G1 to G4 and the measurement target point D11 in the first stage of the two-stage search. Figure 14A shows an XYZ Cartesian coordinate system similar to that shown in Figure 5. Figure 14B shows an example of the theoretical signal values ​​g1 to g4 and the measured value d11 in the first stage of the two-stage search. Here, the measured value d11 represents the 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 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 target point is used as the reference point, and a detailed search is performed using the primary candidate point 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 ranges predetermined for each candidate point (in this example, the primary candidate point) do 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 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 result of the final estimated position is not valid. Thus, as in the examples in Figures 14A to 14D, in a configuration where the theoretical signal value with the highest similarity to the measured value is searched in a two-stage search, there were cases where the most valid candidate point was not identified. The reason for this is that, as a result of the coarse search in the first stage, the original correct position (the true position corresponding to the measured value) may fall outside the search range of the second stage.

[0072] <Explanation of why no problems occur with the one-stage search technique> Referring to Figures 15A to 15B, it will be explained that even if the signal source 31 is located at the measurement target point D11, similar to the example in Figures 14A to 14D, no problems occur with the one-stage search technique. In the one-stage search, the position 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-stage search. Here, the measured value d11 and measurement target point D11 are the same as in the examples in Figures 14A-14D.

[0074] As shown in Figure 15B, in a one-stage search, the signal theoretical 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 signal theoretical value f14 is determined as the final estimated position. Thus, the result of the final estimated position in a one-stage search is reasonable. However, while the accuracy of position estimation is good in a one-stage search because it uses detailed candidate points from the beginning, it has the problem of being computationally intensive and taking a long time to process.

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

[0076] For the sake of clarity, this example uses 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. Also, for the sake of simplicity, this example explains the case where the measurement target point is located on a two-dimensional plane (a plane parallel to the XY plane) formed by multiple candidate points, but the same applies to cases where the measurement target point is located at an arbitrary position in three-dimensional space (an arbitrary 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 described. Figure 7A is a diagram showing 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 is a diagram showing an example of boundary points 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, two boundary points J1 to J2, and two lines (referred to as boundary normals K1 to K2 for convenience of explanation). Note that Figure 7A also shows a hypothetical line (parallel to the XY plane) perpendicular to the line connecting main candidate points G3 and G4 and passing through boundary point J1, and a hypothetical line (parallel to the XY plane) perpendicular to the line connecting main candidate points G2 and G3 and passing through boundary point J2. However, these lines do not necessarily need to be defined.

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

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

[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 main 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, for a total of six boundary points. This predetermined distance is, for example, half the distance between the main candidate point and other main 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-g4, the measured value d2, two boundary points j1-j2, two boundary normals k1-k2, axes p1 and q1 for boundary point j1, and axes p2 and q2 for boundary point j2 in relation to the state shown in Figure 7B. Also, Figure 7D shows two points related to boundary point j1 (referred to as auxiliary points i1-i2 for convenience of explanation), the origin O1 in the theoretical signal value space, the direction c1 from the origin O1 toward the measured value d2, and the direction c2 from the origin O1 toward 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 signal theoretical value space as examples, but the same applies to other boundary points. For boundary point J1 between main candidate point G3 and main candidate point G4, auxiliary point I1 is a point shifted by a predetermined value from boundary point J1 in the direction of main candidate point G4 from main candidate point G3, and auxiliary point I2 is a point shifted by the same predetermined value from boundary point J1 in the direction of main candidate point G3 from main candidate point G4. Here, the predetermined value may be a value smaller than, for example, the distance between boundary point J1 and main candidate point G3 (the distance between boundary point J1 and main candidate point G4 is the same).

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

[0087] Figure 7E shows an example of a separating 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, (Procedure Q1) to (Procedure Q4) are shown as an example of the procedure for the first stage of the search.

[0089] (Procedure Q1) From the set group of candidate points, select coarse candidate points (primary candidate points in this embodiment) to be used as the search range. For each of the set coarse candidate points, set boundary points between it and other adjacent coarse candidate points in the directions parallel to the X-axis, the Y-axis, and the 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, two boundary points, boundary points J1 and J2, are shown for main candidate point G3. Although not shown in Figure 7A, six boundary points are set for main candidate point G3. Similarly, six boundary points are set for each of the other main candidate points G1, G2, and G4.

[0091] (Procedure Q2) In the signal theory space, a predetermined wall (also called 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, a boundary wall is defined as a separating hyperplane that passes through the signal points generated by each point (boundary point in the 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 (six boundary walls in this example) surrounding any of the signal theory values ​​(signal theory values ​​corresponding to the main candidate points). At this time, for each boundary wall, it is determined which side of the boundary wall the measured value d2 lies on. Here, for each main candidate point, six boundary walls are generated in the signal theory value space from the six boundary points that exist three-dimensionally around the one main candidate point, and an inner region (subspace) enclosed by these six boundary walls is generated.

[0094] In the example shown 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 one signal theory value (the signal theory value corresponding to the main candidate point), the main candidate point corresponding to that signal theory value is determined as the target point.

[0096] In the examples of Figures 7A and 7B, it is determined that the measured value d2 lies inside all the boundary walls (six boundary walls in this example) surrounding the theoretical signal value g3, and the primary candidate point G3 corresponding to the theoretical signal value g3 is determined as the target point. Note that the fact that the measured value d2 lies inside all the boundary walls (six boundary walls in this example) surrounding the theoretical signal value g3 corresponds to the fact that 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 of ​​responsibility of 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., if 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 of similarity calculation and boundary wall determination] 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 explained the signal theoretical value space by referring to a three-dimensional schematic diagram. However, let me provide a supplementary explanation for the generalized case. In other words, in reality, the signal source 31 may have a directional parameter, and the signal theoretical value 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 where, even if the position of the object being tracked (in this embodiment, the signal source 31) is the same, the object may generate signals with different patterns. For example, when the signal source 31 is a coil such as a circle, 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, when 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] The magnitude of the current flowing through the coil can also be a parameter, but 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> The similarity score of a vector field source (when the signal source 31 has a direction parameter) will be explained. At the point of interest (point p, which is one candidate point), let lx be the signal (column vector) generated when the signal source 31 is active with direction (X, Y, Z) = (1, 0, 0), let ly be the signal (column vector) generated when the signal source 31 is active with direction (X, Y, Z) = (0, 1, 0), and let lz be the signal (column vector) generated when the signal source 31 is active with direction (x, y, z) = (0, 0, 1). Let Lp = [lx, ly, lz] be the matrix obtained 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 pseudo-inverse (sometimes called the pseudo-inverse) of the matrix Lp, so s = (pseudo-inverse of Lp)w. Note that the pseudo-inverse of matrix Lp may be calculated in advance, for example.

[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 pseudo-inverse matrix. In this embodiment, the evaluation is performed based on the similarity between "the signal closest to w that point p can create" 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 an angle may be used as the similarity score. 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 result of the orthogonal projection of (w / |w|), which is the result of normalizing the measured value w (= Lps / |w|), may be used as the similarity score. In this case, the larger the magnitude, the higher the similarity. As yet another example, the dot product of (w / |w|) and its orthogonal projection result (= Lps / |w|) may be used as the similarity score. In this case, the larger the dot 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 a difference as the similarity score may be done in any of the above examples. However, when using such a difference, normalization is required 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 pseudo-inverse matrix of matrix Lp is such that the number of rows (vertical) is 3, and the number of columns (horizontal) is the same 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 as the number of measurement sensors A1 to An (n).

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

[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, when the normal of the boundary wall is parallel to the X-axis, we set auxiliary points 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 the boundary point.

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

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

[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. Note that the pseudoinverse of matrix Ab may be calculated in advance, for example.

[0114] Then, 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 Ab is such that the number of rows (vertical) is 3, 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 as the number of measurement sensors A1 to An (n). The measured value w is a vector with the same number of dimensions as the number of measurement sensors A1 to An (n).

[0116] [Example of Multi-Stage Processing Procedure] Figure 8 is a diagram showing an example of the multi-stage search processing procedure according to the embodiment. It shows an example of the multi-stage search processing procedure performed by the information processing device 11. In this example, the two-stage search process and the three-stage or higher search process will be explained 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. Note that the setting of the candidate point group and the calculation of the theoretical signal value group 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 the candidate point group and the calculation of the theoretical signal value group may be performed at any time to update the previously set candidate point group and theoretical signal value group.

[0118] The following describes the processes (Step S1) to (Step S4) performed in the information processing device 11. (Step S1) The position calculation unit 173 performs the first stage of a multi-stage search based on the measurement value acquired by the measurement sensor unit 21, the information of the pre-set candidate point group, and the information of the pre-calculated signal theoretical value, thereby estimating the rough position of the measurement value. Then, the position calculation unit 173 proceeds to the process 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 first 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 to terminate 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 not to terminate the multi-stage search (i.e., to continue) (Step S3: NO), it proceeds to the process in 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 ends.

[0122] Here, 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 before proceeding to the process in step S4. In a two-stage search, in the process of step S1 (the first stage of the search), one main candidate point is determined among the main candidate points by a determination based on the positional relationship between the boundary wall in the signal theoretical value space and the measured value. Then, in the process of step S2 (the second stage of the search), using the search range based on the main candidate point determined in the first stage of the search, one candidate point is determined among the candidate points (main candidate point and secondary candidate point) that corresponds to the 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 at this time, 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. Here, we have shown a case where boundary wall determination is performed in all stages except the final stage in a multi-stage search with three or more stages, but as another example, the configuration may be such that boundary wall determination is performed in some stages (at least one stage) other than the final stage, and similarity determination is performed in the other stages in the same way as in the final stage.

[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, when a search is performed in three or more stages, for example, the search may be 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 may be performed using the most candidate points (for example, all candidate points). In general terms, in a multi-stage search, a wide range (coarse range) may be used as the search range in a search with a small number of stages, and a narrower range (more detailed 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 such predetermined conditions, for example, the similarity between the measured value and the determined theoretical signal value may be greater than or equal to a predetermined value, or the similarity in the current step may have improved by a predetermined value or more compared to the similarity in the previous step.

[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 improves the accuracy of the estimated position (e.g., resolution accuracy) with a computation cost equivalent to or similar to that of conventional systems. In this embodiment, during the coarse estimation, instead of using only information on sparse candidate points (primary candidate points in this embodiment), information on neighbors is also reflected, making it possible to determine the correct target point. This makes it possible to suppress (ideally eliminate) the determination of a target point different from the correct value in, for example, the coarse estimation stage (e.g., the first stage) of the multi-stage search.

[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, the cause of failure in the final estimation can be eliminated. As a result, for example, it is possible to improve the accuracy and resolution of position estimation 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 position in real time, and it is possible to speed up the calculation for position tracking while improving the resolution accuracy of the tracking. In this embodiment, when tracking a position in real time, for example, it is possible to obtain a result in which the estimated position follows close to the correct position over time.

[0133] Let me explain the background of this disclosure. For example, in order to perform real-time position tracking with high position resolution and low delay, the position calculation algorithm is important. Normally, obtaining high position resolution requires a large amount of memory and long computation time. For example, if the degree of matching with the sensor measurement value is determined for each candidate point and the candidate point with the best degree of matching is used as the estimated position, then increasing the position resolution in three dimensions (space) by a factor of 10 requires a factor of 1000 (for example, the number of candidate points becomes 1000 times larger).

[0134] Therefore, as a method to improve positional resolution while keeping computational complexity low, a two-stage search (two-stage estimation method) has been proposed, in which a rough estimate is made to determine a potential location, and then a finer estimate is made around that location. Specifically, in a two-stage search, the position is estimated using candidate points at 1 cm intervals, and then the position is estimated again using candidate points at 1 mm intervals within a 1 cm square area around 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 potential location determined in the first stage may deviate from a reasonable candidate point, 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 take measures to ensure that the location of the correct answer is included in the second stage of the two-stage search. This embodiment provides an example of such measures.

[0136] <Regarding Non-Patent Document 1> Non-Patent Document 1 describes an estimation of the distributional signal source of electroencephalograms and electromagnetic fields, where the region is narrowed while increasing the resolution within the 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 (one candidate point in this embodiment) is performed. In this embodiment, the calculation can be simplified, and a significant speed increase can be achieved by not including complex processes such as ellipse determination in Non-Patent Document 1. Also, in Non-Patent Document 1, the region that becomes the solution is not necessarily narrowed down to a single point, but in this embodiment, the estimated position is fixed to a single point. Furthermore, in Non-Patent Document 1, there is no countermeasure for when the correct answer point falls 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 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 aims to avoid missing search points by listing multiple candidate points in coarse searches and to improve search accuracy by utilizing a parametric space in fine searches. Here, Patent Document 1 uses image matching. On the other hand, this embodiment uses signal pattern matching (in this embodiment, theoretical signal values), and is also applicable when the signal from which the search points may arise has degrees of freedom (for example, when the signal is variable). In addition, in this embodiment, the correct answer point is ultimately obtained when performing coarse and fine searches, and speed and stability (reduction of errors) are achieved in single-point searches.

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

[0139] [Information Processing System] In this embodiment, the configuration shown in Figures 1 to 6 of 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 explanation. In this embodiment, instead of the configuration and operation described with reference to Figures 7A to 7E of 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 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) in the two-stage search 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 clarity, this example uses 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. Also, for the sake of simplicity, this example explains the case where the measurement target point is located on a two-dimensional plane (a plane parallel to the XY plane) formed by multiple candidate points, but the same applies when the measurement target point is located at an arbitrary position in three-dimensional space (an arbitrary position in the XYZ Cartesian coordinate system).

[0142] Referring to Figures 9A and 9B, the processing of the second stage of the two-stage search according to the second embodiment will be explained. In this example, it is assumed that the main candidate point G2 (theoretical signal value g2) was determined as the target point in the first stage of the search. Figure 9A is a diagram showing 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 is a diagram showing an example of the search range set for the theoretical signal value group in the second stage according to the second embodiment.

[0143] Figure 9A shows the measurement target point D3 and candidate points (main candidate point G2, secondary candidate points F11-F15, F21-F24) that constitute the search range when the main candidate point G2 is the target point. Figure 9B shows the measured value d3 and the theoretical signal values ​​g2, f11-f15, 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-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 determined 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 determined for each candidate point (in this example, the primary candidate point) may be a range such that the range from that candidate point to 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 when one of two adjacent candidate points (primary candidate points in this example) becomes the target point overlaps with the search range when the other candidate point becomes the target point. This is because the search range in the fine search is used after the target point has been determined from the coarse candidate points (primary candidate points in this example) in the coarse search. For example, in the coarse search, even if either of the two adjacent coarse candidate points (primary candidate points in this example) is determined to be the target point, all of the fine candidate points (secondary candidate points in this example) that exist between these two coarse candidate points are included in the search range, thereby suppressing (ideally eliminating) the possibility of missing the correct point.

[0147] Here, when setting the search range in a fine-grained search, it may be considered that, for example, the wider the search range in a fine-grained search, the less likely it is that the correct answer point will be missed, but the computational cost tends to increase. As an example, the search range in a fine-grained search with one candidate point (in this example, the main candidate point) as the target point may not be limited to a range of less than half the distance to other adjacent candidate points (in this example, the main candidate points), but rather to a range of a distance slightly shorter than the distance to those other candidate points (in this example, the main candidate points). In other words, the search range in a fine-grained search with 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 fine-grained 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 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 before proceeding to the process in step S4. In a two-stage search, in the process of step S1 (the first stage of the search), one candidate point is determined that corresponds to the single theoretical signal value with the highest similarity to the measured value among the candidate points (primary candidate points). Then, 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 that corresponds to the single theoretical signal value with the highest similarity to the measured value among the candidate points (primary candidate point and secondary candidate points).

[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 at this time, 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 described 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 described 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 final position estimation result from 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 of estimation: a coarse estimation and a fine estimation (or three or more stages), thereby improving the accuracy of the estimated position (e.g., resolution accuracy) with a computation cost equivalent to or similar to that of conventional systems. In this embodiment, by using a wider search range during the fine estimation, it is possible to include the correct point in the search range even if a different target point 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 a different target point is determined in the coarse estimation stage of the multi-stage search (e.g., the first stage).

[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. This makes it possible to improve the accuracy and resolution of position estimation 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 position in real time, and can improve the resolution accuracy of the tracking while speeding up the calculations for tracking the position. In this embodiment, when tracking a position in real time, for example, it is possible to obtain a result in which the estimated position follows a path close to the correct position 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 measures.

[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 suppress the occurrence of the final position estimation result deviating from the correct value in both coarse and fine searches.

[0158] (Third Embodiment) The third embodiment will now be described.

[0159] [Information Processing System] Figure 10 is a diagram showing an example configuration of an information processing system 501 including an information processing device 511 according to the embodiment. The information processing system 501 comprises an information processing device 511 and a measurement sensor unit 521. Figure 10 also shows a plurality of m (where m is an integer of 2 or more) signal sources B1 to Bm and a communication channel C2. In this embodiment, the number of signal sources B1 to Bm (m) may be called, for example, the number of channels. Note that the information processing system 501 may be considered to comprise either or both of the signal sources B1 to Bm and the communication channel C2.

[0160] <Signal Generators of the Signal Generating Unit> For the sake of explanation, Figure 10 shows the signal generating unit 522. In this embodiment, the signal generating unit 522 includes m signal generators B1 to Bm. Note that the m signal generators B1 to Bm do not necessarily have to be a single unit, and may be dispersed.

[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 of signal sources (m in this embodiment) and their arrangement 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 three or more. Also, in the example in Figure 10, the signal sources B1 to Bm are arranged in an array, but the arrangement of the signal sources may be arbitrary. Note that the array arrangement may be called, for example, a matrix arrangement.

[0162] In this embodiment, the multiple signal sources B1 to Bm are located in fixed positions without movement, but in other cases, they may be movable. If the 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 located 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 one 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 mobile body 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 path C2. The information processing device 511 is capable of acquiring measurement results from the measurement sensor 531. In this embodiment, the information processing device 511 acquires measurement results from the measurement sensor 531 by communicating with the measurement sensor 531. However, in other examples, the measurement results from the measurement sensor 531 may be stored in a portable storage medium, and then the measurement results may be 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 case where the information processing device 511 and the measurement sensor unit 521 are separate entities is shown, but 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, in the example of Figure 10, the case where the measurement sensor unit 521 is outside the information processing device 511 is shown, 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 measuring sensor 531 measures the signals generated from each of the signal sources B1 to Bm. In this embodiment, the measuring 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 is a diagram showing an example configuration of the information processing device 511 according to this embodiment. Here, the example configuration of the information processing device 511 according to this embodiment is generally the same as the example configuration 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 shown. In this embodiment, for the sake of convenience of explanation, components that are the same as those shown in Figure 2 will be denoted by the same reference numerals as in Figure 2 and described accordingly.

[0169] The information processing device 511 includes 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 includes 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 (measured value signal) into m signals (also called separated signals for convenience of explanation) based on the measurement result information 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 has the frequency of 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 (separated signals) with different frequencies. 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 that are arranged 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 signal theoretical value that is estimated to be obtained when the measurement sensor 531 is present at multiple different locations (for convenience of explanation, also called temporary locations). In this embodiment, the signal theoretical value is the signal theoretical value obtained by estimating the values ​​of multiple separated signals (time-series multidimensional signals) obtained by the signal separation unit 574, and is a set of time-series measurement result values ​​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 temporary location.

[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 calculation result information from 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 calculation may be, for example, an estimation calculation.

[0175] <Outline of Processing in the Information Processing System> Figure 12 is a diagram showing an example of the outline of processing in the information processing system 501 according to the 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. Here, 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 group 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 on the measurement result from the measurement sensor 531 (measurement result information a13). In this embodiment, the measurement result information a13 is time-series one-dimensional signal information. 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 on the obtained m separated signals (separation result information a15) to the position calculation unit 573. In this embodiment, the separation result information a15 is time-series multi-dimensional (m-dimensional in this embodiment) signal information. Based on the candidate point cloud information a11, the signal theoretical value cloud information a12, and the separation result information a15, the position calculation unit 573 obtains position information (position information a14) for the measurement sensor unit 521.

[0177] As another example, the information processing device 511 may be provided with a first signal processing unit that performs predetermined processing on the measurement result information a13 output from the measurement sensor unit 521. This predetermined processing may, for example, be a process to reduce noise included in the measurement result. In the example of Figure 12, the first signal processing unit is provided between the measurement sensor unit 521 and the signal separation unit 574, performs predetermined processing on the measurement result information a13 output from the measurement sensor unit 521, and outputs the information of the result of the predetermined processing to the signal separation unit 574. The signal separation unit 574 separates the signals based on the measurement result information a13 after the predetermined processing has been performed. As yet another example, the information processing device 511 may be provided with a second signal processing unit that performs predetermined processing on the separation result information a15 output from the signal separation unit 574. This predetermined processing may, for example, be a process to reduce noise included in the signal separation result. In the example shown in Figure 12, the B signal processing unit is located between the signal separation unit 574 and the position calculation unit 573. The B unit performs predetermined processing on the separation result information a15 output from the signal separation unit 574 and outputs the result of this predetermined processing to the position calculation unit 573. The position calculation unit 573 calculates the position based on the separation result information a15 after the predetermined processing has been performed. Alternatively, a configuration comprising both the A signal processing unit and the 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, the one measurement sensor 531, the position of the one measurement sensor 531, the candidate point group, and the signal theoretical value group correspond, respectively, to the n measurement sensors A1 to An, the n measurement result signals from the n measurement sensors A1 to An, the 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 of the two-stage search can be performed using a theory similar to the theory described with reference to Figures 7A to 7E in the first 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 first embodiment.

[0179] [Supplementary explanation of similarity calculation and boundary wall determination] Similar to the first embodiment, to simplify the explanation, the field measured by the measurement sensor 531 is a scalar (the only variations in activity are position and magnitude), and the signal theoretical value space is explained with reference to a three-dimensional schematic diagram, but a supplementary explanation will be given for the generalized case. In other words, in reality, the measurement sensor 531 may have a direction parameter, and the signal theoretical value space has the same number of dimensions as the number of signal sources B1 to Bm (in this embodiment, m dimensions).

[0180] Here, we will explain a case where, even if the position of the object being tracked (in this embodiment, the measurement sensor 531) is the same, the object may measure signals with different patterns. 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 orientation 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, a case is shown 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 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. As a specific example, 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. As such codes, for example, codes composed of pseudorandom numbers may be used.

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

[0185] [Information Processing System] In this embodiment, the configuration shown in Figures 10 to 12 according to the third embodiment is generally the same as in the third embodiment, and for the sake of 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 applied 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, in the configuration shown in Figures 10 to 12 of the third embodiment, the configuration and operation described with reference to Figures 9A to 9B of the second embodiment are applied. 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 of 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 of 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 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 this configuration, the information processing system 501 can 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 coarse and fine searches.

[0190] (Modifications) The following describes the case where there are multiple measuring sensors as in the first and second embodiments, and multiple signal sources as in the third and fourth embodiments. Here, it is assumed that even if the signals generated from each of the multiple signal sources are mixed in the measurement result signal, it is possible to separate them from the measurement result signal by, for example, frequency or code. The first modification and the second modification are shown for such cases.

[0191] <First Modification> An information processing device equipped with a signal separation unit is configured as in the third and fourth embodiments. The signal separation unit separates the signal of the measurement result from each measurement sensor into multiple signals (signals for each signal source). In this case, focusing on one separated signal corresponding to a specific signal source and multiple measurement sensors, the same processing as in the first and second embodiments can be applied.

[0192] <Second Modification> An information processing device equipped with a signal separation unit is configured as in the third and fourth embodiments. In this case, if we focus on multiple signal sources and a specific single measurement sensor, the same processing as in the third and fourth embodiments can be applied.

[0193] (Regarding the above embodiments) An example of the configuration according to the above embodiments is shown below. As an example of a 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 including a plurality of candidate points. The signal theoretical value calculation unit calculates a signal theoretical value for at least each candidate point's position 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 that position. The position calculation unit 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 group of candidate points as a first point of interest, based on the measurement value, the group of candidate points, and the signal theoretical value corresponding to the measurement value measured by the measurement sensor unit 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 group of candidate points and corresponding to the first point of interest as a second point of interest. Therefore, in information processing devices, it is possible to suppress the occurrence of the final position estimation result deviating from the correct value in multi-stage searches such as two-stage searches.

[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, respectively, an example 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. 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, respectively, an example 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.

[0195] The multiple first candidate points in the first decision process are candidate points included in the search range of the first decision process (primary candidate points in the embodiment). The first point of interest in the first decision process is a candidate point determined by the first decision process. The multiple second candidate points in the second decision process are candidate points included in the search range of the second decision process (primary candidate points and secondary candidate points in the embodiment). The second point of interest in the second decision process is a candidate point 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 and second stages of the search, respectively. In a multi-stage search with three or more stages, the first and second decision processes may be two consecutive search processes at any stage.

[0197] As an example configuration, the information processing device has the following configuration. In the second determination 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 a plurality of 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 determination process is not valid. Such a configuration corresponds to, for example, the second and fourth embodiments.

[0198] As an example configuration, the information processing device has the following configuration. 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. Therefore, in a multi-stage search such as a two-stage search, even if the first point of interest determined in the first determination process is not valid, the information processing device can use a wider search range in the second determination process to prevent the final position estimation result from deviating from the correct value. Such a configuration corresponds to, for example, 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 a first candidate point as the first 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 suitable first point of interest based on the similarity between the measured value and the signal theoretical value. Such a configuration corresponds, 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 a name used for convenience of explanation) 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 convenience of explanation) based on the signal theoretical 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 determination process, for example, and suppress the final position estimation result from deviating from the correct value. Such a configuration corresponds to, for example, 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 an 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 suitable first point of interest based on the determination result of the positional relationship of the measured value relative to the boundary wall. Such a configuration corresponds to, for example, the first and third embodiments.

[0202] As an example configuration, the information processing device has the following configuration. The position calculation unit performs a decision process to determine the α-point of interest from among multiple α-point candidates included in the candidate point group, from α = 1 (α is a parameter for the sake of explanation) up to a value of 3 or more specified by the user. When α is 2 or more, the α-point candidate is the candidate point corresponding to the (α-1) point of interest. Therefore, the information processing device can suppress the final position estimation result from deviating from the correct value in multi-stage searches of three or more stages.

[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 suitable second point of interest based on the similarity between the measured value and the signal theoretical value. Such a configuration corresponds, for example, to the first to fourth embodiments.

[0204] As an example configuration, the information processing device has the following configuration. The similarity is calculated based on the relationship between the measured value and the signal theoretical value corresponding to the second candidate point in the subspace. Therefore, the information processing device can determine a suitable second point of interest based on this similarity. This configuration corresponds, 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 a number of times equal to the number of measurement sensors. The object is a signal source possessed by the signal generation unit. Therefore, the information processing device can estimate (determine) the position of the signal source, which is the object, and can, for example, track the signal source. Such a configuration corresponds to, for example, 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 a plurality of signal generation sources. The measurement sensor unit has one measurement sensor that measures signals generated from the plurality of signal generation sources. The measurement sensor unit outputs a one-dimensional time-series signal value as the measured value. The signal separation unit outputs a multi-dimensional time-series signal value corresponding to each signal generation source by performing signal separation on the signal value. The object is the measurement sensor. Therefore, the information processing device can estimate (determine) the position of the measurement sensor, which is the object, and can, for example, track the measurement sensor. Such a configuration corresponds to, for example, 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. 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 the information processing device, when the measuring 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 measurement sensor is a microphone. The signal source is a sound source. Therefore, in the information processing device, 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 measurement sensor is a radio wave receiver. The signal source is a radio wave source. Therefore, in the information processing device, 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 according to the first and second embodiments may be provided. For example, an information processing system 501 as shown in Figure 10 according to the third and fourth embodiments may be provided.

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

[0214] Furthermore, a program to realize the function of any component in any device 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, CDs (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 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, which holds the program for a certain period of time. 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 this 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" for transmitting 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. Also, the above program may be for the purpose of realizing a part of the functions described above. Furthermore, the above program may be one that can realize the functions described above in combination with a program already recorded in the computer system, a so-called differential file. 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 implementing the functions of each part in separate hardware, or by implementing the functions of each part in 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, and 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 of hardware circuits using multiple ASICs. The processor may also be composed of, for example, a combination of multiple CPUs and hardware circuits using multiple ASICs. Furthermore, the processor may 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) An information processing device comprising: a candidate point setting unit that sets a group of candidate points 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 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 group of candidate points 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 group of candidate points, 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 group of candidate points and corresponding to the first point of interest as a second point of interest.

[0221] (Configuration Example 2) The information processing apparatus according to (Configuration Example 1), wherein the position calculation unit, in the second determination process, 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 a plurality of second candidate points corresponding to the first point of interest.

[0222] (Configuration Example 3) The information processing apparatus according to (Configuration Example 2), wherein the position calculation unit, in the second determination process, uses a 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.

[0223] (Configuration Example 4) The information processing apparatus according to (Configuration Example 2) or (Configuration Example 3), wherein 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 in the first determination process.

[0224] (Configuration Example 5) The information processing apparatus according to any one of (Configuration Example 1) to (Configuration Example 3), wherein 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 surrounded 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 first candidate points adjacent thereto.

[0225] (Configuration Example 6) The position calculation unit, in the first determination process, determines the positional relationship in which the measured value exists with respect 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 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 the determination, as described in (Configuration Example 5).

[0226] (Configuration Example 7) The position calculation unit performs a determination process to determine the α candidate point corresponding to the signal theoretical value corresponding 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, and when α is 2 or more, the α candidate point is a candidate point corresponding to the (α-1) point of interest, as described in any one of (Configuration Example 1) to (Configuration Example 6).

[0227] (Configuration Example 8) The information processing apparatus according to any one of (Configuration Example 1) to (Configuration Example 7), wherein 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 in the second determination process.

[0228] (Configuration Example 9) The information processing device described in (Configuration Example 8), wherein 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.

[0229] (Configuration Example 10) The information processing device according to any one of (Configuration Example 1) to (Configuration Example 9), wherein 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 number of measurement sensors as the measured value, and the object is a signal source that the signal generation unit has.

[0230] (Configuration Example 11) The information processing device according to any one of (Configuration Example 1) to (Configuration Example 9), further comprising a signal separation unit, wherein the signal generation unit has a plurality of signal generation sources, the measurement sensor unit has one measurement sensor that measures signals generated from the plurality of signal generation sources, the measurement sensor unit outputs a one-dimensional time-series signal value as the measured value, the signal separation unit outputs a multi-dimensional time-series signal value corresponding to each of the signal generation sources by performing signal separation on the signal value, and the object is the measurement sensor.

[0231] (Configuration Example 12) The information processing device according to (Configuration Example 10) or (Configuration Example 11), wherein the measuring sensor is a magnetic sensor and the signal source is a coil.

[0232] (Configuration Example 13) The information processing device according to (Configuration Example 10) or (Configuration Example 11), wherein the measurement sensor is a microphone and the signal source is a sound source.

[0233] (Configuration Example 14) The information processing device according to (Configuration Example 10) or (Configuration Example 11), wherein the measurement sensor is a radio wave receiver and the signal source is a radio wave source.

[0234] (Configuration Example 15) An information processing system comprising 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 that is a fraction of the number of measurement sensors as a measured value, and the information processing device includes a candidate point setting unit that sets a group of candidate points including a plurality of candidate points, and a signal theoretical value calculation unit that calculates a theoretical signal 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, An information processing system comprising: a position calculation unit that performs a first determination process to determine 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 to determine 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.

[0235] (Configuration Example 16) An information processing system comprising 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 of a signal generation unit, the measurement sensor unit outputs a one-dimensional time-series signal value as a measured value, the information processing device comprises: 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 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, and a signal separation unit that outputs a multi-dimensional time-series signal value corresponding to each of the signal sources by performing signal separation on the signal value, An information processing system comprising: a position calculation unit that performs a first determination process to determine 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 to determine 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.

[0236] (Configuration Example 17) An information processing method comprising: a candidate point setting unit setting a group of candidate points including a plurality of candidate points; a signal theoretical value calculation unit calculating 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 performing 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 group of candidate points as a first point of interest, 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; 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 group of candidate points and corresponding to the first point of interest as a second point of interest.

[0237] 1, 501... Information processing system, 11, 511... Information processing device, 21, 521... Measurement sensor unit, 31, B1 to 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... Separation hyperplane, 531, A1 to 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 point, K1, K2, k1, k2...Boundary normal, p1, p2, q1, q2...Axis

Claims

1. An information processing device comprising: a candidate point setting unit that sets a group of candidate points 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 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 group of candidate points 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 group of candidate points, 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 group of candidate points and corresponding to the first point of interest as a second point of interest.

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

3. The information processing apparatus according to claim 2, wherein in the second determination process, the position calculation unit uses a range as 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.

4. The information processing apparatus according to claim 2, wherein 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.

5. The information processing apparatus according to claim 1, wherein, 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 a boundary wall region surrounded 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 a first candidate point, and other first candidate points adjacent thereto.

6. The information processing apparatus according to claim 5, wherein the position calculation unit, in the first determination process, determines the positional relationship in which the measured value exists with respect 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 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 the determination.

7. The position calculation unit performs a determination process to determine the α candidate point corresponding to the signal theoretical value corresponding 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, and when α is 2 or more, the α candidate point is a candidate point corresponding to the (α-1) point of interest, as described in any one of claims 1 to 6.

8. The information processing apparatus according to any one of claims 1 to 6, wherein 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 in the second determination process.

9. The information processing apparatus according to claim 8, wherein 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.

10. The information processing apparatus according to claim 1, wherein 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 number of measurement sensors as the measured value, and the object is a signal source that the signal generation unit has.

11. The information processing apparatus according to claim 1, further comprising a signal separation unit, wherein the signal generation unit has a plurality of signal sources, the measurement sensor unit has one measurement sensor that measures signals generated from the plurality of signal sources, the measurement sensor unit outputs a one-dimensional time-series signal value as the measured value, the signal separation unit outputs a multi-dimensional time-series signal value corresponding to each of the signal sources by performing signal separation on the signal value, and the object is the measurement sensor.

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

13. The information processing apparatus according to claim 10 or claim 11, wherein the measurement sensor is a microphone and the signal source is a sound source.

14. The information processing apparatus according to claim 10 or claim 11, wherein the measurement sensor is a radio wave receiver and the signal source is a radio wave source.

15. An information processing system comprising 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 that is a fraction of the number of measurement sensors as a measured value, and the information processing device includes a candidate point setting unit that sets a group of candidate points including a plurality of candidate points, and a signal theoretical value calculation unit that 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, An information processing system comprising: a position calculation unit that performs a first determination process to determine 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 to determine 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.

16. An information processing system comprising 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 of a signal generation unit, the measurement sensor unit outputs a one-dimensional time-series signal value as a measured value, the information processing device comprises: 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 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 at least each of the positions of the candidate points, and a signal separation unit that outputs a multi-dimensional time-series signal value corresponding to each of the signal sources by performing signal separation on the signal value, An information processing system comprising: a position calculation unit that performs a first determination process to determine 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 to determine 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.

17. An information processing method comprising: a candidate point setting unit setting a group of candidate points including multiple candidate points; a signal theoretical value calculation unit calculating a signal theoretical value for at least each of the positions of the candidate points 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; and a position calculation unit performing 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 group of candidate points as a first point of interest, 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; 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 group of candidate points and corresponding to the first point of interest as a second point of interest.

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