Measuring device

Abstract

The invention provides a measuring device capable of improving the accuracy of removing noise from the measurement result of a required signal by using a plurality of reference sensors. The measurement device is provided with: a signal sensor unit having one or more signal sensors for measuring a measurement signal in which a target signal and noise are mixed; a reference sensor unit having two or more reference sensors for measuring a reference signal including the noise; and a processing unit including: a reference sensor data grouping unit that divides, into two groups, reference sensor data, which is data of two or more reference signals measured by the two or more reference sensors of the reference sensor unit; a primary signal processing unit that performs noise removal processing on the signal sensor data using the reference sensor data of the first group; and a secondary signal processing unit that uses a second group of reference sensor data to perform noise removal processing on primary signal-processed data obtained by performing noise removal processing on the signal sensor data by the primary signal processing unit.

Description

Technical Field

[0001] The present disclosure relates to a measuring device. Background Art

[0002] In order to obtain good analysis results in various measurements, it is necessary to remove various environmental noises superimposed on the required signal. Generally, as a method for removing environmental noise, a method can be used in which, in addition to a signal sensor for measuring the required signal, a reference sensor is additionally prepared, and the data obtained by the reference sensor is filtered in time series. In the case where there are many types of environmental noises, it is necessary to increase the reference sensors accordingly.

[0003] In the measuring device described in Patent Document 1, there are provided: a plurality of measuring sensor units that are provided at a measuring position for measuring a measurement object and detect an input magnetic field in at least one detection axis direction; a plurality of reference sensor units that are provided at a reference position spaced apart from the measuring position and detect an input magnetic field in three-axis directions; and a correction unit that uses a reference signal indicating the outputs of the plurality of reference sensor units to correct a measurement signal corresponding to the outputs of the plurality of measuring sensor units (see Patent Document 1). Prior Art Documents Patent Documents

[0004] Patent Document 1: Japanese Unexamined Patent Application Publication No. 2020-139840 Summary of the Invention Technical Problem to be Solved by the Invention

[0005] However, in the prior art, in the case where a plurality of reference sensors are used, sometimes the accuracy of removing noise from the measurement result of the required signal is not sufficient.

[0006] The present disclosure has been made in view of such a situation, and the technical problem thereof is to provide a measuring device capable of using a plurality of reference sensors to improve the accuracy of removing noise from the measurement result of the required signal. Technical Means for Solving the Problem

[0007] One approach provides a measurement device, comprising: a signal sensor unit having one or more signal sensors that measure a measurement signal in which a target signal and noise coexist; a reference sensor unit having Q or more reference sensors that measure a reference signal including the noise, where Q is an integer of 2 or more; and a processing unit, the processing unit including: a reference sensor data grouping unit that divides data of the Q or more reference signals measured by the Q or more reference sensors of the reference sensor unit, i.e., reference sensor data, into Q groups, i.e., a first group to a Q-th group; and k-th signal processing units for each k from 1 to Q and where Q is an integer from 1 to Q, the first signal processing unit performs: using the reference sensor data of the first group, performing noise removal processing on data of the measurement signal measured by the signal sensors, i.e., signal sensor data, or using the reference sensor data of the first group, performing noise removal processing on data of the measurement signal measured by the signal sensors, i.e., signal sensor data, and reference sensor data of one or more groups other than the first group, the u-th signal processing unit performs: using the reference sensor data of the u-th group or signal-processed reference sensor data obtained by performing noise removal processing on the reference sensor data of the u-th group by one or more of the first signal processing unit to the (u - 1)-th signal processing unit, performing noise removal processing on the (u - 1)-th signal-processed data obtained by performing noise removal processing on the signal sensor data by the (u - 1)-th signal processing unit, or using the reference sensor data of the u-th group or signal-processed reference sensor data obtained by performing noise removal processing on the reference sensor data of the u-th group by one or more of the first signal processing unit to the (u - 1)-th signal processing unit, performing noise removal processing on the (u - 1)-th signal-processed data obtained by performing noise removal processing on the signal sensor data by the (u - 1)-th signal processing unit, and reference sensor data of one or more groups other than the first group to the u-th group or signal-processed reference sensor data obtained by performing noise removal processing on the reference sensor data by one or more of the first signal processing unit to the (u - 1)-th signal processing unit, where u is an integer from 2 to (Q - 1), the Q-th signal processing unit performs: using the reference sensor data of the Q-th group or signal-processed reference sensor data obtained by performing noise removal processing on the reference sensor data of the Q-th group by one or more of the first signal processing unit to the (Q - 1)-th signal processing unit, performing noise removal processing on the (Q - 1)-th signal-processed data obtained by performing noise removal processing on the signal sensor data by the (Q - 1)-th signal processing unit. Advantages of the Invention

[0008] According to the present disclosure, in a measuring device, a plurality of reference sensors can be used to improve the accuracy of removing noise from the measurement results of a required signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 FIG. is a schematic diagram showing a general structure of a measuring device including an information processing device according to an embodiment. Figure 2 FIG. is a schematic diagram showing a general structure of the information processing device according to an embodiment. Figure 3 FIG. schematically shows a processing flow of a first structural example according to an embodiment. Figure 4 FIG. schematically shows a processing flow of a second structural example according to an embodiment. Figure 5 FIG. schematically shows a processing flow of a third structural example according to an embodiment. Figure 6 FIG. schematically shows a processing flow of a fourth structural example according to an embodiment. Figure 7 FIG. shows an example of the noise density after signal processing according to an embodiment. Figure 8 FIG. shows a structural example of a signal processing unit according to an embodiment. Figure 9 FIG. shows an example of interval division. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0010] Embodiments of the present disclosure will be described below with reference to the drawings.

[0011] [Measuring Device] Figure 1 FIG. is a schematic diagram showing a general structure of a measuring device 1 including an information processing device 21 according to an embodiment. In Figure 1 , for ease of explanation, an XYZ orthogonal coordinate system, which is a three-dimensional orthogonal coordinate system, is shown. The measuring device 1 includes a plurality of, i.e., L, signal sensors A1 to AL, a plurality of, i.e., M, reference sensors B1 to BM, and an information processing device 21. In Figure 1 's example, the area where the signal sensors A1 to AL are arranged is schematically shown as a signal sensor arrangement unit 11.

[0012] Here, in the present embodiment, as an example, the case where L = 16 and M = 20 will be described. In addition, the number of the signal sensors A1 to AL can be any number of 1 or more. In the present embodiment, the case where the number of the signal sensors A1 to AL is plural is shown, but the number of the signal sensors can also be one. In addition, the number of reference sensors B1 to BM can also be any number of 2 or more. In Figure 1 the measurement object 31 is also shown. In addition, the measuring device can also be referred to as a measurement system or the like. Also, measurement can also be referred to as determination or detection or the like.

[0013] In the case where there are a plurality of signal sensors A1 to AL, for example, a device (e.g., a signal sensor unit) that integrally includes these plurality of signal sensors A1 to AL can also be formed. In addition, in the case where there are a plurality of reference sensors B1 to BM, for example, a device (e.g., a reference sensor unit) that integrally includes these plurality of reference sensors B1 to BM can also be formed. In addition, a device (e.g., a sensor unit) that integrally includes both the signal sensors A1 to AL and the reference sensors B1 to BM can also be formed.

[0014] <Configuration of Signal Sensors> In Figure 1 the example of, the signal sensor configuration unit 11 is a square planar region parallel to the XY plane. In Figure 1 the example of, inside the signal sensor configuration unit 11, 16 signal sensors A1 to A16 are arranged at equal intervals in a direction parallel to one side of the square (a direction parallel to the X-axis) and at equal intervals in a direction perpendicular to this direction (a direction parallel to the Y-axis). Specifically, 4 signal sensors A1 to A4 are arranged at equal intervals in the direction parallel to the Y-axis, 4 signal sensors A5 to A8 are arranged at equal intervals in the direction parallel to the Y-axis, 4 signal sensors A9 to A12 are arranged at equal intervals in the direction parallel to the Y-axis, and 4 signal sensors A13 to A16 are arranged at equal intervals in the direction parallel to the Y-axis. In addition, 4 signal sensors A1, A5, A9, A13 are arranged at equal intervals in the direction parallel to the X-axis, 4 signal sensors A2, A6, A10, A14 are arranged at equal intervals in the direction parallel to the X-axis, 4 signal sensors A3, A7, A11, A15 are arranged at equal intervals in the direction parallel to the X-axis, and 4 signal sensors A4, A8, A12, A16 are arranged at equal intervals in the direction parallel to the X-axis. In Figure 1 the example of, the equal intervals in the direction parallel to the X-axis and the equal intervals in the direction parallel to the Y-axis are the same intervals. In Figure 1 the example of, the plurality of signal sensors A1 to AL are arranged in an array.

[0015] Here, as a method of configuring (overall configuration) the signal sensors A1 to AL, there is no particular limitation, and other methods can also be used. In addition, as the configuration (such as position and direction) of each signal sensor A1 to AL, there is no particular limitation, and various methods can be used. For example, in Figure 1 the example, the directions of the signal sensors A1 to AL are shown as the same direction, but the directions of the signal sensors A1 to AL can also be arbitrary. Furthermore, the name "signal sensor" is a name for explanation, and for example, it can also be called other names such as a measurement sensor or (simply called) a sensor.

[0016] <Configuration of the object to be measured> In Figure 1 the example, when projecting onto the XY plane while ignoring the position deviation parallel to the Z axis, the object to be measured 31 is arranged at the center (or near it) of the signal sensor configuration unit 11. Here, as a method of the configuration relationship between the object to be measured 31 and the signal sensors A1 to AL, there is no particular limitation, and other methods can also be used.

[0017] <Object to be measured> As the object to be measured 31, there is no particular limitation, and it can be various objects. As the object to be measured 31, as an example, it can be a coil that generates magnetic force. In this case, when measuring the magnetic force generated from this coil, the influence of surrounding noise can be suppressed by the information processing device 21. As other examples, the object to be measured 31 can also use a sound source that emits sound (a signal of sound).

[0018] <Target signal> Each of the signal sensors A1 to AL measures (detects) the required signal (also called the target signal for the sake of explanation) resulting from the object to be measured 31. At this time, noise is superimposed on this target signal. Therefore, each of the signal sensors A1 to AL measures (detects) the signal in which this target signal and this noise coexist (also called the mixed signal for the sake of explanation). Then, each of the signal sensors A1 to AL obtains its measurement result (also called the signal measurement result for the sake of explanation). As the target signal, a signal of any physical quantity can be used. For example, signals such as magnetic force, current, voltage, sound, and light can also be used. In addition, the target signal can also be called, for example, the signal of interest or the required signal, etc.

[0019] <Configuration of the reference sensor> In Figure 1In the example, the reference sensors B1 to B20 are arranged so as to surround the signal sensor configuration unit 11. Specifically, six reference sensors B1 to B6 are arranged at equal intervals in the direction parallel to the Y-axis on the negative side in the direction parallel to the X-axis with respect to the signal sensor configuration unit 11. In addition, six reference sensors B6 to B11 are arranged at equal intervals in the direction parallel to the X-axis on the positive side in the direction parallel to the Y-axis with respect to the signal sensor configuration unit 11. In addition, six reference sensors B11 to B16 are arranged at equal intervals in the direction parallel to the Y-axis on the positive side in the direction parallel to the X-axis with respect to the signal sensor configuration unit 11. In addition, six reference sensors B16 to B20 and B1 are arranged at equal intervals in the direction parallel to the X-axis on the negative side in the direction parallel to the Y-axis with respect to the signal sensor configuration unit 11. In Figure 1 the example, the equal intervals in the direction parallel to the X-axis and the equal intervals in the direction parallel to the Y-axis are the same intervals. In Figure 1 the example, a plurality of reference sensors B1 to BM are arranged around the signal sensors A1 to AL.

[0020] In Figure 1 the example, four reference sensors B2 to B5 parallel to the Y-axis are respectively arranged at the same positions on the Y-axis as the four signal sensors A1 to A4 parallel to the Y-axis. Similarly, four reference sensors B12 to B15 parallel to the Y-axis are respectively arranged at the same positions on the Y-axis as the four signal sensors A4, A3, A2, and A1 parallel to the Y-axis. In addition, in Figure 1 the example, four reference sensors B7 to B10 parallel to the X-axis are respectively arranged at the same positions on the X-axis as the four signal sensors A1, A5, A9, and A13 parallel to the X-axis. Similarly, four reference sensors B17 to B20 parallel to the X-axis are respectively arranged at the same positions on the X-axis as the four signal sensors A13, A9, A5, and A1 parallel to the X-axis.

[0021] Here, there is no particular limitation on the arrangement (overall arrangement) of the plurality of reference sensors B1 to BM, and other methods can also be used. In addition, there is no particular limitation on the arrangement (such as position and direction) of each reference sensor B1 to BM, and various methods can be used. For example, in Figure 1 the example, the directions of the respective reference sensors B1 to BM are shown as the same direction, but the directions of the respective reference sensors B1 to BM can also be arbitrary. In addition, the name "reference sensor" is a name for explanation purposes, and it can also be called other names such as a noise sensor or (simply) a sensor, etc.

[0022] <Reference signal (signal containing noise)> Each of the reference sensors B1 to BM measures (detects) the noise (signal of the noise) superimposed on the target signal as a reference signal. Thus, each of the reference sensors B1 to BM obtains its measurement result (also called a noise measurement result for the sake of explanation). In addition, in the present embodiment, the case where each of the reference sensors B1 to BM measures noise but does not measure the component of the target signal is described. However, as another example, a structure in which the component of the target signal is included to such an extent that there is no practical obstacle in the measurement results of the noise by each of the reference sensors B1 to BM can also be used.

[0023] Here, the noise is, for example, noise caused by a signal (interference signal) other than the target signal, and it can also be called environmental noise, etc. Environmental noise can, for example, include various noises emitted from multiple different sources. As a specific example, when the target signal is a magnetic signal, the noise can be a magnetic signal caused by an object other than the measurement object (such as a train, etc.). In addition, as a specific example, when the target signal is an electromagnetic wave, the noise can be an electromagnetic wave emitted from other circuits, etc., near the measurement object 31.

[0024] <Types of signal sensors and types of reference sensors> When using multiple signal sensors A1 to AL, for example, they can all be of the same type of sensor (sensors that measure the same physical quantity), or they can also include different types of sensors (sensors that measure different physical quantities). When using multiple reference sensors B1 to BM, for example, they can all be of the same type of sensor (sensors that measure the same physical quantity), or they can also include different types of sensors (sensors that measure different physical quantities).

[0025] In addition, as each of the reference sensors B1 to BM, for example, a sensor of the same type as any of the signal sensors A1 to AL can be used, or a sensor of a different type from the signal sensors A1 to AL can also be used. As a specific example, when using magnetic sensors as the signal sensors A1 to AL, magnetic sensors can be used as the reference sensors B1 to BM, or a combination of a magnetic sensor and an acceleration sensor can also be used as the reference sensors B1 to BM. In addition, for example, even sensors that measure the same physical quantity can be regarded as different types of sensors when the products are different and the rated values are different.

[0026] In addition, the orientations of the respective configurations of the signal sensors A1 to AL and the orientations of the respective configurations of the reference sensors B1 to BM are not particularly limited. For example, there may be a case where the orientations of one or more of the configurations of the signal sensors A1 to AL are the same as the orientations of one or more of the configurations of the reference sensors B1 to BM, or the orientations of the configurations of the signal sensors A1 to AL and the orientations of the configurations of the reference sensors B1 to BM may also be different.

[0027] <Connection between information processing device and each sensor> In Figure 1 example, each of the signal sensors A1 to AL and the information processing device 21 are communicably connected by wire or wirelessly. Moreover, the information processing device 21 can obtain the measurement results (detection results) of each of the signal sensors A1 to AL. In addition, each of the reference sensors B1 to BM and the information processing device 21 are communicably connected by wire or wirelessly. Moreover, the information processing device 21 can obtain the measurement results (detection results) of each of the reference sensors B1 to BM. In addition, in Figure 1 example, the details of the connection between the information processing device 21 and each sensor (signal sensors A1 to AL, reference sensors B1 to BM) are omitted from the drawing.

[0028] Here, in the present embodiment, a case where the information processing device 21 obtains the measurement results of each sensor by communicating with each sensor (signal sensors A1 to AL, reference sensors B1 to BM) is shown. However, as another example, a structure may also be used in which after the measurement results of each sensor are temporarily stored in a removable storage medium, the measurement results are output from the storage medium to the information processing device 21, and thus the information processing device 21 obtains the measurement results.

[0029] <Information processing device> Figure 2 is a diagram showing the schematic structure of the information processing device 21 of the embodiment. The information processing device 21 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 includes a reference sensor data grouping unit 161 and Q (Q is an integer of 2 or more) signal processing units, namely, a first signal processing unit P1 to a Q-th signal processing unit PQ. In addition, the processing unit 151 may also be referred to as an arithmetic unit or the like, for example.

[0030] The input unit 111 performs input from the outside. In the present embodiment, the input unit 111 inputs signals (measurement result signals) output from each sensor (signal sensors A1 to AL, reference sensors B1 to BM). As a specific example, the input unit 111 may input the signal by receiving the signal transmitted from each sensor (signal sensors A1 to AL, reference sensors B1 to BM), or may also input the signal stored in the movable storage device from the storage device. In addition, the input unit 111 may have an operation unit operated by a user, and may input information corresponding to the content of the operation performed by the user on the operation unit.

[0031] The acquisition unit 131 acquires the signal input by the input unit 111. The acquisition unit 131 may also store the acquired signal in the storage unit 113. Here, when the signal input by the input unit 111 is an analog signal, for example, the acquisition unit 131 may have an A / D (Analog to Digital) conversion function to convert the signal from an analog signal to a digital signal. In addition, when the information processing device 21 is applied to real-time processing, the acquisition unit 131 acquires the signal in real time. In addition, even when the information processing device 21 is not applied to real-time processing, the acquisition unit 131 may acquire the signal in real time.

[0032] Here, in the present embodiment, a case where the acquisition unit 131 has a function of acquiring signals measured by each of the signal sensors A1 to AL and a function of acquiring signals measured by each of the reference sensors B1 to BM is shown. However, as other examples, these functions may also be provided separately.

[0033] The output unit 112 performs output to the outside. The display unit 141 displays and outputs information on the signal processing result. The display unit 141 has a screen such as a liquid crystal display (LCD: Liquid Crystal Display), and displays and outputs information on the signal processing result to the screen. As another structural example, the display unit 141 may also print and output information on the signal processing result to paper. In addition, the output unit 112 may also have a function of performing output in other ways, such as sound output.

[0034] The storage unit 113 has a storage device such as a memory for storing information, for example. The storage unit 113 stores information such as the input signal and the processing result of the signal, for example. In addition, the storage unit 113 stores information such as a control program, for example.

[0035] The control unit 114 performs various processes and various controls. In the present embodiment, the control unit 114 has a processor such as a CPU (Central Processing Unit), and the processor executes the control program stored in the storage unit 113, thereby performing various processes and various controls. In addition, the processor includes an arithmetic unit for performing various operations.

[0036] The processing unit 151 performs a prescribed process based on the signals of the measurement results of the signal sensors A1 to AL and the signals of the measurement results of the reference sensors B1 to BM. In the present embodiment, the prescribed process is a process of removing the noise from the mixed signal of the target signal and the noise. Here, as the degree of removing the noise from the mixed signal, any degree that is practically effective can be used. That is, a method of completely removing the noise component from the mixed signal can be used, or a method of removing the noise component from the mixed signal to a necessary degree can also be used. In addition, the removal of the noise can also be referred to as noise reduction or noise suppression, for example.

[0037] The reference sensor data grouping unit 161 divides the data of the measurement results of the respective reference sensors B1 to BM (reference sensor data) into two or more prescribed numbers (Q) of groups. In the present embodiment, the plurality of reference sensors B1 to BM are divided into Q groups in advance. And the reference sensor data of each of the reference sensors B1 to BM are divided into the groups to which the respective reference sensors B1 to BM belong. That is, in the present embodiment, the plurality of reference sensors B1 to BM being divided into Q groups and the plurality of reference sensor data obtained from the plurality of reference sensors B1 to BM being divided into Q groups have the same meaning.

[0038] Here, as a method of dividing multiple reference sensors B1 to BM into two or more groups (that is, a method of dividing multiple reference sensor data into two or more groups), there is no particular limitation. For example, a method of dividing based on the arrangement order of reference sensors B1 to BM, a method of dividing based on the installation location of reference sensors B1 to BM, a method of dividing based on the installation orientation of reference sensors B1 to BM, or a method of dividing based on the type of reference sensors B1 to BM, etc. can be used.

[0039] As a specific example, as a method of dividing into two groups based on the arrangement order of reference sensors B1 to BM, a method like this can be used: for multiple reference sensors arranged along a specified direction, they are divided into groups of reference sensors with odd numbers such as the first, third, fifth, etc., and groups of reference sensors with even numbers such as the second, fourth, sixth, etc. (that is, a method of dividing alternately according to the arrangement order). Similarly, a method of dividing into three or more groups based on the arrangement order of reference sensors B1 to BM can also be used.

[0040] As a specific example, as a method of dividing into two groups based on the installation location of reference sensors B1 to BM, a method like this can be used: the distribution area of reference sensors B1 to BM is divided into two areas, and the reference sensors arranged in each area are set as reference sensors in the same group (that is, a method of dividing according to the installation location of the reference sensors). Similarly, a method of dividing into three or more groups based on the installation location of reference sensors B1 to BM can also be used.

[0041] As a specific example, as a method of dividing into two groups based on the installation orientation of reference sensors B1 to BM, a method like this can be used: the installation orientations of reference sensors B1 to BM are divided into two types of groups, and each reference sensor is divided into the group to which its installation orientation belongs (for example, a method of dividing according to the installation orientation of the reference sensors). Similarly, a method of dividing into three or more groups based on the installation direction of reference sensors B1 to BM can also be used.

[0042] As a specific example, as a method of dividing into two groups based on the type of reference sensors B1 to BM, a method of dividing into two groups according to the type of reference sensors B1 to BM (for example, a method of dividing according to the type of reference sensors) can be used. Similarly, a method of dividing into three or more groups based on the type of reference sensors B1 to BM can also be used.

[0043] Here, in the present embodiment, each group includes one or more reference sensor data (one or more reference sensors). In addition, in the present embodiment, grouping is performed such that the same reference sensor data (the same reference sensor) is not simultaneously assigned to two or more different groups.

[0044] In addition, in the present embodiment, the grouping method (grouping method) of a plurality of reference sensor data (a plurality of reference sensors) is pre-stored in the information processing device 21 (for example, the storage unit 113 or the like). And the reference sensor data grouping unit 161 groups the reference sensor data based on this method. In this case, the correspondence between each reference sensor data (each reference sensor) and each group is preset. As a specific example, when the measuring device 1 is shipped as a product, the grouping method (this correspondence) can be set in the information processing device 21 before shipment, and the grouping method (this correspondence) can be fixed after shipment. In addition, for example, the grouping method (this correspondence) set in the information processing device 21 can also be adjusted (changed) by manual judgment as needed before leaving the factory.

[0045] The first signal processing unit P1 to the Qth signal processing unit PQ perform processing to remove the noise included in the measurement result using the data of the measurement results of the signal sensors A1 to AL and the reference sensor data divided into Q groups. As the method of this processing in each signal processing unit, there is no particular limitation. For example, a method such as adaptive noise canceling (ANC: Adaptive Noise Canceling) as a method of removing noise can be used. The display control unit 152 displays and outputs various information to the screen of the display unit 141.

[0046] [Specific example of environmental noise removal processing] In this example, for the sake of convenience of explanation, the noise superimposed on the target signal is referred to as environmental noise for explanation. Refer to Figures 3 - 6 , and a specific example of the environmental noise removal processing performed by the information processing device 21 is shown. In the present embodiment, for the sake of convenience of explanation, the part that aggregates the L signal sensors A1 to AL is referred to as the signal sensor unit 331 for explanation, the part that aggregates the M reference sensors B1 to BM is referred to as the reference sensor unit 332 for explanation, and the part that aggregates the signal sensor unit 331 and the reference sensor unit 332 is referred to as the sensor unit 311 for explanation. The signal sensor unit 331 and the reference sensor unit 332 are provided separately, that is, neither the signal sensors A1 to AL nor the reference sensors B1 to BM have a common sensor.

[0047] [Specific example of the processing of the first structural example] Figure 3 This is a diagram schematically showing the process flow of the first structural example of the embodiment. Figure 3 It shows a sensor unit 311 including a signal sensor unit 331 and a reference sensor unit 332, and a processing unit 151a. Here, the processing unit 151a is Figure 2 an example of the processing unit 151 shown.

[0048] The processing unit 151a includes a reference sensor data grouping unit 161a, a primary signal processing unit P1a, and a secondary signal processing unit P2a. This example is a case where signal processing is performed in two stages. Here, the reference sensor data grouping unit 161a, the primary signal processing unit P1a, and the secondary signal processing unit P2a are respectively Figure 2 examples of the reference sensor data grouping unit 161, the primary signal processing unit P1, and the secondary signal processing unit P2 in

[0049] A specific example of the processing in this example will be described. The signal sensor unit 331 measures the measurement signal a1 (a mixed signal in this example), thereby obtaining the data of the measurement signal a1, that is, the signal sensor data a11. The signal sensor data a11 includes the signal sensor data of each signal sensor A1 to AL. Here, in this example, the measurement signal a1 includes a target signal and environmental noise. In addition, the reference sensor unit 332 measures the environmental noise signal a2 (an example of a reference signal, the same below), thereby obtaining the data of the environmental noise signal a2, that is, the reference sensor data a12. The reference sensor data a12 includes the reference sensor data of each reference sensor B1 to BM.

[0050] The reference sensor data grouping unit 161 performs grouping of the reference sensor data a12. In this example, the reference sensor data grouping unit 161a divides the multiple reference sensor data included in the reference sensor data a12 into two groups, namely, a first group and a second group. In this example, for the sake of convenience of explanation, the set of the first group of reference sensor data (which may sometimes be one reference sensor data) is referred to as the first group of data g1, and the set of the second group of reference sensor data (which may sometimes be one reference sensor data) is referred to as the second group of data g2 for explanation.

[0051] The primary signal processing unit P1a uses the signal sensor data a11 and the first group of data g1 to perform a specified process, and generates the data of the result of this process, that is, the data a21 after primary signal processing. Here, this process is a process of removing environmental noise included in the signal sensor data a11 using the first set of data g1.

[0052] The secondary signal processing unit P2a performs a prescribed process using the data a21 after the primary signal processing and the second set of data g2, and generates the data of the result of this process, that is, the data a31 after the secondary signal processing. Here, this process is a process of removing environmental noise included in the data a21 after the primary signal processing using the second set of data g2. In this way, in this example, the environmental noise removal process is performed in two stages. The data a31 after the secondary signal processing includes the extracted target signal and becomes the data from which the environmental noise has been removed.

[0053] In this example, regarding the signal sensor data, through the signal processing of each time, one set of the reference sensor data is used to perform the environmental noise removal process respectively. On the other hand, in this example, regarding the reference sensor data, the initial data (in Figure 3 the example is the reference sensor data a12) is used for the environmental noise removal process.

[0054] <Specific Example of the Process of the Second Structural Example> Figure 4 is a diagram schematically showing the flow of the process of the second structural example of the embodiment. Figure 4 Shows the sensor unit 311 including the signal sensor unit 331 and the reference sensor unit 332, and the processing unit 151b. Here, the processing unit 151b is Figure 2 an example of the processing unit 151 shown.

[0055] The processing unit 151b includes a reference sensor data grouping unit 161a, a primary signal processing unit P1b, and a secondary signal processing unit P2b. This example is an example of the case where the signal processing is performed in two stages. Here, the primary signal processing unit P1b and the secondary signal processing unit P2b are respectively Figure 2 an example of the primary signal processing unit P1 and the secondary signal processing unit P2 in

[0056] The specific example of the process in this example will be described. In this example, the process until the signal sensor data a11, the first set of data g1, and the second set of data g2 are obtained is the same as that of the first structural example, and the subsequent process is different from that of the first structural example. Therefore, in Figure 4 the example, the same reference numerals are assigned to the parts that are the same as those of the first structural example.

[0057] In this example, the signal sensor data a11, the first set of data g1, and the second set of data g2 are input to the first-stage signal processing unit P1b.

[0058] The first-stage signal processing unit P1b performs a prescribed process using the signal sensor data a11 and the first set of data g1, and generates the data of the result of this process, i.e., the first-stage signal processed data a51. Here, this process is a process of removing the environmental noise included in the signal sensor data a11 using the first set of data g1. In addition, as this process, when performing the same process on the same input as the first-stage signal processing in the first structural example, the first-stage signal processed data a51 is Figure 3 the same as the first-stage signal processed data a21 shown.

[0059] In addition, the first-stage signal processing unit P1b performs a prescribed process using the second set of data g2 and the first set of data g1, and generates the data of the result of this process, i.e., the first-stage signal processed second set of data g12. Here, this process is a process of removing the environmental noise included in the second set of data g2 using the first set of data g1.

[0060] The second-stage signal processing unit P2b performs a prescribed process using the first-stage signal processed data a51 and the first-stage signal processed second set of data g12, and generates the data of the result of this process, i.e., the second-stage signal processed data a61. Here, this process is a process of removing the environmental noise included in the first-stage signal processed data a51 using the first-stage signal processed second set of data g12. In this way, in this example, the environmental noise removal process is performed in two stages. The second-stage signal processed data a61 includes the extracted target signal and becomes the data from which the environmental noise has been removed.

[0061] In this example, regarding the signal sensor data, through the signal processing of each stage, the environmental noise removal process is performed respectively using one set of reference sensor data. In addition, in this example, for the reference sensor data of the set used in the second-stage signal processing, the environmental noise removal process is performed using the reference sensor data of other sets.

[0062] <Specific Example of the Process of the Third Structural Example> Figure 5 is a diagram schematically showing the flow of the process of the third structural example of the embodiment. Figure 5 Shows a sensor unit 311 including a signal sensor unit 331 and a reference sensor unit 332, and a processing unit 151c. Here, the processing unit 151c isFigure 2 An example of the processing unit 151 shown.

[0063] The processing unit 151c includes a reference sensor data grouping unit 161c, a primary signal processing unit P1c, a secondary signal processing unit P2c, and a tertiary signal processing unit P3c. This example is an example of the case where signal processing is performed in three stages. Here, the reference sensor data grouping unit 161c, the primary signal processing unit P1c, the secondary signal processing unit P2c, and the tertiary signal processing unit P3c are respectively Figure 2 an example of the reference sensor data grouping unit 161, the primary signal processing unit P1, the secondary signal processing unit P2, and the tertiary signal processing unit P3 in

[0064] A specific example of the processing in this example will be described. In this example, the processing until the signal sensor data a11 and the reference sensor data a12 are obtained is the same as that in the first structural example, and the subsequent processing is different from that in the first structural example. Therefore, in Figure 5 the example of

[0065] The reference sensor data grouping unit 161c performs grouping of the reference sensor data a12. In this example, the reference sensor data grouping unit 161c divides the multiple reference sensor data included in the reference sensor data a12 into three groups, namely, a first group, a second group, and a third group. In this example, for the sake of convenience of explanation, the set of the first group of reference sensor data (which may sometimes be one reference sensor data) is referred to as the first group of data g101, the set of the second group of reference sensor data (which may sometimes be one reference sensor data) is referred to as the second group of data g102, and the set of the third group of reference sensor data (which may sometimes be one reference sensor data) is referred to as the third group of data g103.

[0066] The primary signal processing unit P1c uses the signal sensor data a11 and the first group of data g101 to perform a prescribed process, and generates the data of the result of this process, that is, the primary signal processed data a101. Here, this process is a process of removing the environmental noise included in the signal sensor data a11 using the first group of data g101.

[0067] The secondary signal processing unit P2c uses the primary signal processed data a101 and the second group of data g102 to perform a prescribed process, and generates the data of the result of this process, that is, the secondary signal processed data a111. Here, this process is a process of removing the environmental noise included in the primary signal processed data a101 using the second group of data g102.

[0068] The third signal processing unit P3c uses the data a111 after the second signal processing and the third set of data g103 to perform a specified process, and generates the data of the result of this process, that is, the data a121 after the third signal processing. Here, this process is a process of using the third set of data g103 to remove the environmental noise included in the data a111 after the second signal processing. In this way, in this example, the environmental noise removal process is performed in three stages. The data a121 after the third signal processing includes the extracted target signal and becomes the data from which the environmental noise has been removed.

[0069] In this example, regarding the signal sensor data, through the signal processing of each time, one group of the reference sensor data is used respectively to perform the environmental noise removal process. On the other hand, in this example, regarding the reference sensor data, the initial data (in Figure 3 the example of is the reference sensor data a12) is used for the environmental noise removal process. In addition, in this embodiment, a structural example of using the signal processing units in three stages from the first to the third is shown, but for example, it may also be a structure using signal processing units in four or more stages. In this case, the same number of groups as the number of stages of the signal processing unit is set.

[0070] <Specific Example of the Process of the Fourth Structural Example> Figure 6 It is a diagram schematically showing the process flow of the fourth structural example of the embodiment. Figure 6 It shows a sensor unit 311 including a signal sensor unit 331 and a reference sensor unit 332, and a processing unit 151d. Here, the processing unit 151d is Figure 2 an example of the processing unit 151 shown.

[0071] The processing unit 151d includes a reference sensor data grouping unit 161c, a first signal processing unit P1d, a second signal processing unit P2d, and a third signal processing unit P3d. This example is an example of the case where the signal processing is performed in three stages. Here, the first signal processing unit P1d, the second signal processing unit P2d, and the third signal processing unit P3d are respectively Figure 2 examples of the first signal processing unit P1, the second signal processing unit P2, and the third signal processing unit P3 in

[0072] A specific example of the process in this example will be described. In this example, the processing up to obtaining the signal sensor data a11, the first group of data g101, the second group of data g102, and the third group of data g103 is the same as that of the third structural example, and the subsequent processing is different from that of the third structural example. Therefore, in Figure 6 the example of, the same reference numerals are assigned to the parts that are the same as those of the third structural example.

[0073] In this example, the signal sensor data a11, the first group of data g101, the second group of data g102, and the third group of data g103 are input to the first-stage signal processing unit P1d.

[0074] The first-stage signal processing unit P1d performs a prescribed process using the signal sensor data a11 and the first group of data g101, and generates the data of the result of this process, i.e., the first-stage signal-processed data a201. Here, this process is a process of removing the environmental noise included in the signal sensor data a11 using the first group of data g101. In addition, as this process, when performing the same process on the same input as the first-stage signal processing in the third structural example, the first-stage signal-processed data a201 is the same as Figure 5 the first-stage signal-processed data a101 shown.

[0075] In addition, the first-stage signal processing unit P1d performs a prescribed process using the second group of data g102 and the first group of data g101, and generates the data of the result of this process, i.e., the first-stage signal-processed second group of data g112. Here, this process is a process of removing the environmental noise included in the second group of data g102 using the first group of data g101.

[0076] In addition, the first-stage signal processing unit P1d performs a prescribed process using the third group of data g103 and the first group of data g101, and generates the data of the result of this process, i.e., the first-stage signal-processed third group of data g113. Here, this process is a process of removing the environmental noise included in the third group of data g103 using the first group of data g101.

[0077] The second-stage signal processing unit P2d performs a prescribed process using the first-stage signal-processed data a201 and the first-stage signal-processed second group of data g112, and generates the data of the result of this process, i.e., the second-stage signal-processed data a211. Here, this process is a process of removing the environmental noise included in the first-stage signal-processed data a201 using the first-stage signal-processed second group of data g112.

[0078] In addition, the secondary signal processing unit P2d performs a prescribed process using the third set of data g113 after the first signal processing and the second set of data g112 after the first signal processing, and generates the data of the result of this process, that is, the third set of data g123 after the secondary signal processing. Here, this process is a process of using the second set of data g112 after the first signal processing to remove the environmental noise included in the third set of data g113 after the first signal processing.

[0079] The tertiary signal processing unit P3d performs a prescribed process using the data a211 after the secondary signal processing and the third set of data g123 after the secondary signal processing, and generates the data of the result of this process, that is, the data a221 after the tertiary signal processing. Here, this process is a process of using the third set of data g123 after the secondary signal processing to remove the environmental noise included in the data a211 after the secondary signal processing. In this way, in this example, the environmental noise removal process is performed in three stages. The data a221 after the tertiary signal processing includes the extracted target signal and becomes the data from which the environmental noise has been removed.

[0080] In this example, regarding the signal sensor data, through the signal processing of each number of times, the environmental noise removal process is performed using one set of reference sensor data respectively. In addition, in this example, regarding the reference sensor data of the set used in the signal processing after the secondary signal processing, the environmental noise removal process is performed using the reference sensor data of other sets. Furthermore, in the present embodiment, a structural example of using the signal processing units in three stages from the first to the third is shown, but for example, it may also be a structure using signal processing units in four or more stages. In this case, the same number of sets as the number of stages of the signal processing units is set.

[0081] Here, in this example, a structure is shown in which in the signal processing units of each number of times, the environmental noise removal process using the reference sensor data of other sets is surely performed on the reference sensor data of the set output to the signal processing unit of the next number of times. However, as another example, such an environmental noise removal process (that is, the environmental noise removal process performed on the reference sensor data of each set) may be performed only on a part of it.

[0082] [Example of waveform after environmental noise removal] Figure 7 It is a diagram showing an example of the noise density after the signal processing of the embodiment. In Figure 7In the shown curve graph, the horizontal axis represents frequency [Hz], and the vertical axis represents the power spectral density function (PSD) [pT / √Hz]. This curve graph shows the characteristic 1011 of the result obtained by the environmental noise removal method of this embodiment and the characteristic 1031 of the result obtained by the environmental noise removal method of the comparative example.

[0083] Here, as the environmental noise removal method of this embodiment, the method of Figure 3 the first structural example shown (the method of environmental noise removal in two stages) is used. Specifically, in the environmental noise removal method of this embodiment, 18 reference sensors (reference sensors B1 to B18) are divided into two groups of 9 each (the first group and the second group). In the first signal processing unit P1, the signal sensor data is subjected to environmental noise removal processing based on a specified frequency division ANC (using the method described later Figures 8 - 9 to be described) using the first group of reference sensor data. In the second signal processing unit P2, the data of the result of the first signal processing is subjected to environmental noise removal processing based on ANC using the second group of reference sensor data.

[0084] In addition, as the environmental noise removal method of the comparative example, a one-stage environmental noise removal method is used. Specifically, in the environmental noise removal method of the comparative example, the reference sensor data of 18 reference sensors is used to perform environmental noise removal processing on the signal sensor data based on ANC.

[0085] As Figure 7 shown, in the environmental noise removal method of this embodiment, the accuracy of environmental noise removal is higher than that of the environmental noise removal method of the comparative example.

[0086] [Examples of signal processing] As the signal processing performed by each signal processing unit from the first signal processing unit P1 to the PQth signal processing unit PQ, various signal processings can be used. In addition, as the combination of multi-stage signal processing performed by the multi-stage signal processing unit, various combinations can also be used. Furthermore, in the multi-stage signal processing unit, for example, a method in which all signal processings are different processing steps can be used, or a method in which two or more signal processings are the same processing steps can also be used.

[0087] As an example, as the signal processing method, the ANC method can be used. As other examples, as a method of signal processing, it can also be a method using ANC that performs frequency division (in this embodiment, it is called frequency division ANC).

[0088] For example, in the structure of performing primary signal processing and secondary signal processing, the primary signal processing and the secondary signal processing can be the processing of ANC and the processing of ANC, or the processing of ANC and the processing of frequency division ANC, or the processing of frequency division ANC and the processing of ANC, or the processing of frequency division ANC and the processing of frequency division ANC. In addition, in the structure of performing signal processing at three or more levels, for example, ANC or frequency division ANC can be arbitrarily assigned to each signal processing.

[0089] [Specific example of signal processing of frequency division ANC] Refer to Figures 8 - 9 , which shows an example of frequency division ANC. Figure 8 It is a diagram showing a structural example of the signal processing unit P of the embodiment. Here, the signal processing unit P can be applied to one or more of the primary signal processing unit P1 to the Q - th signal processing unit PQ. The signal processing unit P includes an interval division unit 171, a frequency range division unit 172, and a coefficient calculation unit 173.

[0090] The interval division unit 171 divides the time period into a predetermined interval. Here, various methods can be used for the number of intervals or the length of the interval (interval length), etc. In addition, the length of time can be counted based on the number of samples of the signal data, for example.

[0091] The frequency range division unit 172 divides the time - series signal into signals for each frequency range. The frequency range division unit 172 can have, for example, a function of Fourier transform. This function can be a function of fast Fourier transform (FFT: Fast Fourier Transform). In this example, frequency analysis is performed on the signals of the measurement results of all signal sensors A1 to AL and the signals of the measurement results of all reference sensors used in the frequency division ANC. The coefficient calculation unit 173 calculates a predetermined coefficient. In this example, this coefficient is a coefficient for removing noise.

[0092] In this example, for the sake of convenience of explanation, it is assumed that all the reference sensors B1 to BM are used for frequency division ANC and the explanation is given using mathematical expressions. However, in the case where only a part of the reference sensors is used as in the present embodiment, the reference sensors in the following explanation are limited to a part of the reference sensors (in the present embodiment, the reference sensors of a specific group).

[0093] In this example, the signal processing unit P analyzes each of a plurality of time intervals and each of a plurality of frequency ranges. In this analysis, for example, the method of ANC, which is a method of removing noise, can be used. In addition, generally, the method of ANC does not use a plurality of time intervals but is performed over the entire time series.

[0094] The signal data obtained from one or more signal sensors A1 to AL is represented by y(f). The signal data y(f) is, for example, the result of performing FFT on the measurement result. In this example, f represents a frequency component, and (f) represents a function of f. Here, the signal data y(f) can be, for example, the signal data of the measurement result of one signal sensor Aj (j = 1 to L), or can also be the signal data that is the result of performing a prescribed operation on the measurement results of two or more signal sensors Aj. As the prescribed operation, for example, an operation such as averaging can be used.

[0095] Let x i (f) represent the signal data of the measurement result of the i-th (i = 1 to M) reference sensor Bi. The signal data x i (f) is, for example, the result of performing FFT on the measurement result. In this case, the signal data v(f) which is the result of removing noise from the signal data y(f) is represented by Equation (1). Here, w1 to w M are filter coefficients for weighting, and the vector w represents a vector composed of M filter coefficients. v(f) = y(f) - w1x1(f) - w2x2(f) - … - w M x M (f) … (1)

[0097] Equation (1) is obtained by calculating Equation (2). In addition, in Equation (2), Σyx is obtained by general calculation. Also, Σxx -1 is the inverse matrix of the covariance matrix Σxx of the measurement results of the reference sensors B1 to BM.

[0098]

[0099] The calculation of the covariance matrix ∑xx is described. The measurement results of multiple reference sensors B1 to BM are represented by the vector x(f) shown in Equation (3).

[0100]

[0101] The covariance matrix ∑xx is calculated by Equation (4). * represents conjugate transpose.

[0102]

[0103] Here, N represents the number of divisions (total number of intervals) when the signal data is divided into multiple intervals. There is a vector x(f) in each interval, and there are N vectors x(f) in total for N intervals. To obtain a stable value, N needs to be of a certain magnitude. N affects the calculation accuracy of the vector w of the filter coefficient. To stably calculate the inverse matrix ∑xx of the covariance matrix ∑xx -1 , generally, it is necessary to satisfy N > 3M to about 10M.

[0104] In this example, for the stabilization of the operation result determined by N, regularization represented by Equation (5) is performed. Through regularization, for example, overestimation can be prevented. Here, γ represents the regularization parameter, which is generally set to 10 -2 to 10 -6 times the maximum eigenvalue of the covariance matrix. I represents the identity matrix. Σ xx → Σ xx + γλmax(Σ xx )I… (5)

[0106] <Interval division> Figure 9 is a diagram showing an example of interval division. In Figure 9 , the axis representing time (t) is shown as the horizontal axis. In addition, Figure 9 an example of the signal data 211 of the time series signal is shown. In the example of Figure 9 , the case where the time series signal is a sine wave is shown, but it is not limited to this. In the example of Figure 9 , the overall length of the signal data 211 to be processed as a signal is represented by the period T. Here, the signal data 211 is data of a signal obtained from the measurement results of signal sensors A1 to AL. The length of the signal data 211 (signal data length) corresponds to the period of the measurement result (length of time). In addition, in the present embodiment, the sampling period of the signal data 211 is a prescribed period (for example, a fixed period). In addition, the total number of intervals into which the period T is divided is N.

[0107] The N intervals are represented by the r-th interval (r = 1 to N). In Figure 9 the example, the intervals from the interval with r = 1 to the interval with r = N are arranged in order from an earlier time to a later time. In this example, all intervals have the same length (interval length). This interval length is represented by Tw. In addition, in Figure 9 the example, two adjacent intervals, that is, the r-th interval and the (r + 1)-th interval, overlap by 1 / 2 of the interval length (i.e., Tw / 2). Here, the total number N of divided intervals can be expressed approximately as in Equation (6) using the entire period T and the interval length Tw.

[0108] N ∼ T / (Tw / 2)…(6)

[0109] In this way, in this example, the signal with the total data length T is divided into intervals with the interval length Tw, and N x(f) are calculated using the data included in each interval, whereby the covariance matrix Σxx can be calculated, and thus the noise removal result can be calculated. For example, in order to be able to cope with various environmental noises, it is necessary to increase the number of reference sensors B1 to BM. However, in the method of this example, it is possible to cope by varying the interval length Tw of the data division and setting N within an optimal range.

[0110] Here, in this example, the case where two adjacent intervals overlap by 1 / 2 of the interval length is shown, but the degree of overlap can also be arbitrary. For example, a method in which two adjacent intervals are connected without overlapping can also be used. Approximately, it can be described as follows. That is, when two adjacent intervals overlap by more than 1 / 2 of the interval length, in the discrete data sampled under the time window, components above the Nyquist frequency are not included, and so-called aliasing does not occur. On the contrary, if the overlap is 1 / 2 or less of the interval length, aliasing may occur. In addition, in this example, the case where all intervals have the same interval length is shown, but intervals with different interval lengths can also be included in multiple intervals.

[0111] Thus, in the frequency division ANC of this example, signal measurement results of one or more signal sensors that measure a mixed signal in which a target signal and noise coexist, and noise measurement results of a plurality of reference sensors that measure the noise are obtained. Further, in this method, for each of a plurality of time-divided intervals, the obtained signal measurement results and noise measurement results are each divided into a plurality of frequency ranges, and signal processing for removing the noise included in the mixed signal is performed. The signal processing unit uses, for example, intervals that each have the same interval length and in which the interval length is repeated by 1 / 2 for two adjacent intervals. This interval is determined, for example, based on the number of reference sensors and the signal data length of the signal to be processed. For example, the signal processing unit performs signal processing through an operation using a covariance matrix, and a regularization parameter that adjusts the degree of regularization when regularizing the covariance matrix is determined based on the number of reference sensors and the signal-to-noise ratio of the signal sensor data in this interval.

[0112] [Summary of the Embodiment] As described above, in the measuring device 1 of the present embodiment, a plurality of reference sensors B1 to BM can be used, and the accuracy of removing noise from the measurement results of the required signal can be improved. In the measuring device 1 of the present embodiment, together with signal sensors A1 to AL for measuring a target signal, a plurality of reference sensors B1 to BM for measuring noise are provided, and a plurality of reference sensor data obtained by the plurality of reference sensors B1 to BM are divided into two or more groups, and these groups are used to perform the same or different signal processing by a signal processing unit in two or more stages. Therefore, by using a plurality of groups and combining signal processing in two or more stages, the effect of removing the noise included in the measurement results can be improved, and high-precision measurement can be achieved.

[0113] For example, in the measuring device 1, by combining hardware for removing various noises (interference signals other than the target signal) and a signal processing method utilizing its characteristics, the added value of the measurement can be improved. In particular, by using a plurality of reference sensors B1 to BM, various noises can be accurately removed from the measurement signal.

[0114] For example, in the measuring device 1, as in the case of using different noise removal methods in the first-stage signal processing and the second-stage signal processing, by using different noise removal methods in multi-stage signal processing, specific noises of different natures can be removed by each noise removal method. That is, in the measuring device 1, in the signal processing of each number of times, noise removal processing corresponding to different types of noise can be performed. In the present embodiment, the noise removal effect can be improved while maintaining the same number of reference sensors. Therefore, the number of reference sensors can also be reduced while maintaining the noise removal effect.

[0115] In addition, in Patent Document 1, multiple reference sensors are used to cope with 3-axis detection. However, for example, there is no disclosure or suggestion regarding the use of multiple reference sensors to obtain various noises, nor is there any disclosure or suggestion regarding grouping multiple reference sensor data obtained by multiple reference sensors and repeatedly performing (multistage) signal processing multiple times.

[0116] As a structural example (hereinafter, for ease of explanation, referred to as structural example α1), the measurement device 1 includes: one or more signal sensors A1 to AL (measurement sensors) that obtain a measurement signal regarding a measurement signal measured in a state where a target signal (signal of interest) and environmental noise coexist; two or more reference sensors B1 to BM that obtain environmental noise; a reference sensor data grouping unit 161 that groups the reference sensor data obtained by the reference sensors B1 to BM; a first signal processing unit P1 that uses one group of the grouped reference sensor data to perform noise removal processing on the signal sensor data obtained by the signal sensors A1 to AL; and a second signal processing unit that uses another group to perform noise removal processing on the data after the first signal processing, which is the result of the processing by the first signal processing unit P1. Therefore, in the measurement device 1, by grouping and using multiple reference sensor data, for example, more effective signal processing can be achieved without increasing the number of reference sensors. In addition, for example, the number of reference sensors can be reduced without degrading the accuracy of signal processing.

[0117] Here, in the measurement device 1, for example, it may also be configured to include three or more reference sensors B1 to BM, perform three or more groupings, and for the subsequent stages, also use one group of reference sensor data from the remaining groups to perform noise removal processing on the data after the signal processing of the previous stage number of times. Thereby, in the measurement device 1, more effective signal processing can be achieved.

[0118] As another structural example (hereinafter, for ease of explanation, referred to as structural example β1), in the measuring device 1, it includes: one or more signal sensors A1 to AL (measurement sensors) that acquire a measurement signal regarding a measurement signal measured in a state where a target signal (signal of interest) and ambient noise coexist; two or more reference sensors B1 to BM that acquire ambient noise; a reference sensor data grouping unit 161 that groups the reference sensor data acquired by the reference sensors B1 to BM; a primary signal processing unit P1 that uses one group of the grouped reference sensor data to perform noise removal processing on the signal sensor data acquired by the signal sensors A1 to AL and also performs noise removal processing on another group of the reference sensor data; and a secondary signal processing unit that uses the processing result of the other group processed by the primary signal processing unit P1 to perform noise removal processing on the processed result of the signal sensor data processed by the primary signal processing unit P1, that is, the data after the primary signal processing. Therefore, in the measuring device 1, by grouping and using a plurality of reference sensor data, for example, more effective signal processing can be achieved without increasing the number of reference sensors. Additionally, for example, the number of reference sensors can be reduced without degrading the accuracy of signal processing.

[0119] Here, in the measuring device 1, for example, it may also be configured to have three or more reference sensors B1 to BM, perform three or more groupings, and in the first instance, use one group of the grouped reference sensor data to perform noise removal processing on the signal sensor data acquired by the signal sensors A1 to AL and also perform noise removal processing on another group of the reference sensor data. After the second time, except for the last stage, use one group of the remaining groups of reference sensor data (in this example, the data after noise removal processing) to perform noise removal processing on another group of the reference sensor data (in this example, the data after noise removal processing), and in all times after the second time, use this one group of the reference sensor data (in this example, the data after noise removal processing) to perform noise removal processing on the data after the signal processing of the previous stage (the result of the noise removal processing of the signal sensor data up to the previous stage). Thereby, in the measuring device 1, more effective signal processing can be achieved.

[0120] In the above structural examples α1 and β1, the reference sensors B1 to BM may include two or more of the same type of sensors as the signal sensors A1 to AL or different types of sensors from the signal sensors A1 to AL. Thus, in the measuring device 1, it is possible to remove environmental noise from noise sources (e.g., two or more noise sources) that generate different noises such as magnetic noise and vibration noise.

[0121] In the above-described structural examples α1 and β1, ANC can be used as a noise removal method in the signal processing unit for one or more times. Thus, in the measuring device 1, effective noise removal can be performed using ANC.

[0122] In the above-described structural examples α1 and β1, ANC can also be used as a noise removal method in the signal processing unit for one or more times, and in other signal processing units for one or more times, frequency division ANC can be used as a noise removal method. Thus, in the measuring device 1, effective noise removal can be performed using a combination of ANC and frequency division ANC. For example, by performing frequency decomposition processing for each interval using frequency division ANC, noise removal can be performed more effectively.

[0123] In the above-described structural examples α1 and β1, the measuring device 1 can be applied, for example, to the case where the signal sensors A1 to AL are all magnetic sensors. Thus, in the measuring device 1, it is possible to remove the noise superimposed on the output signals of the magnetic sensors (signal sensors A1 to AL).

[0124] [Regarding the above embodiments] In addition, a program for implementing the functions of any constituent part in any of the above-described devices can be recorded on a computer-readable recording medium, and the computer system can read and execute this program. Here, the "computer system" includes hardware such as an operating system or peripheral devices. In addition, the "computer-readable recording medium" refers to removable media such as floppy disks, optical disks, ROMs, CD (Compact Disc)-ROMs (Read Only Memories), and storage devices such as hard disks built into the computer system. Furthermore, the "computer-readable recording medium" includes media such as volatile memories inside a server or a client computer system when a program is transmitted via a network such as the Internet or a communication line such as a telephone line and can hold the program for a certain period of time. This volatile memory can be, for example, RAM (Random Access Memory). The recording medium can be, for example, a non-transitory recording medium.

[0125] In addition, the above program can also be transmitted from a computer system that stores the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the "transmission medium" for transmitting the program refers to a medium having a function of transmitting information such as a network like the Internet or a communication line like a telephone line. In addition, the above program can also be used to implement a part of the above functions. Moreover, the above program can also be a program that can implement the above functions by combining with a program already recorded in the computer system, that is, a so-called differential file. The differential file can also be referred to as a differential program.

[0126] In addition, the functions of any structural part in any of the above-described devices can also be implemented by a processor. For example, each process in the embodiment can 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 can implement the functions of each part by individual hardware, for example, or can also implement the functions of each part by integrated hardware. For example, the processor can include hardware, and the hardware can include at least one of a circuit that processes digital signals and a circuit that processes analog signals. For example, the processor can be constituted by using one or both of one or more circuit devices or one or more circuit elements mounted on a circuit board. As the circuit device, an IC (Integrated Circuit) or the like can be used, and as the circuit element, a resistor or a capacitor or the like can be used.

[0127] Here, the processor can be a CPU, for example. However, the processor is not limited to the CPU, and various processors such as a GPU (Graphics Processing Unit) or a DSP (Digital Signal Processor) can also be used, for example. In addition, the processor can be a hardware circuit based on an ASIC (Application Specific Integrated Circuit), for example. In addition, the processor can be constituted by multiple CPUs, for example, or can also be constituted by multiple hardware circuits based on ASICs. In addition, the processor can be constituted by a combination of multiple CPUs and multiple hardware circuits based on ASICs. Furthermore, the processor can include one or more of an amplification circuit or a filter circuit or the like that processes analog signals, for example.

[0128] The embodiments of the present disclosure have been described in detail with reference to the drawings, but the specific structure is not limited to this embodiment and also includes designs and the like within the scope not departing from the gist of the present disclosure.

[0129] [Supplementary Note] (Structural Examples 1 - 7) are shown.

[0130] (Structural Example 1) A measuring device, comprising: A signal sensor unit having one or more signal sensors for measuring a measurement signal in which a target signal and noise coexist; A reference sensor unit having Q or more reference sensors for measuring a reference signal including the noise, where Q is an integer of 2 or more; and A processing unit, The processing unit includes: a reference sensor data grouping unit that divides data of the Q or more reference signals measured by the Q or more reference sensors of the reference sensor unit, i.e., reference sensor data, into Q groups, i.e., the first group to the Qth group; and A k-th signal processing unit for each k from 1 to Q and not exceeding Q, where k is an integer from 1 to Q and not exceeding Q, The first signal processing unit performs: using the reference sensor data of the first group, performing noise removal processing on the data of the measurement signal measured by the signal sensor, i.e., signal sensor data, or using the reference sensor data of the first group, performing noise removal processing on the data of the measurement signal measured by the signal sensor, i.e., signal sensor data, and the reference sensor data of one or more groups other than the first group, The u-th signal processing unit performs: using the reference sensor data of the u-th group or the signal-processed reference sensor data obtained by performing noise removal processing on the reference sensor data of the u-th group by one or more of the first signal processing unit to the (u - 1)-th signal processing unit, performing noise removal processing on the (u - 1)-th signal-processed data obtained by performing noise removal processing on the signal sensor data by the (u - 1)-th signal processing unit, or using the reference sensor data of the u-th group or the signal-processed reference sensor data obtained by performing noise removal processing on the reference sensor data of the u-th group by one or more of the first signal processing unit to the (u - 1)-th signal processing unit, performing noise removal processing on the (u - 1)-th signal-processed data obtained by performing noise removal processing on the signal sensor data by the (u - 1)-th signal processing unit and the reference sensor data of one or more groups other than the first group to the u-th group or the signal-processed reference sensor data obtained by performing noise removal processing on the reference sensor data by one or more of the first signal processing unit to the (u - 1)-th signal processing unit, where u is an integer from 2 to (Q - 1) and not exceeding (Q - 1), The Q-th signal processing unit performs: using the reference sensor data of the Q-th group or the signal-processed reference sensor data obtained by removing noise from the reference sensor data of the Q-th group by one or more of the first to (Q - 1)-th signal processing units, to perform noise removal processing on the (Q - 1)-th signal-processed data obtained by removing noise from the signal sensor data by the (Q - 1)-th signal processing unit.

[0131] Here, the first signal processing unit will be described. As an example, the first signal processing unit performs: using the reference sensor data of the first group, to perform noise removal processing on the data of the measurement signal measured by the signal sensor, that is, the signal sensor data. As another example, the first signal processing unit performs: using the reference sensor data of the first group, to perform noise removal processing on the data of the measurement signal measured by the signal sensor, that is, the signal sensor data, and the reference sensor data of one or more groups other than the first group. That is, the first signal processing unit uses the reference sensor data of the first group to perform noise removal processing on at least the signal sensor data among the data of the measurement signal measured by the signal sensor, that is, the signal sensor data, and the reference sensor data of one or more groups other than the first group.

[0132] In addition, the u-th signal processing unit will be described, where u is an integer greater than or equal to 2 and less than or equal to (Q - 1). As an example, the u-th signal processing unit performs: using the reference sensor data of the u-th group or the signal-processed reference sensor data obtained by removing noise from the reference sensor data of the u-th group by one or more of the first to (u - 1)-th signal processing units, to perform noise removal processing on the (u - 1)-th signal-processed data obtained by removing noise from the signal sensor data by the (u - 1)-th signal processing unit. As another example, the u-th signal processing unit performs: using the reference sensor data of the u-th group or the signal-processed reference sensor data obtained by performing noise removal processing on the reference sensor data of the u-th group by one or more of the first signal processing unit to the (u-1)-th signal processing unit, to perform noise removal processing on the (u-1)-th signal-processed data obtained by performing noise removal processing on the signal sensor data by the (u-1)-th signal processing unit, and the reference sensor data of one or more groups other than the first group to the u-th group or the signal-processed reference sensor data obtained by performing noise removal processing on the reference sensor data of one or more groups other than the first group to the u-th group by one or more of the first signal processing unit to the (u-1)-th signal processing unit. That is, as an example, the u-th signal processing unit uses the reference sensor data of the u-th group, and as another example, uses the signal-processed reference sensor data obtained by performing noise removal processing on the reference sensor data of the u-th group by one or more of the first signal processing unit to the (u-1)-th signal processing unit. In addition, the u-th signal processing unit performs noise removal processing on at least the signal sensor data among the (u-1)-th signal-processed data obtained by performing noise removal processing on the signal sensor data by the (u-1)-th signal processing unit and the specified data regarding one or more groups other than the first group to the u-th group. Here, as an example, the specified data is the reference sensor data of one or more groups other than the first group to the u-th group, and as another example, is the signal-processed reference sensor data obtained by performing noise removal processing on the reference sensor data of one or more groups other than the first group to the u-th group by one or more of the first signal processing unit to the (u-1)-th signal processing unit. As still another example, for two or more groups, it may be a combination of them (a combination of the reference sensor data regarding one or more groups and the signal-processed reference sensor data regarding one or more other groups).

[0133] The Q-th signal processing unit will be described. As an example, the Q-th signal processing unit uses the reference sensor data of the Q-th group, and as another example, uses the signal-processed reference sensor data obtained by performing noise removal processing on the reference sensor data of the Q-th group by one or more of the first signal processing unit to the (Q-1)-th signal processing unit. And, the Q-th signal processing unit performs noise removal processing on the (Q-1)-th signal-processed data obtained by performing noise removal processing on the signal sensor data by the (Q-1)-th signal processing unit.

[0134] Thus, the primary signal processing unit uses the reference sensor data of the first group to perform noise removal processing on the signal sensor data. Alternatively, the primary signal processing unit may use the reference sensor data of the first group to perform noise removal processing on the reference sensor data of one or more groups among the second group to the Qth group. The u-th signal processing unit (each of the second signal processing unit to the (Q - 1)-th signal processing unit) uses the reference sensor correlation data of the u-th group to perform noise removal processing on the measurement correlation data from the signal processing unit of the previous stage (i.e., the previous u - 1 times). This measurement correlation data is the data on which noise removal processing has been sequentially performed up to the signal processing unit of the previous stage (i.e., the previous u - 1 times) (the (u - 1)-th signal processing data after noise removal processing). This reference sensor correlation data is the data on which noise removal processing has never been performed up to the signal processing unit of the previous stage (i.e., the previous u - 1 times) (reference sensor data), or the data on which noise removal processing has been performed one or more times up to the signal processing unit of the previous stage (i.e., the previous u - 1 times) (signal - processed reference sensor data). Alternatively, the u-th signal processing unit (each of the second signal processing unit to the (Q - 1)-th signal processing unit) may use the reference sensor correlation data of the u-th group to perform noise removal processing on the reference sensor correlation data of one or more groups among the (u + 1)-th group to the Qth group. The Q-th signal processing unit uses the reference sensor correlation data of the Qth group to perform noise removal processing on the measurement correlation data from the (Q - 1)-th signal processing unit.

[0135] (Structural Example 2) The measuring device as described in (Structural Example 1), wherein, Q is 2, The primary signal processing unit uses the reference sensor data of the first group to perform noise removal processing on the signal sensor data, The second signal processing unit uses the reference sensor data of the second group other than the first group to perform noise removal processing on the first - stage signal - processed data obtained by the primary signal processing unit performing noise removal processing on the signal sensor data.

[0136] (Structural Example 3) The measuring device as described in (Structural Example 1), wherein, Q is 2, The primary signal processing unit uses the reference sensor data of the first group to perform noise removal processing on both the signal sensor data and the reference sensor data of the second group other than the first group. The secondary signal processing unit performs noise removal processing on the primary signal processed data obtained by the primary signal processing unit performing noise removal processing on the signal sensor data, using the signal processed reference sensor data obtained by the primary signal processing unit performing noise removal processing on the reference sensor data of the second group.

[0137] (Structural Example 4) The measuring device according to any one of (Structural Example 1) to (Structural Example 3), wherein The reference sensor unit includes two or more sensors among sensors of the same type as the signal sensor and sensors of a different type from the signal sensor as the reference sensors.

[0138] (Structural Example 5) The measuring device according to any one of (Structural Example 1) to (Structural Example 4), wherein The K-th signal processing unit uses ANC as a method for noise removal processing, where K is an integer greater than or equal to 1 and less than or equal to Q.

[0139] (Structural Example 6) The measuring device according to (Structural Example 5), wherein The U-th signal processing unit uses frequency division ANC as a method for noise removal processing, where U is an integer greater than or equal to 1 and less than or equal to Q and different from K.

[0140] (Structural Example 7) The measuring device according to any one of (Structural Example 1) to (Structural Example 6), wherein The signal sensor unit includes a magnetic sensor as the signal sensor. Explanation of Reference Numerals

[0141] 1... Measuring device, 11... Signal sensor configuration unit, 21... Information processing device, 31... Object to be measured, 111... Input unit, 112... Output unit, 113... Storage unit, 114... Control unit, 131... Acquisition unit, 141... Display unit, 151, 151a, 151b, 151c, 151d... Processing unit, 152... Display control unit, 161, 161a, 161c... Reference sensor data grouping unit, 171... Interval division unit, 172... Frequency range division unit, 173... Coefficient calculation unit, 211... Signal data, 311... Sensor unit, 331... Signal sensor unit, 332... Reference sensor unit, 1011, 1031... Characteristics, A1 to AL... Signal sensors, B1 to BM... Reference sensors, P (P1 to PQ)... Signal processing unit, P1, P1a, P1b, P1c, P1d... First-order signal processing unit, P2, P2a, P2b, P2c, P2d... Second-order signal processing unit, P3c, P3d... Third-order signal processing unit, PQ... Qth-order signal processing unit

Claims

1. A measuring device, wherein: have: A signal sensor unit including one or more signal sensors for measuring a measurement signal in which a target signal and noise are mixed; a reference sensor unit having Q or more reference sensors for measuring a reference signal including the noise, wherein Q is an integer greater than or equal to 2; and Processing Department, The processing unit includes: a reference sensor data grouping unit that divides reference sensor data, which is data of the Q or more reference signals measured by the Q or more reference sensors of the reference sensor unit, into first to Q-th groups as Q groups; and k is an integer greater than or equal to 1 and less than or equal to Q, each of the k-th signal processing units, wherein k is an integer greater than or equal to 1 and less than or equal to Q, The primary signal processing unit performs: using the reference sensor data of the first group to perform noise removal processing on the signal sensor data, which is the data of the measurement signal measured by the signal sensor, or using the reference sensor data of the first group to perform noise removal processing on the signal sensor data, which is the data of the measurement signal measured by the signal sensor, and the reference sensor data of one or more groups other than the first group, The u-th signal processing unit performs: using the reference sensor data of the u-th group or the signal processed reference sensor data after noise removal processing is performed on the reference sensor data of the u-th group by one or more of the 1st signal processing unit to the (u-1)th signal processing unit, performing noise removal processing on the (u-1)th signal processed data after noise removal processing is performed on the signal sensor data by the (u-1)th signal processing unit, or using the reference sensor data of the u-th group or the signal processed reference sensor data after noise removal processing is performed on the u-th group by one or more of the 1st signal processing unit to the (u-1)th signal processing unit. the reference sensor data subjected to noise removal processing, the (u-1)th signal processed data subjected to noise removal processing by the (u-1)th signal processing unit, and the reference sensor data of one or more groups other than the first group to the uth group, or the reference sensor data subjected to noise removal processing by one or more of the 1st signal processing unit to the (u-1)th signal processing unit, wherein u is an integer greater than 2 and less than (Q-1), The Q-time signal processing unit executes: using the reference sensor data of the Q-th group or the signal processed reference sensor data after noise removal processing is performed on the reference sensor data of the Q-th group by one or more of the 1-time signal processing unit to the (Q-1)-time signal processing unit, performing noise removal processing on the (Q-1)-time signal processed data after noise removal processing is performed on the signal sensor data by the (Q-1)-time signal processing unit.

2. The measuring device according to claim 1, wherein: Q is 2, The primary signal processing unit performs noise removal processing on the signal sensor data using the reference sensor data of the first group. The secondary signal processing unit performs noise removal processing on the primary signal processed data after the noise removal processing is performed on the signal sensor data by the primary signal processing unit, using the reference sensor data of a second group other than the first group.

3. The measuring device according to claim 1, wherein: Q is 2, The primary signal processing unit uses the reference sensor data of the first group to perform noise removal processing on both the signal sensor data and the reference sensor data of a second group other than the first group. The secondary signal processing unit performs noise removal on the primary signal processed data after the primary signal processing unit has performed noise removal on the signal sensor data, using the signal processed reference sensor data after the primary signal processing unit has performed noise removal on the reference sensor data of the second group.

4. The measuring device according to any one of claims 1 to 3, wherein: The reference sensor unit includes two or more sensors of the same type as the signal sensor and a sensor of a different type from the signal sensor as the reference sensor.

5. The measuring device according to any one of claims 1 to 3, wherein: The K-th signal processing unit uses ANC as a noise removal processing method, where K is an integer greater than or equal to 1 and less than or equal to Q.

6. The measuring device according to claim 5, wherein: The U-order signal processing unit uses frequency division ANC as a noise removal processing method, where U is an integer greater than or equal to 1 and less than or equal to Q and other than K.

7. The measuring device according to any one of claims 1 to 3, wherein: The signal sensor section includes a magnetic sensor as the signal sensor.

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