Measurement device

Abstract

To provide a measurement device capable of increasing the accuracy of canceling noise from a result of measuring a desired signal using a plurality of reference sensors.SOLUTION: A measurement device comprises: a signal sensor section having one or more signal sensors for measuring a measurement signal in which a target signal and noise are mixed; a reference sensor section having two or more reference sensors for measuring a reference signal including the noise; and a processing section. The processing section includes: a reference sensor data grouping section that 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 section, into two groups; a primary signal processing section that performs noise cancellation processing for signal sensor data using reference sensor data of a first group; and a secondary signal processing section that performs noise cancellation processing for primary signal processed data after the noise cancellation processing of the primary signal processing section for the signal sensor data using reference sensor data of a second group.SELECTED DRAWING: Figure 3

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 a desired signal. Generally, as a method for removing environmental noise, a reference sensor is prepared separately from a signal sensor for measuring a desired signal, and a filtering process is performed on the data acquired by the reference sensor in time series. When there are many types of environmental noise, it is necessary to increase the number of reference sensors accordingly.

[0003] In the measuring device described in Patent Document 1, a plurality of measuring sensor units are provided at a measuring position for measuring a measurement object and detecting an input magnetic field in at least one detection axis direction, and a reference position separated from the measuring position is provided. A plurality of reference sensor units for detecting an input magnetic field in three-axis directions, and a correction unit for correcting a measurement signal corresponding to the output of the plurality of measurement sensor units using a reference signal indicating the output of the plurality of reference sensor units (see Patent Document 1).

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, in the conventional technology, when a plurality of reference sensors are used, the accuracy of removing noise from the measurement result of a desired signal may be insufficient.

[0006] The present disclosure has been made in consideration of such circumstances, and an object thereof is to provide a measuring device that can improve the accuracy of removing noise from measurement results of a desired signal by using a plurality of reference sensors.

Means for Solving the Problems

[0007] One aspect includes a signal sensor unit having one or more signal sensors that measure a measurement signal in which a target signal and noise are mixed, 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 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 Q groups, namely, a first group to a Qth group. Assuming that k is an integer from 1 to Q, for each k from 1 to Q, it includes a kth signal processing unit. The first signal processing unit performs noise removal processing on signal sensor data, which is data of the measurement signal measured by the signal sensor, using the reference sensor data of the first group, or performs noise removal processing on the signal sensor data, which is 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. Assuming that u is an integer from 2 to (Q - 1), the uth signal processing unit performs noise removal processing on the (u - 1)th signal processing data after noise removal processing by the (u - 1)th signal processing unit for the signal sensor data, using the reference sensor data of the uth group, or the reference sensor data of the uth group after noise removal processing by one or more of the first signal processing unit to the (u - 1)th signal processing unit, or performs noise removal processing on the (u - 1)th signal processing data after noise removal processing by the (u - 1)th signal processing unit for the signal sensor data, and the reference sensor data of one or more groups other than the first group to the uth group or the reference sensor data of the reference sensor data after noise removal processing by one or more of the first signal processing unit to the (u - 1)th signal processing unit. The Qth signal processing unit uses the reference sensor data of the Qth group, orA measuring device that performs noise removal processing on the (Q - 1) - th signal - processed data after noise removal processing by the (Q - 1) - th signal processing unit on the signal sensor data, using the signal - processed reference sensor data after noise removal processing by one or more of the first - order signal processing unit to the (Q - 1) - th signal processing unit for the reference sensor data of the Q - th group.

Advantages of the Invention

[0008] According to the present disclosure, in a measuring device, by using a plurality of reference sensors, the accuracy of removing noise from the measurement result of a desired signal can be increased.

Brief Description of the Drawings

[0009]

Figure 1

Figure 2

Figure 3

Figure 4

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Figure 8

Figure 9

Modes for Carrying Out the Invention

[0010] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

[0011] Measuring device FIG. 1 is a diagram showing a schematic configuration of a measuring device 1 including an information processing device 21 according to an embodiment. In FIG. 1, for convenience 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 L signal sensors A1 to AL, a plurality of M reference sensors B1 to BM, and an information processing device 21. In the example of FIG. 1, the region 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. Note that the number of the signal sensors A1 to AL may 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 may be 1. Also, the number of the reference sensors B1 to BM may be any number of 2 or more. In FIG. 1, a measurement target 31 is also shown. Note that the measuring device may be called, for example, a measurement system or the like. Also, the measurement may be called, for example, measurement or detection or the like.

[0013] When there are a plurality of signal sensors A1 to AL, for example, a device (for example, a signal sensor unit) integrally including these plurality of signal sensors A1 to AL may be configured. Also, when there are a plurality of reference sensors B1 to BM, for example, a device (for example, a reference sensor unit) integrally including these plurality of reference sensors B1 to BM may be configured. Also, a device (for example, a sensor unit) integrally including both the signal sensors A1 to AL and the reference sensors B1 to BM may be configured.

[0014] <Arrangement of signal sensors> In the example of FIG. 1, the signal sensor arrangement unit 11 is a square planar region parallel to the XY plane. In the example of FIG. 1, inside the signal sensor arrangement 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 that direction (a direction parallel to the Y-axis). Specifically, four signal sensors A1 to A4 are arranged at equal intervals in a direction parallel to the Y-axis, four signal sensors A5 to A8 are arranged at equal intervals in a direction parallel to the Y-axis, four signal sensors A9 to A12 are arranged at equal intervals in a direction parallel to the Y-axis, and four signal sensors A13 to A16 are arranged at equal intervals in a direction parallel to the Y-axis. Also, four signal sensors A1, A5, A9, A13 are arranged at equal intervals in a direction parallel to the X-axis, four signal sensors A2, A6, A10, A14 are arranged at equal intervals in a direction parallel to the X-axis, four signal sensors A3, A7, A11, A15 are arranged at equal intervals in a direction parallel to the X-axis, and four signal sensors A4, A8, A12, A16 are arranged at equal intervals in a direction parallel to the X-axis. In the example of FIG. 1, 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 interval. In the example of FIG. 1, a plurality of signal sensors A1 to AL are arranged in an array.

[0015] Here, the mode of the arrangement (overall arrangement) of the signal sensors A1 to AL is not particularly limited, and other modes may be used. Also, the arrangement (for example, position, direction) of each of the signal sensors A1 to AL is not particularly limited, and various modes may be used. For example, in the example of FIG. 1, the directions of the respective signal sensors A1 to AL are shown to be the same, but the directions of the respective signal sensors A1 to AL may be arbitrary. Note that the name "signal sensor" is a name for explanation, and it may be called by other names such as a measurement sensor or simply a sensor.

[0016] <Arrangement of the object to be measured> In the example of FIG. 1, when projected onto the XY plane ignoring the displacement in the position parallel to the Z-axis, the measurement object 31 is arranged at the center (or in the vicinity thereof) of the signal sensor arrangement unit 11. Here, the mode of the arrangement relationship between the measurement target 31 and the signal sensors A1 to AL is not particularly limited, and other modes may be used.

[0017] <Measurement target> The measurement target 31 is not particularly limited and may be various things. As an example, the measurement target 31 may be a coil that generates magnetism. In this case, when measuring the magnetism generated from the coil, it is possible to suppress the influence of ambient noise by the information processing device 21. As another example, as the measurement target 31, a sound source that emits sound (a sound signal) may be used.

[0018] <Target signal> Each of the signal sensors A1 to AL measures (detects) a desired signal (also referred to as a target signal for convenience of explanation) caused by the measurement target 31. At this time, noise is superimposed on the target signal. For this reason, each of the signal sensors A1 to AL measures (detects) a signal in which the target signal and the noise are mixed (also referred to as a mixed signal for convenience of explanation). And each of the signal sensors A1 to AL obtains its measurement result (also referred to as a signal measurement result for convenience of explanation). As the target signal, a signal of any physical quantity may be used. For example, signals such as magnetism, current, voltage, sound, and light may be used. Note that the target signal may be referred to as, for example, an interested signal or a desired signal.

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

[0020] In the example of FIG. 1, four reference sensors B2 to B5 parallel to the Y-axis are respectively arranged at the same position on the Y-axis as each of 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 position on the Y-axis as each of the four signal sensors A4, A3, A2, and A1 parallel to the Y-axis. In addition, in the example of FIG. 1, four reference sensors B7 to B10 parallel to the X-axis are respectively arranged at the same position on the X-axis as each of 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 position on the X-axis as each of the four signal sensors A13, A9, A5, and A1 parallel to the X-axis.

[0021] Here, the mode of the arrangement (overall arrangement) of the plurality of reference sensors B1 to BM is not particularly limited, and other modes may be used. In addition, the arrangement (for example, position, direction) of each reference sensor B1 to BM is not particularly limited, and various modes may be used. For example, in the example of FIG. 1, the directions of the respective reference sensors B1 to BM are shown to be the same, but the directions of the respective reference sensors B1 to BM may be arbitrary. Note that the name "reference sensor" is a name for explanation, and it may be called by other names such as, for example, a noise sensor or (simply) a sensor.

[0022] <Reference signal (signal including noise)> Each of the reference sensors B1 to BM measures (detects) the noise (noise signal) superimposed on the target signal as a reference signal. Then, each of the reference sensors B1 to BM obtains its measurement result (also referred to as a noise measurement result for convenience of explanation). In this embodiment, although the case where each of the reference sensors B1 to BM measures noise without measuring the components of the target signal will be described, as another example, a configuration in which the components of the target signal are included in the results of the noise measurement by each of the reference sensors B1 to BM may be used as long as there is no practical problem.

[0023] Here, the noise is, for example, noise caused by a signal (interference signal) other than the target signal, and may be called environmental noise, for example. The environmental noise may include, for example, a plurality of different types of noise emitted from a plurality of different noise sources. As a specific example, when the target signal is a magnetic signal, the noise may be a magnetic signal caused by an object other than the measurement target (for example, a train, etc.). Also, as a specific example, when the target signal is an electromagnetic wave, the noise may be an electromagnetic wave emitted from another circuit or the like near the measurement target 31.

[0024] <Types of signal sensors and types of reference sensors> When a plurality of signal sensors A1 to AL are used, for example, all of them may be the same type of sensor (a sensor that measures the same physical quantity), or different types of sensors (sensors that measure different physical quantities) may be included. When a plurality of reference sensors B1 to BM are used, for example, all of them may be the same type of sensor (a sensor that measures the same physical quantity), or different types of sensors (sensors that measure different physical quantities) may be included.

[0025] Further, as each of the reference sensors B1 to BM, for example, sensors of the same type as any of the signal sensors A1 to AL may be used, or sensors of a type different from the signal sensors A1 to AL may be used. As a specific example, when magnetic sensors are used as the signal sensors A1 to AL, magnetic sensors may be used as the reference sensors B1 to BM, or a combination of a magnetic sensor and an acceleration sensor may be used as the reference sensors B1 to BM. Note that, for example, even if sensors measure the same physical quantity, if the products are different and their ratings are different, they may be regarded as different types of sensors.

[0026] Further, there are no particular limitations on the orientation of the arrangement of each of the signal sensors A1 to AL and the orientation of the arrangement of each of the reference sensors B1 to BM. For example, the orientation of the arrangement of one or more of the signal sensors A1 to AL and the orientation of the arrangement of one or more of the reference sensors B1 to BM may be the same, or the orientation of the arrangement of the signal sensors A1 to AL and the orientation of the arrangement of the reference sensors B1 to BM may be different.

[0027] <Connection between the information processing device and each sensor> In the example of FIG. 1, each of the signal sensors A1 to AL and the information processing device 21 are communicably connected by wire or wirelessly. And the information processing device 21 can acquire the measurement results (detection results) by each of the signal sensors A1 to AL. Also, each of the reference sensors B1 to BM and the information processing device 21 are communicably connected by wire or wirelessly. And the information processing device 21 can acquire the measurement results (detection results) by each of the reference sensors B1 to BM. Note that in the example of FIG. 1, details of the connection between the information processing device 21 and each sensor (signal sensors A1 to AL, reference sensors B1 to BM) are not shown.

[0028] Here, in the present embodiment, the information processing apparatus 21 is shown in the case of acquiring the measurement results by each sensor by communicating with each sensor (signal sensors A1 to AL, reference sensors B1 to BM). As another example, after the measurement results by each sensor are once stored in a portable storage medium, the information processing apparatus 21 may acquire the measurement results by the measurement results being output from the storage medium to the information processing apparatus 21.

[0029] <Information processing apparatus> FIG. 2 is a diagram showing a schematic configuration of the information processing apparatus 21 according to the embodiment. The information processing apparatus 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 group dividing unit 161 and first to Q-th signal processing units P1 to PQ which are Q (Q is an integer of 2 or more) signal processing units. Note that the processing unit 151 may be called, for example, an arithmetic unit or the like.

[0030] The input unit 111 performs an 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 input the signal stored in a portable storage device from the storage device. Further, the input unit 111 may have, for example, an operation unit operated by a user, and may input information according 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 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 and convert the signal from an analog signal to a digital signal. Also, when the information processing apparatus 21 is applied to real-time processing, the acquisition unit 131 acquires signals in real time. Note that even when the information processing apparatus 21 is not applied to real-time processing, the acquisition unit 131 may acquire signals in real time.

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

[0033] The output unit 112 performs output to the outside. The display unit 141 displays and outputs information regarding the signal processing result. The display unit 141 has a screen such as a liquid crystal display (LCD), and displays and outputs information regarding the signal processing result on the screen. As another configuration example, the display unit 141 may print and output information regarding the signal processing result on paper. Note that the output unit 112 may have a function of performing output in other modes, such as voice output.

[0034] The storage unit 113 has a storage device such as a memory, for example, and stores information. The storage unit 113 stores information such as the input signal and the processing result of the signal, for example. Also, 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 this embodiment, the control unit 114 has a processor such as a CPU (Central Processing Unit), and the processor executes a control program stored in the storage unit 113 to perform various processes and various controls. Note that the processor includes an arithmetic unit that performs various arithmetic operations.

[0036] The processing unit 151 performs predetermined processing based on the signals of the measurement results by the signal sensors A1 to AL and the signals of the measurement results by the reference sensors B1 to BM. In this embodiment, the predetermined processing is processing for 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 may be used. That is, an aspect of completely removing the noise component from the mixed signal may be used, or an aspect of removing the noise component from the mixed signal to a necessary degree may be used. Note that the removal of the noise may be referred to as, for example, noise reduction or noise suppression.

[0037] The reference sensor data grouping unit 161 divides the data of the measurement results (reference sensor data) by each of the plurality of reference sensors B1 to BM into two or more predetermined numbers (Q) of groups. In this embodiment, the plurality of reference sensors B1 to BM are divided into Q groups in advance. Then, the reference sensor data by 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 this embodiment, the fact that the plurality of reference sensors B1 to BM are divided into Q groups and the fact that the plurality of reference sensor data obtained by the plurality of reference sensors B1 to BM are divided into Q groups represent the same thing.

[0038] Here, the mode of dividing the plurality of reference sensors B1 to BM into two or more groups (that is, the mode of dividing the plurality of reference sensor data into two or more groups) is not particularly limited. For example, a mode of dividing based on the order of arrangement of the reference sensors B1 to BM, a mode of dividing based on the arrangement location of the reference sensors B1 to BM, a mode of dividing based on the arrangement orientation of the reference sensors B1 to BM, or a mode of dividing based on the type of the reference sensors B1 to BM may be used.

[0039] As a specific example, as a mode of dividing into two groups based on the order of arrangement of the reference sensors B1 to BM, for a plurality of reference sensors arranged along a predetermined direction, a group of reference sensors at odd positions such as the first, third, and fifth, and a group of reference sensors at even positions such as the second, fourth, and sixth may be used (that is, a mode of dividing alternately according to the order of arrangement). Similarly, a mode of dividing into three or more groups based on the order of arrangement of the reference sensors B1 to BM may be used.

[0040] As a specific example, as a mode of dividing into two groups based on the arrangement location of the reference sensors B1 to BM, the region where the reference sensors B1 to BM are distributed is divided into two regions, and the reference sensors arranged in each region are regarded as reference sensors in the same group (that is, a mode of dividing according to the location where the reference sensors are arranged) may be used. Similarly, a mode of dividing into three or more groups based on the arrangement location of the reference sensors B1 to BM may be used.

[0041] As a specific example, as a mode of dividing into two groups based on the arrangement orientation of the reference sensors B1 to BM, the arrangement orientations of the reference sensors B1 to BM are divided into two types of groups, and each reference sensor is divided into the group to which the arrangement orientation belongs (for example, a mode of dividing according to the arrangement orientation of the reference sensors) may be used. Similarly, a mode of dividing into three or more groups based on the arrangement orientation of the reference sensors B1 to BM may be used.

[0042] As a specific example, as an aspect of dividing into two groups based on the types of the reference sensors B1 to BM, an aspect of dividing into two groups according to the types of the reference sensors B1 to BM (for example, an aspect of dividing for each type of reference sensor) may be used. Similarly, an aspect of dividing into three or more groups based on the types of the reference sensors B1 to BM may be used.

[0043] Here, in the present embodiment, each group includes one or more reference sensor data (one or more reference sensors). Further, in the present embodiment, grouping is performed so 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, an aspect (way of grouping) of grouping a plurality of reference sensor data (a plurality of reference sensors) is stored in advance in the information processing apparatus 21 (for example, the storage unit 113 or the like). Then, the reference sensor data grouping unit 161 performs grouping of the reference sensor data based on the aspect. In this case, the correspondence between each reference sensor data (each reference sensor) and each group is set in advance. As a specific example, when the measuring device 1 is shipped as a product, the aspect of grouping (the correspondence) may be set in the information processing apparatus 21 before shipment, and the aspect of grouping (the correspondence) may be fixed after shipment. Further, for example, if necessary, the aspect of grouping (the correspondence) set in the information processing apparatus 21 may be adjusted (changed) by human judgment before shipment.

[0045] The primary signal processing units P1 to the Q-th signal processing units PQ perform a process of removing noise included in the measurement result using the data of the measurement results by the signal sensors A1 to AL and the reference sensor data divided into Q groups. The method of this process in each order signal processing unit is not particularly limited, and for example, a method of adaptive noise canceling (ANC) which is a method of removing noise may be used. The display control unit 152 outputs various types of information for display on 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 will be referred to as environmental noise for explanation. Referring to FIGS. 3 to 6, a specific example of the environmental noise removal processing performed by the information processing apparatus 21 is shown. In the present embodiment, for the sake of convenience of explanation, the part integrating the L signal sensors A1 to AL will be referred to as the signal sensor unit 331 for explanation, the part integrating the M reference sensors B1 to BM will be referred to as the reference sensor unit 332 for explanation, and the part integrating the signal sensor unit 331 and the reference sensor unit 332 will be 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, the signal sensors A1 to AL and the reference sensors B1 to BM do not have a common sensor for both.

[0047] [Specific Example of Processing According to the First Configuration Example] FIG. 3 is a diagram schematically showing the flow of processing according to the first configuration example of the embodiment. FIG. 3 shows the sensor unit 311 including the signal sensor unit 331 and the reference sensor unit 332, and the processing unit 151a. Here, the processing unit 151a is an example of the processing unit 151 shown in FIG. 2.

[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 an example in which two-stage signal processing is performed. Here, the reference sensor data grouping unit 161a, the primary signal processing unit P1a, and the secondary signal processing unit P2a are examples of the reference sensor data grouping unit 161, the primary signal processing unit P1, and the secondary signal processing unit P2 in FIG. 2, respectively.

[0049] A specific example of the processing in this example will be described. When the measurement signal a1 (in this example, a mixed signal) is measured by the signal sensor unit 331, signal sensor data a11, which is the data of the measurement signal a1, is obtained. The signal sensor data a11 includes the signal sensor data from the respective signal sensors A1 to AL. Here, in this example, the measurement signal a1 includes the target signal and environmental noise. Also, when the environmental noise signal a2 (an example of a reference signal, and the same applies hereinafter) is measured by the reference sensor unit 332, reference sensor data a12, which is the data of the environmental noise signal a2, is obtained. The reference sensor data a12 includes the reference sensor data from the respective reference sensors B1 to BM.

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

[0051] The primary signal processing unit P1a performs a predetermined process using the signal sensor data a11 and the first group data g1, and generates primary signal processed data a21, which is the data of the result of the process. Here, the process is a process of removing the environmental noise included in the signal sensor data a11 using the first group data g1.

[0052] The secondary signal processing unit P2a performs a predetermined process using the primary signal processed data a21 and the second group data g2, and generates secondary signal processed data a31, which is the data of the result of the process. Here, the process is a process of removing the environmental noise included in the primary signal processed data a21 using the second group data g2. Thus, 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 data with environmental noise removed.

[0053] In this example, for the signal sensor data, the environmental noise removal process is performed for each order of signal processing using one group of reference sensor data respectively. On the other hand, in this example, for the reference sensor data, the initial data (in the example of FIG. 3, the reference sensor data a12) is used for the environmental noise removal process.

[0054] <Specific Example of the Process According to the Second Configuration Example> FIG. 4 is a diagram schematically showing the flow of the process according to the second configuration example of the embodiment. FIG. 4 shows a sensor unit 311 including a signal sensor unit 331 and a reference sensor unit 332, and a processing unit 151b. Here, the processing unit 151b is an example of the processing unit 151 shown in FIG. 2.

[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 when two-stage signal processing is performed. Here, the primary signal processing unit P1b and the secondary signal processing unit P2b are examples of the primary signal processing unit P1 and the secondary signal processing unit P2 in FIG. 2 respectively.

[0056] A specific example of the process in this example will be described. In this example, the processing until the signal sensor data a11, the first group data g1, and the second group data g2 are obtained is the same as that of the first configuration example, and the subsequent processing is different from that of the first configuration example. Therefore, in the example of FIG. 4, the same reference numerals are given to the parts that are the same as those of the first configuration example.

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

[0058] The primary signal processing unit P1b performs predetermined processing using the signal sensor data a11 and the first group of data g1, and generates the data after primary signal processing a51, which is the result of the said processing. Here, the said processing is a process of removing the environmental noise included in the signal sensor data a11 using the first group of data g1. In addition, when the same processing is performed for the same input as the primary signal processing in the first configuration example, the data after primary signal processing a51 will be the same as the data after primary signal processing a21 shown in FIG. 3.

[0059] Moreover, the primary signal processing unit P1b performs predetermined processing using the second group of data g2 and the first group of data g1, and generates the data of the second group after primary signal processing g12, which is the result of the said processing. Here, the said processing is a process of removing the environmental noise included in the second group of data g2 using the first group of data g1.

[0060] The secondary signal processing unit P2b performs predetermined processing using the data after primary signal processing a51 and the data of the second group after primary signal processing g12, and generates the data after secondary signal processing a61, which is the result of the said processing. Here, the said processing is a process of removing the environmental noise included in the data after primary signal processing a51 using the data of the second group after primary signal processing g12. In this way, in this example, the environmental noise removal process is performed in two stages. The data after secondary signal processing a61 includes the extracted target signal and is the data with the environmental noise removed.

[0061] In this example, for the signal sensor data, the environmental noise removal process is performed for each order of signal processing using one group of reference sensor data respectively. Also, in this example, for the group of reference sensor data used in the secondary signal processing, the environmental noise removal process is performed using the reference sensor data of other groups.

[0062] <Specific Example of the Process According to the Third Configuration Example> FIG. 5 is a diagram schematically showing the flow of processing according to the third configuration example of the embodiment. FIG. 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 is an example of the processing unit 151 shown in FIG. 2.

[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 in which three-stage signal processing is performed. 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 examples 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 FIG. 2, respectively.

[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 configuration example, and the subsequent processing is different from that in the first configuration example. Therefore, in the example of FIG. 5, the same reference numerals are given to the parts that are the same as those in the first configuration example.

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

[0066] The primary signal processing unit P1c performs a predetermined process using the signal sensor data a11 and the first group of data g101, and generates the data after primary signal processing a101, which is the result of the process. Here, the process is a process of removing environmental noise included in the signal sensor data a11 using the first group of data g101.

[0067] The secondary signal processing unit P2c performs a predetermined process using the data after primary signal processing a101 and the second group of data g102, and generates the data after secondary signal processing a111, which is the result of the process. Here, the process is a process of removing environmental noise included in the data after primary signal processing a101 using the second group of data g102.

[0068] The tertiary signal processing unit P3c performs a predetermined process using the data after secondary signal processing a111 and the third group of data g103, and generates the data after tertiary signal processing a121, which is the result of the process. Here, the process is a process of removing environmental noise included in the data after secondary signal processing a111 using the third group of data g103. In this way, in this example, the environmental noise removal process is performed in three stages. The data after tertiary signal processing a121 includes the extracted target signal and becomes data from which environmental noise has been removed.

[0069] In this example, for the signal sensor data, the environmental noise removal process is performed for each stage of signal processing using one group of reference sensor data. On the other hand, in this example, for the reference sensor data, the initial data (in the example of FIG. 3, the reference sensor data a12) is used for the environmental noise removal process. Note that in this embodiment, a configuration example in which primary to tertiary signal processing units are used is shown. However, for example, a configuration in which signal processing units of four or more stages are used may be adopted. In this case, the same number of groups as the number of stages of the signal processing units is set.

[0070] <Specific Example of the Process According to the Fourth Configuration Example> FIG. 6 is a diagram schematically showing the flow of processing according to the fourth configuration example of the embodiment. FIG. 6 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 an example of the processing unit 151 shown in FIG. 2.

[0071] The processing unit 151d includes a reference sensor data grouping unit 161c, a primary signal processing unit P1d, a secondary signal processing unit P2d, and a tertiary signal processing unit P3d. This example is an example in which three-stage signal processing is performed. Here, the primary signal processing unit P1d, the secondary signal processing unit P2d, and the tertiary signal processing unit P3d are examples of the primary signal processing unit P1, the secondary signal processing unit P2, and the tertiary signal processing unit P3 in FIG. 2, respectively.

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

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

[0074] The primary signal processing unit P1d performs a predetermined process using the signal sensor data a11 and the first group data g101, and generates primary signal processed data a201 which is the data of the result of the process. Here, the process is a process of removing environmental noise included in the signal sensor data a11 using the first group data g101. Note that when the same process as the primary signal processing in the third configuration example is performed for the same input, the primary signal processed data a201 becomes the same as the primary signal processed data a101 shown in FIG. 5.

[0075] Also, the primary signal processing unit P1d performs a predetermined process using the second group data g102 and the first group data g101, and generates the second group data g112 after primary signal processing, which is the data of the result of the process. Here, the process is a process of removing environmental noise included in the second group data g102 using the first group data g101.

[0076] Also, the primary signal processing unit P1d performs a predetermined process using the third group data g103 and the first group data g101, and generates the third group data g113 after primary signal processing, which is the data of the result of the process. Here, the process is a process of removing environmental noise included in the third group data g103 using the first group data g101.

[0077] The secondary signal processing unit P2d performs a predetermined process using the data a201 after primary signal processing and the second group data g112 after primary signal processing, and generates the data a211 after secondary signal processing, which is the data of the result of the process. Here, the process is a process of removing environmental noise included in the data a201 after primary signal processing using the second group data g112 after primary signal processing.

[0078] Also, the secondary signal processing unit P2d performs a predetermined process using the third group data g113 after primary signal processing and the second group data g112 after primary signal processing, and generates the third group data g123 after secondary signal processing, which is the data of the result of the process. Here, the process is a process of removing environmental noise included in the third group data g113 after primary signal processing using the second group data g112 after primary signal processing.

[0079] The tertiary signal processing unit P3d performs a predetermined process using the data a211 after secondary signal processing and the third group data g123 after secondary signal processing, and generates the data a221 after tertiary signal processing, which is the data of the result of the process. Here, the process is a process of removing environmental noise included in the data a211 after the secondary signal processing using the third group data g123 after the secondary signal processing. Thus, 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 data from which environmental noise has been removed.

[0080] In this example, for the signal sensor data, the environmental noise removal process is performed for each stage of signal processing using one group of reference sensor data respectively. Also, in this example, for the group of reference sensor data used in the signal processing after the secondary stage and later, the environmental noise removal process is performed using the reference sensor data of other groups. Note that in this embodiment, a configuration example using a three-stage signal processing unit of the primary to tertiary stages is shown. However, for example, a configuration using a signal processing unit of four stages or more may be adopted. In this case, the same number of groups as the number of stages of the signal processing unit is set.

[0081] Here, in this example, in each stage of the signal processing unit, a configuration is shown in which the environmental noise removal process using the reference sensor data of other groups is always performed on the group of reference sensor data output to the next stage of the signal processing unit. However, as another example, such an environmental noise removal process (that is, the environmental noise removal process for the reference sensor data of each group) may be performed only for a part.

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

[0083] Here, as the environmental noise removal method according to this embodiment, the method according to the first configuration example shown in FIG. 3 (a two-stage environmental noise removal method) is used. Specifically, in the environmental noise removal method according to 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), and in the primary signal processing unit P1, the reference sensor data of the first group is used to perform environmental noise removal processing on the signal sensor data by a predetermined frequency division ANC (a method described with reference to FIGS. 8 to 9 to be described later), and in the secondary signal processing unit P2, the reference sensor data of the second group is used to perform environmental noise removal processing on the data of the result of the primary signal processing by ANC.

[0084] Also, as the environmental noise removal method according to the comparative example, a one-stage environmental noise removal method is used. Specifically, in the environmental noise removal method according to the comparative example, environmental noise removal processing is performed on the signal sensor data by ANC using the reference sensor data of 18 reference sensors.

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

[0086] [Example of signal processing] As the signal processing performed by each of the signal processing units from the primary signal processing unit P1 to the PQ-th signal processing unit PQ, various signal processings may be used. Also, as the combination of multiple-stage signal processings by multiple-stage signal processing units, various combinations may be used. Note that in the multiple-stage signal processing unit, for example, a mode in which all signal processings have different processing procedures may be used, or a mode in which two or more signal processings have the same processing procedure may be used.

[0087] As an example, as the signal processing method, the ANC method may be used. As another example, as a signal processing method, a method using frequency-division ANC (referred to as frequency-division ANC in the present embodiment) may be used.

[0088] For example, in a configuration where primary signal processing and secondary signal processing are performed, the primary signal processing and the secondary signal processing may be ANC processing and ANC processing, may be ANC processing and frequency-division ANC processing, may be frequency-division ANC processing and ANC processing, or may be frequency-division ANC processing and frequency-division ANC processing. Also, even in a configuration where signal processing of three or more stages is performed, for example, ANC or frequency-division ANC may be arbitrarily assigned to each signal processing.

[0089] [Specific Example of Signal Processing of Frequency-Division ANC] With reference to FIGS. 8 to 9, an example of frequency-division ANC is shown. FIG. 8 is a diagram showing a configuration example of a signal processing unit P according to an embodiment. Here, the signal processing unit P may 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 a section division unit 171, a frequency domain division unit 172, and a coefficient calculation unit 173.

[0090] The section division unit 171 divides a time period into predetermined sections. Here, various modes may be used for the number of sections or the length (section length) of the sections. Note that the time length may be counted, for example, based on the number of samples of the signal data.

[0091] The frequency domain division unit 172 divides a time-series signal into signals for each frequency domain. The frequency domain division unit 172 may have, for example, a function of Fourier transform. The function may be a function of fast Fourier transform (FFT). 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 for frequency division ANC. The coefficient calculation unit 173 calculates a predetermined coefficient. In this example, the coefficient is a coefficient for noise removal.

[0092] In this example, for the sake of convenience of explanation, assuming that all reference sensors B1 to BM are used for frequency division ANC, the explanation will be given using mathematical formulas. However, when only some reference sensors are used as in this embodiment, the reference sensors in the following explanation are limited to some reference sensors (in this embodiment, a specific group of reference sensors).

[0093] In this example, the signal processing unit P performs analysis for each of a plurality of frequency regions for each of a plurality of time intervals. In this analysis, for example, the ANC method, which is a method for removing noise, may be used. Note that usually, the ANC method is executed for the entire time series without using a plurality of time intervals.

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

[0095] The signal data of the measurement results by the i-th (i = 1 to M) reference sensor Bi is x i (f). 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 a vector composed of M filter coefficients is represented by the vector w.

[0096]

Equation

[0097] Equation (1) is obtained by calculating Equation (2). 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 by the reference sensors B1 to BM.

[0098]

Equation

[0099] The calculation of the covariance matrix Σxx will be described. The measurement results of the plurality of reference sensors B1 to BM are represented by the vector x(f) shown in Equation (3).

[0100]

Equation

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

[0102]

Equation

[0103] Here, N represents the number of divisions (total number of intervals) when dividing the signal data into a plurality of intervals. There is a vector x(f) for each interval, and there are a total of N vectors x(f) for N intervals. To obtain a stable value, it is necessary to set N to a certain magnitude. N affects the calculation accuracy of the vector w of the filter coefficients. The inverse matrix Σxx of the covariance matrix Σxx -1 To calculate it stably, generally, N > 3M to about 10M is required.

[0104] In this example, for the stabilization of the calculation result by N, regularization represented by Equation (5) is performed. By regularization, for example, overestimation can be prevented. Here, γ represents the regularization parameter, and generally, it is set to 10 times the maximum eigenvalue of the covariance matrix ―2 to 10 -6 times. I represents the identity matrix.

[0105]

Number

[0106] <Interval Division> FIG. 9 is a diagram showing an example of interval division. In FIG. 9, an axis representing time (t) is shown as the horizontal axis. Also, FIG. 9 shows an example of the signal data 211 of the time-series signal. In the example of FIG. 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 FIG. 9, the entire length of the signal data 211 to be signal-processed is represented by the period T. Here, the signal data 211 is the data of the signal obtained from the measurement results by the signal sensors A1 to AL. The length (signal data length) of the signal data 211 corresponds to the period (length of time) of the measurement results. In this embodiment, the sampling period of the signal data 211 is a predetermined period (for example, a constant period). Also, the total number of intervals into which the period T is divided into a plurality is N.

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

[0108]

Equation

[0109] Thus, in this example, by dividing the signal with the total data length T into intervals with the interval length Tw and calculating N x(f)'s for the data included in each interval, the covariance matrix Σxx can be calculated, and from this, the noise removal result is calculated. For example, it is necessary to increase the number of reference sensors B1 to BM so as to cope with various environmental noises. However, with the method of this example, it is possible to set N within an optimal range by changing the interval length Tw of data division to cope with it.

[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 may be arbitrary. For example, a mode in which two adjacent intervals are in contact without overlapping may be used. Approximately, the following can be said. That is, when two adjacent intervals overlap by more than 1 / 2 of the interval length, the discrete data sampled in the time window does not contain components above the Nyquist frequency, 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, a case where all intervals have the same interval length is shown, but among a plurality of intervals, intervals with different interval lengths may be included.

[0111] As described above, in the frequency division ANC in this example, signal measurement results by one or more signal sensors that measure a mixed signal in which a target signal and noise are mixed, and noise measurement results by a plurality of reference sensors that measure the noise are obtained. Then, in this method, for each of the intervals that are temporally divided into a plurality, the obtained signal measurement results and each of the noise measurement results are divided into a plurality of frequency domains, and signal processing for removing the noise included in the mixed signal is performed. The signal processing unit uses, for example, intervals that have the same interval length and in which half of the interval length overlaps between two adjacent intervals as the intervals. The 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 by an operation using a covariance matrix, and a regularization parameter for adjusting 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 the interval.

[0112] [Summary of Embodiment] As described above, in the measuring device 1 according to this embodiment, by using a plurality of reference sensors B1 to BM, it is possible to improve the accuracy of removing noise from the measurement results of a desired signal. The measuring device 1 according to this embodiment includes a plurality of reference sensors B1 to BM for measuring noise, together with signal sensors A1 to AL for measuring a target signal, divides a plurality of reference sensor data obtained by the plurality of reference sensors B1 to BM into two or more groups, and uses these groups to perform the same or different signal processing by a signal processing unit of two or more stages. Therefore, by combining signal processing of two or more stages using a plurality of groups, the effect of removing noise included in the measurement results can be enhanced, and high-precision measurement can be achieved.

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

[0114] For example, in the measuring device 1, by using different noise removal methods in multiple stages of signal processing, such as using different noise removal methods in the primary signal processing and the secondary signal processing, it is possible to remove noises with different specific properties by each noise removal method. That is, in the measuring device 1, in the signal processing of each order, it is possible to perform noise removal processing corresponding to different types of noises. In this embodiment, it is possible to enhance the noise removal effect while maintaining the same number of reference sensors. Therefore, it is also possible to reduce the number of reference sensors while maintaining the noise removal effect.

[0115] Note that in Patent Document 1, a plurality of reference sensors are used to support three-axis detection. However, for example, there is no disclosure or suggestion regarding the use of a plurality of reference sensors for obtaining various noises, nor is there any disclosure or suggestion regarding the point of grouping the plurality of reference sensor data obtained by the plurality of reference sensors and performing signal processing repeatedly multiple times (in multiple stages).

[0116] As a configuration example (hereinafter, for convenience of explanation, referred to as configuration example α1), in the measurement device 1, regarding a measurement signal measured in a state where a target signal (signal of interest) and environmental noise are mixed, one or more signal sensors A1 to AL (measurement sensors) that acquire the measurement signal, two or more reference sensors B1 to BM that acquire environmental noise, a reference sensor data grouping unit 161 that groups the reference sensor data acquired by the reference sensors B1 to BM, and using one group of the grouped reference sensor data, a primary signal processing unit P1 that performs noise removal processing on the signal sensor data acquired by the signal sensors A1 to AL, and a secondary signal processing unit that performs noise removal processing on the primary signal processed data, which is the processing result of the primary signal processing unit P1, using another group. Therefore, in the measurement device 1, by grouping and using a plurality of reference sensor data, for example, more effective signal processing can be realized without increasing the number of reference sensors. Also, for example, it is possible to reduce the number of reference sensors without degrading the accuracy of signal processing.

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

[0118] As another configuration example (hereinafter referred to as configuration example β1 for convenience of explanation), in the measuring device 1, regarding a measurement signal measured in a state where a target signal (signal of interest) and environmental noise are mixed, one or more signal sensors A1 to AL (measurement sensors) for acquiring the measurement signal, two or more reference sensors B1 to BM for acquiring environmental noise, a reference sensor data grouping unit 161 for grouping the reference sensor data acquired by the reference sensors B1 to BM, and using one group of the grouped reference sensor data, a primary signal processing unit P1 that performs noise removal processing on the signal sensor data acquired by the signal sensors A1 to AL and also performs noise removal processing on the reference sensor data of another group, and a secondary signal processing unit that performs noise removal processing on the primary signal processed data, which is the processing result of the signal sensor data by the primary signal processing unit P1, using the processing result of the other group by the primary signal processing unit P1. Therefore, in the measuring device 1, by grouping and using a plurality of reference sensor data, for example, more effective signal processing can be realized without increasing the number of reference sensors. Also, for example, it is possible to reduce the number of reference sensors without degrading the accuracy of signal processing.

[0119] Here, in the measuring device 1, for example, it is provided with three or more reference sensors B1 to BM, performs three or more groupings, and in the first stage, 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 the reference sensor data of other groups. In the second stage and subsequent stages, except for the last stage, using the reference sensor data of one group among the remaining groups (in this example, the data after noise removal processing), performs noise removal processing on the reference sensor data of other groups (in this example, the data after noise removal processing), and at all stages from the second stage and subsequent, using the reference sensor data of the one group (in this example, the data after noise removal processing), performs noise removal processing on the data after signal processing at the previous stage (the noise removal processing result of the signal sensor data up to the previous stage). Such a configuration may be adopted. Thereby, in the measuring device 1, more effective signal processing can be further realized.

[0120] In the above-described configuration examples α1 and β1, the reference sensors B1 to BM may include two or more types of sensors among the same type of sensors as the signal sensors A1 to AL or different types of sensors from the signal sensors A1 to AL. Thereby, in the measuring device 1, environmental noise from noise sources (for example, two or more noise sources) that generate different noises such as magnetic noise and vibration noise can be removed.

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

[0122] In the above-described configuration examples α1 and β1, ANC may be used as a noise removal method in one or more stages of the signal processing unit, and frequency division ANC may be used as a noise removal method in one or more other stages of the signal processing unit. Thereby, 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 section using frequency division ANC, noise removal can be performed more effectively.

[0123] In the above-described configuration examples α1 and β1, the measuring device 1 may be applied, for example, when all of the signal sensors A1 to AL are magnetic sensors. Thereby, in the measuring device 1, noise superimposed on the output signals of the magnetic sensors (signal sensors A1 to AL) can be removed.

[0124] [Regarding the above embodiments] In addition, a program for realizing the functions of any component in any of the devices described above may be recorded on a computer-readable recording medium, and the program may be read into a computer system and executed. Here, the "computer system" shall include an operating system or hardware such as peripheral devices. Further, the "computer-readable recording medium" refers to a portable medium such as a flexible disk, a magneto-optical disk, a ROM, a CD (Compact Disc)-ROM (Read Only Memory), or a storage device such as a hard disk built into a computer system. Furthermore, the "computer-readable recording medium" also includes a volatile memory inside a computer system that serves as a server or a client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line, and holds the program for a certain period of time. The volatile memory may be, for example, a RAM (Random Access Memory). The recording medium may be, for example, a non-transitory recording medium.

[0125] Also, the above program may be transmitted from a computer system storing 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 such as the Internet or a communication line such as a telephone line. Also, the above program may be for realizing a part of the functions described above. Furthermore, the above program may be a so-called difference file that can be realized in combination with a program already recorded in a computer system for the functions described above. The difference file may be called a difference program.

[0126] Moreover, the functions of any component in any of the devices described above may be realized by a processor. For example, each process in the embodiment may be realized by a processor that operates based on information such as a program, and a computer-readable recording medium that stores information such as a program. Here, the processor may be configured such that, for example, the functions of each part are realized by individual hardware, or the functions of each part are realized by integrated hardware. For example, the processor includes hardware, and the hardware may include at least one of a circuit that processes digital signals and a circuit that processes analog signals. For example, the processor may be configured using one or more circuit devices mounted on a circuit board, or one or both of one or more circuit elements. As the circuit device, an IC (Integrated Circuit) or the like may be used, and as the circuit element, a resistor or a capacitor or the like may be used.

[0127] Here, the processor may be, for example, a CPU. However, the processor is not limited to a CPU, and various processors such as a GPU (Graphics Processing Unit) or a DSP (Digital Signal Processor) may be used. Further, the processor may be, for example, a hardware circuit formed by an ASIC (Application Specific Integrated Circuit). Further, the processor may be configured by, for example, a plurality of CPUs, or may be configured by a hardware circuit formed by a plurality of ASICs. Further, the processor may be configured by, for example, a combination of a plurality of CPUs and a hardware circuit formed by a plurality of ASICs. Further, the processor may include, for example, one or more of an amplifier circuit or a filter circuit that processes analog signals.

[0128] As described above in detail with reference to the drawings for the embodiments of this disclosure, the specific configuration is not limited to this embodiment, and designs and the like within the scope not departing from the gist of this disclosure are also included.

[0129] [Appendix] (Configuration Example 1) to (Configuration Example 7) are shown.

[0130] (Configuration Example 1) A signal sensor unit having one or more signal sensors that measure a measurement signal in which a target signal and noise are mixed, 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, A processing unit, and is provided with, The processing unit, A reference sensor data grouping unit that divides reference sensor data, which is data of Q or more reference signals measured by Q or more of the reference sensors of the reference sensor unit, into Q groups, namely, a first group to a Qth group, Assuming that k is an integer from 1 to Q, for each k from 1 to Q, it includes a k-th order signal processing unit, The first-order signal processing unit uses 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 uses 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. Assuming that u is an integer between 2 and (Q - 1), the u-th signal processing unit uses the reference sensor data of the u-th group or the signal-processed reference sensor data after noise removal processing by one or more of the first signal processing unit to the (u - 1)-th signal processing unit on the reference sensor data of the u-th group, and performs noise removal processing on the (u - 1)-th signal-processed data after noise removal processing by the (u - 1)-th signal processing unit on the signal sensor data, or uses the reference sensor data of the u-th group or the signal-processed reference sensor data after noise removal processing by one or more of the first signal processing unit to the (u - 1)-th signal processing unit on the reference sensor data of the u-th group, and performs noise removal processing on the (u - 1)-th signal-processed data after noise removal processing by the (u - 1)-th signal processing unit on the signal sensor data 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 after noise removal processing by one or more of the first signal processing unit to the (u - 1)-th signal processing unit on the reference sensor data. The Q-th signal processing unit uses the reference sensor data of the Q-th group or the signal-processed reference sensor data after noise removal processing by one or more of the first signal processing unit to the (Q - 1)-th signal processing unit on the reference sensor data of the Q-th group, and performs noise removal processing on the (Q - 1)-th signal-processed data after noise removal processing by the (Q - 1)-th signal processing unit on the signal sensor data. Measuring device.

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

[0132] Also, assuming that u is an integer from 2 to (Q - 1), the u-th order signal processing unit will be described. As an example, the u-th order signal processing unit uses the reference sensor data of the u-th group, or the signal-processed reference sensor data after noise removal processing by one or more of the primary signal processing unit to the (u - 1)-th order signal processing unit for the reference sensor data of the u-th group, and performs noise removal processing on the (u - 1)-th order signal-processed data after noise removal processing by the (u - 1)-th order signal processing unit for the signal sensor data. As another example, the u-th order signal processing unit uses the reference sensor data of the u-th group, or the signal-processed reference sensor data after noise removal processing by one or more of the primary signal processing unit to the (u - 1)-th order signal processing unit for the reference sensor data of the u-th group, and performs noise removal processing on the (u - 1)-th order signal-processed data after noise removal processing by the (u - 1)-th order signal processing unit for the signal sensor data, and on 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 after noise removal processing by one or more of the primary signal processing unit to the (u - 1)-th order signal processing unit for the reference sensor data of one or more groups other than the first group to the u-th group. That is, the u-th order signal processing unit, as an example, uses the reference sensor data of the u-th group, and as another example, uses the signal-processed reference sensor data after noise removal processing by one or more of the primary signal processing unit to the (u - 1)-th order signal processing unit for the reference sensor data of the u-th group. Further, 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 after noise removal processing of the signal sensor data by the (u - 1)-th signal processing unit and the predetermined data related to one or more groups other than the first group to the u-th group. Here, as an example, the predetermined data is the reference sensor data of one or more groups other than the first group to the u-th group. As another example, the predetermined data is the signal processed reference sensor data after noise removal processing by one or more of the first signal processing unit to the (u - 1)-th signal processing unit for the reference sensor data of one or more groups other than the first group to the u-th group. As yet another example, it may be a combination of these (a combination of reference sensor data related to one or more groups and signal processed reference sensor data related to one or more other groups) for two or more groups.

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

[0134] In this way, the first signal processing unit uses the reference sensor data of the first group to perform noise removal processing on the signal sensor data. Further, the first 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 Q-th group. The u-th signal processing unit (each of the 2nd to (Q - 1)-th signal processing units) performs noise removal processing on the measurement-related data from the signal processing unit of the previous order (i.e., the previous order) using the reference sensor-related data of the u-th group. The measurement-related data is data on which noise removal processing has been sequentially performed up to the signal processing unit of the previous order (i.e., the previous order) (data after (u - 1)-th signal processing after noise removal processing). The reference sensor-related data is data on which noise removal processing has not been performed at all up to the signal processing unit of the previous order (i.e., the previous order) (reference sensor data), or data on which noise removal processing has been performed one or more times up to the signal processing unit of the previous order (i.e., the previous order) (reference sensor data after signal processing). Also, the u-th signal processing unit (each of the 2nd to (Q - 1)-th signal processing units) may perform noise removal processing on the reference sensor-related data of one or more groups among the (u + 1)-th to Q-th groups using the reference sensor-related data of the u-th group. The Q-th signal processing unit performs noise removal processing on the measurement-related data from the (Q - 1)-th signal processing unit using the reference sensor-related data of the Q-th group.

[0135] (Configuration Example 2) Q is 2, The 1st signal processing unit performs noise removal processing on the signal sensor data using the reference sensor data of the 1st group, The 2nd signal processing unit performs noise removal processing on the data after 1st signal processing after noise removal processing of the signal sensor data by the 1st signal processing unit using the reference sensor data of the 2nd group other than the 1st group. The measuring device according to (Configuration Example 1).

[0136] (Configuration Example 3) Q is 2, The 1st signal processing unit performs noise removal processing on both the signal sensor data and the reference sensor data of the 2nd group other than the 1st group using the reference sensor data of the 1st group. The secondary signal processing unit performs noise removal processing on the first-stage signal-processed data after noise removal processing by the first-stage signal processing unit for the signal sensor data, using the post-signal-processing reference sensor data after noise removal processing by the first-stage signal processing unit for the reference sensor data of the second group. The measuring device according to (Configuration Example 1).

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

[0138] (Configuration Example 5) Assuming that K is an integer from 1 to Q, the K-th signal processing unit uses ANC as a noise removal processing method. The measuring device according to any one of (Configuration Example 1) to (Configuration Example 4).

[0139] (Configuration Example 6) Assuming that U is an integer from 1 to Q and different from K, the U-th signal processing unit uses frequency division ANC as a noise removal processing method. The measuring device according to (Configuration Example 5).

[0140] (Configuration Example 7) The signal sensor unit includes a magnetic sensor as the signal sensor. The measuring device according to any one of (Configuration Example 1) to (Configuration Example 6).

Explanation of Signs

[0141] 1…Measurement device, 11…Signal sensor arrangement section, 21…Information processing device, 31…Measurement target, 111…Input section, 112…Output section, 113…Memory section, 114…Control section, 131…Acquisition section, 141…Display section, 151, 151a, 151b, 151c, 151d…Processing sections, 152…Display control section, 161, 161a, 161c…Reference sensor data group division sections, 171…Interval division section, 172…Frequency domain division section, 173…Coefficient calculation section, 211…Signal data, 311…Sensor section, 331…Signal sensor section, 332…Reference sensor section, 1011, 1031…Characteristics, A1 to AL…Signal sensors, B1 to BM…Reference sensors, P(P1 to PQ)…Signal processing sections, P1, P1a, P1b, P1c, P1d…Primary signal processing sections, P2, P2a, P2b, P2c, P2d…Secondary signal processing sections, P3c, P3d…Tertiary signal processing sections, PQ…Qth signal processing section

Claims

1. A signal sensor unit having one or more signal sensors that measure a measurement signal in which a target signal and noise are mixed; 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; A processing unit; Comprising: The processing unit: 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 Q groups, namely, a first group to a Qth group; Assuming that k is an integer from 1 to Q, each k from 1 to Q includes a k-th signal processing unit, The first signal processing unit performs noise removal processing on signal sensor data, which is data of the measurement signal measured by the signal sensor, using the reference sensor data of the first group, or performs noise removal processing on the signal sensor data, which is 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; Assuming that u is an integer from 2 to (Q - 1), the u-th signal processing unit performs noise removal processing on the (u - 1)-th signal processed data after noise removal processing by the (u - 1)-th signal processing unit for the signal sensor data, using the reference sensor data of the u-th group, or the signal processed reference sensor data after noise removal processing by one or more of the first signal processing unit to the (u - 1)-th signal processing unit for the reference sensor data of the u-th group, or performs noise removal processing on the (u - 1)-th signal processed data after noise removal processing by the (u - 1)-th signal processing unit for the signal sensor data, 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 after noise removal processing by one or more of the first signal processing unit to the (u - 1)-th signal processing unit for the reference sensor data; The Q-th signal processing unit uses the reference sensor data of the Q-th group or the signal-processed reference sensor data after noise removal processing by one or more of the first-stage signal processing unit to the (Q-1)-th signal processing unit on the reference sensor data of the Q-th group, and performs noise removal processing on the (Q-1)-th signal-processed data after noise removal processing by the (Q-1)-th signal processing unit on the signal sensor data. Measuring device.

2. Q is 2, The first-stage signal processing unit uses the reference sensor data of the first group to perform noise removal processing on the signal sensor data. The second-stage 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 after noise removal processing by the first-stage signal processing unit on the signal sensor data. The measuring device according to claim 1.

3. Q is 2, The first-stage 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 second-stage signal processing unit uses the signal-processed reference sensor data after noise removal processing by the first-stage signal processing unit on the reference sensor data of the second group to perform noise removal processing on the first-stage signal-processed data after noise removal processing by the first-stage signal processing unit on the signal sensor data. The measuring device according to claim 1.

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

5. Assuming that K is an integer from 1 to Q, the K-th signal processing unit uses ANC as a noise removal processing method. The measuring device according to any one of claims 1 to 3.

6. Assuming that U is an integer from 1 to Q and different from K, the U-th signal processing unit uses frequency division ANC as a noise removal processing method. The measuring device according to claim 5.

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

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