Individual identification device and individual identification method

Abstract

An individual identification device (200) comprises: a measurement unit (220) that outputs measured individual data including a frequency and an amplitude value that indicate the results of measurement of an identification target by a sensor; a data extraction unit (230) that extracts, from the measured individual data and on the basis of the measured individual data, a frequency and an amplitude value in each of an extraction range including a fundamental frequency and an extraction range including a harmonic frequency, and that outputs the post-extraction measured individual data; a data comparison unit (260) that compares the measured individual data after extraction by the data extraction unit with registered individual data stored in advance; and an individual identification unit (270) that, on the basis of the results of comparison performed by the data comparison unit, identifies the identification target as an individual.

Description

Individual identification device and individual identification method

[0001] The disclosed technology relates to an individual identification technology for identifying an object.

[0002] Individual identification is a method for distinguishing objects such as individual objects and organisms. Patent Document 1 below discloses an "animal individual identification device". The "animal individual identification device" in Patent Document 1 focuses on the fact that there are individual differences in the walking habits of animals (paragraph 0011) and "attaches a three-dimensional sensor to an animal and measures the vibration during walking to identify the animal" (paragraph 0015). Specifically, the "animal individual identification device" "compares the frequency components of the walking vibration pattern, which shows the unique rhythm of the individual's walking pattern" (paragraph 0025) with the registered patterns pre-registered in the storage unit 6 to calculate the degree of difference, (paragraph 00) 27) "The degree of difference calculated by the degree-of-difference calculation unit 3 for each registered pattern is compared with a required threshold value. If the degree of difference is less than or equal to the threshold value, it is determined that it is acceptable and the attribute information of the horse is output. Also, if the degree of difference of all registered patterns is greater than the required value, the result of rejection is output as the identification result" (paragraph 0029).

[0003] Japanese Patent Application Laid-Open No. 2000-193520

[0004] However, the "animal individual identification device" described in Patent Document 1 has a problem that the accuracy of individual identification may be low depending on the identification target, such as when the identification target does not walk.

[0005] The present disclosure solves the above problems and aims to improve the accuracy of individual identification.

[0006] The individual identification device of this disclosure comprises: a measurement unit that outputs measured individual data including frequency and amplitude values ​​indicating the measurement results of an object to be identified by a sensor; a data extraction unit that extracts frequency and amplitude values ​​in an extraction range including the fundamental frequency and an extraction range including the harmonic frequency, respectively, from the measured individual data based on the measured individual data, and outputs the extracted measured individual data; a data comparison unit that compares the measured individual data extracted by the data extraction unit with pre-stored registered individual data; and an individual identification unit that individually identifies an object to be identified based on the comparison result by the data comparison unit.

[0007] According to this disclosure, it is possible to improve the accuracy of individual identification.

[0008] Figure 1 is a diagram showing an example configuration of an individual identification system including an individual identification device according to Embodiment 1 of this disclosure. Figure 2 is a flowchart showing an example of processing by the individual identification device according to Embodiment 1 of this disclosure. Figure 3 is a flowchart showing an example of data extraction processing in the individual identification device according to Embodiment 1 of this disclosure. Figure 4 is a measurement result of vibration to a motor when the vibration source to be identified is a motor, relating to Embodiment 2 of this disclosure. Figure 5 is a diagram showing a first configuration example of an individual identification system including an individual identification device according to Embodiment 3 of this disclosure. Figure 6 is a flowchart showing an example of processing by the individual identification device according to Embodiment 3 of this disclosure. Figure 7 is a diagram showing a second configuration example of an individual identification system including an individual identification device according to Embodiment 3 of this disclosure. Figure 8 is a diagram showing a first example of a hardware configuration for realizing the functions according to the configuration of this disclosure. Figure 9 is a diagram showing a second example of a hardware configuration for realizing the functions according to the configuration of this disclosure.

[0009] To further illustrate this disclosure, embodiments of this disclosure will be described below with reference to the accompanying drawings.

[0010] Embodiment 1. Embodiment 1 describes an example of the configuration of the basic form of the present disclosure.

[0011] An example of the configuration of an individual identification device according to Embodiment 1 of this disclosure will be described. Figure 1 is a diagram showing an example of the configuration of an individual identification system including an individual identification device according to Embodiment 1 of this disclosure. The individual identification system 1 (1A) individually identifies the object to be identified. The individual identification system 1 (1A) shown in Figure 1 is configured to include a sensor 100, an individual identification device 200 (200A), and an output destination device 400.

[0012] Sensor 100 performs measurements on the object to be identified and outputs a sensor signal. Sensor 100 is a sensor that performs measurements such that the characteristics of the object to be identified can be represented by frequency and amplitude values ​​for each frequency. Sensor 100 is, for example, a vibration sensor, an ultrasonic sensor, or a sensor composed of a pattern on a substrate. In this description, sensor 100 is shown as being located outside the individual identification device 200 (200A), but it may also be configured to be located inside the individual identification device 200 (200A).

[0013] The individual identification device 200 (200A) identifies whether an object to be identified is a registered object by comparing measured individual data, which includes measurement results for the object to be identified, with registered individual data that has been stored in advance. The measured individual data is individual data that includes frequencies and amplitude values ​​for each frequency that indicate the measurement results for the object to be identified. The registered individual data is individual data that includes frequencies and amplitude values ​​for each frequency that have been registered in advance for each individual. By registering, for example, the individual data of genuine products as registered individual data, the individual identification device 200 (200A) can perform identification such as determining the authenticity of the object to be identified.

[0014] The output destination device 400 is a device that receives and processes the individual identification result from the individual identification device 200 (200A). The output destination device 400 is, for example, an output device or input / output device such as a display or a terminal.

[0015] Next, the individual identification device 200 (200A) will be described in detail. The individual identification device 200 (200A) shown in Figure 1 is composed of a storage unit 210, a measurement unit 220, a data extraction unit 230, a data comparison unit 260, and an individual identification unit 270. In the individual identification device 200 (200A) shown in Figure 1, the measurement unit 220 is connected downstream of the sensor 100, the data extraction unit 230 is connected downstream of the measurement unit 220, the data comparison unit 260 is connected downstream of the data extraction unit 230, the individual identification unit 270 is connected downstream of the data comparison unit 260, the data extraction unit 230 is connected downstream of the storage unit 210, and the data comparison unit 260 is connected downstream of the data extraction unit 230.

[0016] The storage unit 210 stores the registered individual data that has been registered in advance. In this description, the storage unit 210 is shown to be located inside the individual identification device 200 (200A), but it may also be located outside the individual identification device 200 (200A). In this case, the individual identification device 200 (200A) can be configured to communicate with the storage unit 210 to realize this disclosure.

[0017] The measurement unit 220 outputs individual measurement data including frequency and amplitude values ​​that indicate the measurement results of the object to be identified by the sensor 100. Specifically, the measurement unit 220 receives the sensor signal output by the sensor 100 and, based on the sensor signal, outputs individual measurement data including the frequency and amplitude value for each frequency that indicates the measurement results of the object to be identified by the sensor 100.

[0018] The data extraction unit 230 extracts frequencies and amplitude values ​​of those frequencies within an extraction range from the measured individual data and outputs them as extracted measured individual data. The data extraction unit 230 also extracts frequencies and amplitude values ​​of those frequencies within an extraction range from pre-registered registered individual data and outputs them as extracted registered individual data. The extraction range is a range determined to include the fundamental frequency and the harmonic frequencies (harmonic frequencies). For example, the data extraction unit 230 determines the range of frequency bands centered on the fundamental frequency and the range of frequency bands centered on each harmonic frequency.

[0019] A detailed configuration example of the data extraction unit 230 will be described. The data extraction unit 230 shown in Figure 1 includes a storage data extraction unit 240 and a measurement data extraction unit 250.

[0020] The stored data extraction unit 240 in the data extraction unit 230 extracts the frequency and amplitude values ​​of the said frequency within the extraction range from the registered individual data that has been registered in advance, and outputs them as the extracted registered individual data. Specifically, the stored data extraction unit 240 extracts the frequency and amplitude values ​​in the extraction range that includes the fundamental frequency of vibration in the registered individual and the extraction range that includes the harmonic frequencies, respectively, from the registered individual data based on the registered individual data that has been registered in advance, and outputs the extracted registered individual data. The fundamental frequency can be said to be the vibration frequency caused by the vibration source in the individual. The extraction range that includes the fundamental frequency is calculated and determined so that it is, for example, a frequency bandwidth set in advance centered on the fundamental frequency. Note that if it is considered that there is little variation in the fundamental frequency of the identification target from individual to individual, the extraction range that includes the fundamental frequency may be determined to include only the fundamental frequency, or it may be calculated and determined so that it is a relatively narrow frequency bandwidth centered on the fundamental frequency. The frequency bandwidth of the extraction range at the fundamental frequency may be calculated and determined so that it is, for example, smaller than the frequency bandwidth of the extraction range at the harmonic frequencies. The extraction range including the harmonic frequencies is calculated, for example, centered around the harmonic frequencies. In this case, the extraction range including the harmonic frequencies is calculated and determined so that it is centered around the harmonic frequencies and, for example, a predetermined frequency bandwidth. That is, the memory data extraction unit 240 uses the registered individual data to determine the vibration frequency (fundamental frequency) of the vibration (vibration source) in the identification target and the harmonic frequencies, which are the harmonics of the vibration frequency (fundamental frequency). The memory data extraction unit 240 determines the extraction range based on the vibration frequency (fundamental frequency) and the harmonic frequencies. For example, multiple frequency bands centered on the vibration frequency (fundamental frequency) and the harmonic frequencies are determined as the extraction range. The memory data extraction unit 240 extracts the frequency and amplitude values ​​within the extraction range. The memory data extraction unit 240 outputs the extracted registered individual data.

[0021] The measurement data extraction unit 250 in the data extraction unit 230 extracts frequency and amplitude values ​​in the extraction range including the fundamental frequency and the extraction range including the harmonic frequency, respectively, from the measurement individual data and outputs the extracted measurement individual data. The measurement data extraction unit 250 in the data extraction unit 230 extracts frequency and amplitude values ​​of the said frequency in the extraction range including the fundamental frequency and the extraction range including the harmonic frequency, respectively, from the measurement individual data and outputs the extracted measurement individual data. The fundamental frequency can be said to be the vibration frequency caused by the vibration source in the individual as the object to be identified. The extraction range including the fundamental frequency is calculated and determined, for example, so that it becomes a frequency bandwidth set in advance centered on the fundamental frequency. Note that if it is considered that there is little variation in the fundamental frequency of the object to be identified from individual to individual, the extraction range including the fundamental frequency may be determined to include only the fundamental frequency, or it may be calculated and determined so that it becomes a relatively narrow frequency bandwidth centered on the fundamental frequency. The extraction range including the harmonic frequency is calculated, for example, centered on the harmonic frequency. In this case, the extraction range including harmonic frequencies is calculated and determined so that it is centered on the harmonic frequencies and, for example, a predetermined frequency bandwidth. That is, the measurement data extraction unit 250 uses the measured individual data to determine the vibration frequency (fundamental frequency) of the vibration (vibration source) in the identification target and the harmonic frequencies, which are the harmonics of the vibration frequency (fundamental frequency). The measurement data extraction unit 250 determines the extraction range based on the vibration frequency (fundamental frequency) and the harmonic frequencies, for example, by determining multiple frequency bands centered on the vibration frequency (fundamental frequency) and the harmonic frequencies, respectively, as the extraction range. The measurement data extraction unit 250 extracts the frequency and amplitude values ​​within the extraction range. The measurement data extraction unit 250 outputs the extracted measured individual data.

[0022] Furthermore, the stored data extraction unit 240 and the measurement data extraction unit 250 in the data extraction unit 230 can also be described as "first data extraction unit" and "second data extraction unit," respectively.

[0023] The data comparison unit 260 compares the measured individual data extracted by the data extraction unit 230 with the pre-stored registered individual data. The data comparison unit 260 compares the measured individual data extracted by the data extraction unit 230 with the registered individual data extracted by the data extraction unit 230. As a result of comparing the measured individual data extracted by the data extraction unit 230 with the registered individual data extracted by the data extraction unit 230, the data comparison unit 260 outputs the difference values ​​of frequency and amplitude for each extraction range. The registered individual data used in the data comparison unit 260 shown in Figure 1 is the registered individual data extracted by the data extraction unit 230.

[0024] Here, the data extraction unit 230 can also be configured without a stored data extraction unit 240. In this configuration, the data comparison unit 260 will compare the measured individual data extracted by the data extraction unit 230 with the entire registered individual data stored in advance. However, if only the portion of the entire registered individual data stored in advance that can be compared with the extracted measured individual data is used as the comparison result, the same effect as when the stored data extraction unit 240 is included can be obtained. This is also true in the embodiments described later.

[0025] The individual identification unit 270 identifies the target to be identified based on the comparison result from the data comparison unit 260. The individual identification unit 270 determines, for example, whether the difference value as a comparison result from the data comparison unit 260 is less than or equal to a pre-stored difference threshold. The individual identification unit 270 performs individual identification using the determination result. For example, the individual identification unit 270 determines that the individual is pre-registered if the difference value is less than or equal to the difference threshold, and determines that the individual is not pre-registered if the difference value exceeds the difference threshold. The individual identification unit 270 outputs the determination result to the output destination device 400.

[0026] The individual identification device 200, in addition to the above configuration, includes a control unit (not shown), an overall storage unit (not shown), and a communication unit (not shown). The control unit (not shown) controls the individual identification device 200 as a whole and each of its components. For example, the control unit (not shown) starts the individual identification device 200 according to an external command. The control unit (not shown) also controls the state of the individual identification device 200 (operating state = state such as start, shutdown, sleep). The control unit (not shown) also commands each component to start or end processing. The overall storage unit (not shown) stores the data used by the individual identification device 200. For example, the overall storage unit (not shown) stores the output (output data) from each component of the individual identification device 200 and outputs the requested data to the requesting component for each component. The communication unit (not shown) communicates with external devices. For example, it communicates between the individual identification device 200 (100A) and peripheral devices (for example, a sensor 100, an output destination device 400, or a server device (not shown)). For example, if the individual identification device 200 and the output destination device 400 are not connected by wire, the communication unit (not shown) has the function of communicating between the individual identification device 200 and the output destination device 400. The communication unit (not shown) also has the function of communicating with an external device, such as a server device. The control unit (not shown), the overall storage unit (not shown), and the communication unit (not shown) are the same in the embodiments described later.

[0027] An example of processing by an individual identification device according to Embodiment 1 of this disclosure will be described. Figure 2 is a flowchart showing an example of processing by an individual identification device according to Embodiment 1 of this disclosure. The processing shown in Figure 2 is an individual identification method by the individual identification device. For example, when the individual identification device 200 (200A) shown in Figure 1 receives a command from an external source or receives a sensor signal from the sensor 100, it starts the processing shown in Figure 2 ("start").

[0028] The individual identification device 200 (200A) then performs a measurement process (step ST1000). In the measurement process of step ST1000, the measurement unit 220 of the individual identification device 200 (200A) outputs measured individual data including frequency and amplitude values ​​that indicate the measurement result of the object to be identified by the sensor 100. Specifically, the measurement unit 220 receives the sensor signal output by the sensor 100 and, based on the sensor signal, outputs measured individual data including frequency and amplitude values ​​that indicate the measurement result of the object to be identified by the sensor 100. The measurement unit 220 outputs the measured individual data to the data extraction unit 230.

[0029] The individual identification device 200 (200A) then performs a data extraction process (step ST2000). In the data extraction process of step ST2000, the data extraction unit 230 of the individual identification device 200 (200A) extracts the frequency and amplitude values ​​in the extraction range including the fundamental frequency and the extraction range including the harmonic frequency, respectively, from the measured individual data and outputs the extracted measured individual data. Specifically, the measurement data extraction unit 250 of the data extraction unit 230 extracts the frequency and the amplitude values ​​of the said frequencies in the extraction range including the fundamental frequency and the extraction range including the harmonic frequency, respectively, of the vibration in the object to be identified from the measured individual data and outputs them as extracted measured individual data. The measurement data extraction unit 250 outputs the extracted measured individual data to the data comparison unit 260.

[0030] Furthermore, in the data extraction process of step ST2000, the data extraction unit 230 extracts the frequency and amplitude values ​​of the said frequency within the extraction range from the pre-registered registered individual data and outputs them as extracted registered individual data. Specifically, the storage data extraction unit 240 of the data extraction unit 230 acquires the pre-registered registered individual data from the storage unit 210, and based on the acquired registered individual data, extracts the frequency and amplitude values ​​in the extraction range including the fundamental frequency of vibration and the extraction range including the harmonic frequencies of the registered individual from the registered individual data and outputs the extracted registered individual data. The storage data extraction unit 240 outputs the extracted registered individual data to the data comparison unit 260.

[0031] Here, a detailed example of the data extraction process will be described. Figure 3 is a flowchart showing an example of the data extraction process in an individual identification device according to Embodiment 1 of this disclosure. When the data extraction unit 230 in the individual identification device 200 (200A) receives measured individual data from, for example, the measurement unit 220, it starts the process shown in Figure 3 ("start").

[0032] The data extraction unit 230 then performs a fundamental frequency determination process (step ST2110 "Determine fundamental frequency"). In the fundamental frequency determination process of step ST2110, the measurement data extraction unit 250 of the data extraction unit 230 uses the measurement individual data to determine the vibration frequency (fundamental frequency) of the vibration (vibration source) in the identification target. If data extraction processing is also performed on registered individual data, in the fundamental frequency determination process of step ST2110, the storage data extraction unit 240 of the data extraction unit 230 acquires the registered individual data from the storage unit 210. At this time, the storage data extraction unit 240 may acquire all of the registered individual data stored in the storage unit 210, or it may acquire registered individual data of a type that has been set in advance from the registered individual data stored in the storage unit 210, or it may acquire only the registered individual data that has been set in advance from the registered individual data stored in the storage unit 210. The storage data extraction unit 240 uses the acquired registered individual data to determine the vibration frequency (fundamental frequency) of the vibration (vibration source) in the identification target.

[0033] The data extraction unit 230 then performs a harmonic frequency determination process (step ST2120 "determine harmonic frequencies"). In the harmonic frequency determination process of step ST2120, the measurement data extraction unit 250 of the data extraction unit 230 uses the measurement individual data to determine the harmonic frequencies, which are the harmonics of the vibration frequency (fundamental frequency). If data extraction processing is also performed on registered individual data, in the harmonic frequency determination process of step ST2120, the storage data extraction unit 240 of the data extraction unit 230 uses the registered individual data acquired in the fundamental frequency determination process of step ST2110 to determine the harmonic frequencies, which are the harmonics of the vibration frequency (fundamental frequency).

[0034] The data extraction unit 230 then performs an extraction range determination process (step ST2130 "Determine Extraction Range"). In the extraction range determination process of step ST2130, the measurement data extraction unit 250 of the data extraction unit 230 determines the extraction range based on the vibration frequency (fundamental frequency) and harmonic frequencies. For example, the measurement data extraction unit 250 determines multiple frequency bands centered on the vibration frequency (fundamental frequency) and harmonic frequencies as the extraction range. Furthermore, when data extraction processing is also performed on registered individual data, in the extraction range determination process of step ST2130, the stored data extraction unit 240 determines the extraction range based on the vibration frequency (fundamental frequency) and harmonic frequencies. For example, it determines multiple frequency bands centered on the vibration frequency (fundamental frequency) and harmonic frequencies as the extraction range.

[0035] The data extraction unit 230 then performs an extraction process (step ST2140). In the extraction process of step ST2140, the measurement data extraction unit 250 of the data extraction unit 230 extracts amplitude values ​​for each frequency within the extraction range. The measurement data extraction unit 250 outputs the extracted measurement individual data to the data comparison unit 260. If data extraction is also performed on registered individual data, in the extraction process of step ST2140, the storage data extraction unit 240 of the data extraction unit 230 extracts frequency and amplitude values ​​within the extraction range. The storage data extraction unit 240 outputs the extracted registered individual data to the data comparison unit 260.

[0036] After the data extraction unit 230 outputs the extracted data to the data comparison unit 260, it then terminates the process shown in Figure 3 ("Termination").

[0037] Returning to the explanation of the process in Figure 2, the individual identification device 200 (200A) then performs a data comparison process (step ST3000). In the data comparison process of step ST3000, the data comparison unit 260 of the individual identification device 200 (200A) compares the measured individual data extracted by the data extraction unit 230 with the pre-stored registered individual data. The data comparison unit 260 compares the measured individual data extracted by the data extraction unit 230 with the registered individual data extracted by the data extraction unit 230. As a result of comparing the extracted measured individual data with the registered individual data extracted by the data extraction unit 230, the data comparison unit 260 calculates the difference values ​​of frequency and amplitude values ​​for each extraction range. The data comparison unit 260 outputs the calculated difference values ​​as a comparison result to the individual identification unit 270.

[0038] The individual identification device 200 (200A) then performs individual identification processing (step ST4000). In the individual identification processing of step ST4000, the individual identification unit 270 of the individual identification device 200 (200A) identifies the target to be identified based on the comparison result by the data comparison unit 260. The individual identification unit 270 determines, for example, whether the difference value as a comparison result by the data comparison unit 260 is less than or equal to a difference threshold value that has been stored in advance. The individual identification unit 270 performs individual identification using the determination result. The individual identification unit 270 determines, for example, that if the difference value is less than or equal to the difference threshold value, it is an individual that has been registered in advance, and determines that if the difference value exceeds the difference threshold value, it is not an individual that has been registered in advance. The individual identification unit 270 outputs the determination result to the output destination device 400.

[0039] The individual identification device 200 (200A) then terminates the process shown in Figure 2 ("Terminate"). If the individual identification device 200 (200A) continues to receive sensor data, it repeats the process from the beginning in Figure 2.

[0040] The above configuration makes it possible to prioritize the extraction of data in the range of large changes in frequency and amplitude values, thereby enabling highly accurate individual identification of objects. Furthermore, individual identification is utilized in a wide range of fields, including biology, medicine, manufacturing, and security, and this disclosure enables highly accurate individual identification of objects in each of these fields.

[0041] This embodiment describes a configuration including the following: (1) A device for identifying an individual, comprising: a measurement unit that outputs individual measurement data including frequency and amplitude values ​​indicating the measurement results of an object to be identified by a sensor; a data extraction unit that extracts frequency and amplitude values ​​in an extraction range including the fundamental frequency and an extraction range including the harmonic frequency, respectively, from the individual measurement data based on the individual measurement data and outputs the extracted individual measurement data; a data comparison unit that compares the extracted individual measurement data from the data extraction unit with pre-stored registered individual data; and an individual identification unit that identifies an object to be identified based on the comparison result from the data comparison unit. Thus, this disclosure has the effect of providing an individual identification device that enables improved accuracy of individual identification.

[0042] This embodiment describes a configuration including the following: (10) A method for individual identification using an individual identification device, wherein the measurement unit of the individual identification device outputs measured individual data including frequency and amplitude values ​​indicating the measurement results of an object to be identified by a sensor; the data extraction unit of the individual identification device extracts frequency and amplitude values ​​in an extraction range including the fundamental frequency and an extraction range including the harmonic frequency, respectively, from the measured individual data based on the measured individual data and outputs the extracted measured individual data; the data comparison unit of the individual identification device compares the measured individual data extracted by the data extraction unit with pre-stored registered individual data; and the individual identification unit of the individual identification device identifies an object to be identified based on the comparison result by the data comparison unit.

[0043] This embodiment further shows a form example including the following configuration. (2) The data extraction unit extracts the frequency and amplitude values in the extraction range including the fundamental frequency of vibration in the object to be identified and the extraction range including the harmonic frequencies from the measured individual data, and outputs the measured individual data after extraction. The individual identification device according to (1), wherein by this, the present disclosure can further provide an individual identification device capable of improving the accuracy of individual identification when characteristic vibrations unique to the object to be identified occur. Further, the present disclosure applies the above configuration to a system including an individual identification device or the above method, thereby achieving the same effect as the above effect.

[0044] This embodiment further shows a form example including the following configuration. (3) The extraction range including the harmonic frequencies is calculated centering on the frequencies of the harmonics. The individual identification device according to (1) or (2), wherein by this, the present disclosure can further provide an individual identification device capable of improving the accuracy of individual identification when the characteristic vibrations unique to the object to be identified are changing in the frequency direction. Further, the present disclosure applies the above configuration to a system including an individual identification device or the above method, thereby achieving the same effect as the above effect.

[0045] This embodiment further shows a form example including the following configuration. (4) The individual identification device according to any one of (1) to (3), comprising the sensor, wherein the sensor is a vibration sensor. By this, the present disclosure can further provide an individual identification device capable of improving the accuracy of individual identification when the object to be identified has a vibration source such as a motor. Further, the present disclosure applies the above configuration to a system including an individual identification device or the above method, thereby achieving the same effect as the above effect.

[0046] This embodiment further shows an example configuration including the following: (6) The data extraction unit further extracts frequency and amplitude values ​​in an extraction range including the fundamental frequency and an extraction range including the harmonic frequency, respectively, from the registered individual data based on the registered individual data which has been registered in advance, and outputs the extracted registered individual data; and the data comparison unit compares the measured individual data extracted by the data extraction unit with the registered individual data extracted by the data extraction unit; thus, the individual identification device according to any one of (1) to (5).

[0047] This embodiment further shows an example configuration including the following: (7) The individual identification device according to any one of (1) to (6), characterized by comprising a storage unit in which the registered individual data is stored. This further provides the individual identification device that can hold pre-registered individual data and can acquire registered individual data without communication. Furthermore, this disclosure can achieve the same effect as above by applying the above configuration to a system including an individual identification device or to the above method.

[0048] Embodiment 2. In the above-described Embodiment 1, a configuration example of the basic form of the present disclosure was described. Here, consider the case where individual identification of a drone is performed using frequency and amplitude values. When evaluating the vibration characteristics of a drone with the horizontal axis representing frequency and the vertical axis representing the amplitude value of vibration, the amplitude value of vibration may not vary for each individual at a certain frequency. That is, by sensing the frequency range where individual differences in vibration are not seen, the correct identification rate for individual identification will decrease. In Embodiment 2, an example of the form when the identification target is, for example, a drone will be described. In Embodiment 2, for the components that are the same as those of the configuration according to Embodiment 1 already described among the components according to Embodiment 2, duplicate explanations will be appropriately omitted.

[0049] Next, a configuration example of the individual identification device according to Embodiment 2 of the present disclosure will be described. Since the individual identification device according to Embodiment 2 is composed of the same components as the individual identification device 200 (200A) according to Embodiment 1 already described, it will be described by referring to FIG. 1. However, the reference signs will be appropriately described with "B" attached to distinguish them from the individual identification device 200 (200A) according to Embodiment 1. Also, the "A" attached in FIG. 1 will be appropriately changed to "B" for the description.

[0050] The sensor 100 acquires sensor data as individual data of the object. Here, an example using a vibration sensor will be described. The vibration sensor is a sensor that detects the vibration of the object, and the physical quantities representing vibration are acceleration, velocity, and displacement. By using a vibration sensor, individual data of a dynamic object represented by a drone can be acquired. However, the sensor 100 for acquiring individual data is not limited to a vibration sensor, and an ultrasonic sensor or a sensor composed of a substrate pattern may also be used. Also, the dynamic object is not limited to a drone, and it may be an electric / electronic device such as a robot or an animal such as a cow or a horse.

[0051] The individual identification device 200 (200B) identifies whether the object to be identified is a registered individual by comparing measured individual data, which includes measurement results for the object to be identified, with registered individual data that has been stored in advance. In this explanation, the object to be identified is assumed to be a drone. The individual identification device 200 (200B) is composed of a storage unit 210 (210B), a measurement unit 220, a data extraction unit 230, a data comparison unit 260, and an individual identification unit 270.

[0052] The memory unit 210 (210B) stores pre-registered individual data. The registered individual data is, for example, individual data for a genuine drone, and includes the vibration frequency and amplitude value for each frequency in the drone.

[0053] The measurement unit 220 outputs individual measurement data, including frequency and amplitude values, which indicate the measurement results of the object identified by the sensor 100. The measurement unit 220 is for acquiring individual data acquired by the sensor 100 (vibration sensor). It has the function of acquiring either the time-axis waveform of vibration or the frequency-axis data of vibration.

[0054] The data extraction unit 230 extracts frequencies and amplitude values ​​of those frequencies within an extraction range from the measured individual data and outputs them as extracted measured individual data. The data extraction unit 230 also extracts frequencies and amplitude values ​​of those frequencies within an extraction range from pre-registered registered individual data and outputs them as extracted registered individual data. The data extraction unit 230 is located before the data comparison unit 260 and extracts the measurement range of individual data (measured individual data, registered individual data). The data extraction unit 230 includes a measurement data extraction unit 250 that extracts the measurement range from the individual data (measured individual data) of the measurement unit 220. The data extraction unit 230 also includes a storage data extraction unit 240 that extracts the measurement range from the registered individual data of the storage unit 210 (210B).

[0055] The data comparison unit 260 compares the measured individual data extracted by the data extraction unit 230 with the registered individual data stored in advance. The data comparison unit 260 compares the measured individual data extracted by the data extraction unit 230 with the registered individual data extracted by the data extraction unit 230. The data comparison unit 260 is a processing unit for comparing the measured individual data acquired by the measurement unit 220 with the registered individual data of genuine products that have been stored in advance in the storage unit 210 (210B). Its main function is to calculate the difference between each individual data set.

[0056] The individual identification unit 270 identifies the drone to be identified based on the comparison results from the data comparison unit 260. The individual identification unit 270 calculates whether the difference in each data calculated by the data comparison unit 260 falls within a certain threshold (difference threshold) and determines whether the drone is genuine. Here, a method for improving the accuracy of individual identification by the data extraction unit 230 will be described. The data extraction unit 230 prioritizes extracting data on vibration frequency and amplitude values, mainly focusing on the frequency of the vibration source and its harmonics. That is, the extraction range including the harmonic frequencies is calculated centered on the harmonic frequencies.

[0057] Figure 4 shows the vibration measurement results for a motor in a second embodiment of the present disclosure, where the vibration source to be identified is a motor. Figure 4 shows the vibration measurement results for 12 motors. The measurement method involved mounting the motors on the frame of a drone and measuring the vibrations propagating along the drone frame when the motors rotated. In Figure 4, the horizontal axis represents frequency, and the vertical axis represents the amplitude value of the vibration. The top 5 points with the highest peak vibration values ​​for each motor are plotted.

[0058] As shown in Figure 4, first, a peak in amplitude was observed at the operating frequency of the motor, which is the vibration source (vibration source frequency (fundamental frequency) 1000). Next, a point of interest in these measurement results is that the amplitude values ​​of individual motors varied significantly, mainly around the harmonics of the vibration source frequency and the surrounding frequencies, as seen in the regions of large amplitude and frequency changes (regions including harmonic frequencies) 2000, 3000, and 4000 shown in Figure 4. Factors that cause such individual differences in vibration include motor shaft misalignment and the way the motor is mounted to the drone frame. By utilizing this characteristic and extracting vibration data mainly from the regions of large amplitude and frequency changes, the accuracy of individual drone identification can be increased, improving the precision of individual identification.

[0059] Regarding the frequencies extracted by the data extraction unit 230, using only the harmonics of the vibration source frequency is too precise, resulting in a small number of extracted data points. In this case, by extracting data using the harmonics of the vibration source frequency ± N (N: integer) Hz as a guideline, it is possible to extract data in a range where the amplitude and frequency changes of the vibration are large, enabling individual identification with a high accuracy rate. Furthermore, it is important to measure with improved frequency resolution of the vibration data, which prevents missing amplitude peaks.

[0060] Here, the frequency of the vibration source may not necessarily be the same depending on the type of drone. In this case, it is necessary to measure the frequency of the vibration source in advance for each type of drone. By attaching a vibration sensor near the rotating part of the motor of the target drone and checking the frequency characteristics of the vibration, a peak in vibration will appear in the frequency band of the vibration source, making it possible to predict the frequency of the vibration source.

[0061] In this embodiment, the explanation was given assuming that the object to be identified is a drone. By employing the technology of this disclosure, a data extraction unit is provided that extracts the vibration frequency and amplitude values, focusing on the vibration frequency and harmonics of the motor mounted on the drone, thereby enabling highly accurate individual identification of the target drone.

[0062] This embodiment further illustrates an example configuration including the following: (3) The extraction range including the harmonic frequencies is calculated centered on the harmonic frequencies, characterized in that the individual identification device according to (1) or (2). This further provides an individual identification device that can improve the accuracy of individual identification when characteristic vibrations unique to the object to be identified are changing in the frequency direction. Furthermore, this disclosure can achieve the same effect as above by applying the above configuration to a system including the individual identification device or to the individual identification method described above.

[0063] Embodiment 3. In the embodiments described above (Embodiment 1 and Embodiment 2, respectively), the configuration in which there is one sensor was described. In the configurations of the embodiments described above, there is a problem that individual identification becomes impossible if the sensor fails. Therefore, in Embodiment 3, an example of a configuration that enables continuous individual identification even if the sensor fails will be described. In Embodiment 3, for components of Embodiment 3 that are the same as those of Embodiment 1 or Embodiment 2 already described, redundant explanations will be omitted as appropriate.

[0064] Next, an example of the configuration of an individual identification device according to Embodiment 3 of the present disclosure will be described. Figure 5 is a diagram showing a first example of the configuration of an individual identification system including an individual identification device according to Embodiment 3 of the present disclosure. The individual identification system 1 (1C) is configured to include a plurality of sensors 100, an individual identification device 200 (200C), and an output destination device 400. The plurality of sensors 100 are configured to include a sensor (first sensor) 100 (100-1) and a sensor (second sensor) 100 (100-2). That is, the individual identification system 1 (1C) shown in Figure 5 is configured to include two sensors (first sensor) 100 (100-1) and a sensor (second sensor) 100 (100-2). In the sensor (first sensor) 100 (100-1) and the sensor (second sensor) 100 (100-2), the types of the plurality of sensors may be the same type or different types. An example of combining two different types of sensors is to use one sensor as a vibration sensor and the other as an ultrasonic sensor. In other words, the two sensors 100 (sensor (first sensor) 100 (100-1) and sensor (second sensor) 100 (100-2)) may be configured to be of different types. When using two different types of sensors 100 (sensor (first sensor) 100 (100-1) and sensor (second sensor) 100 (100-2)), two-dimensional sensor data can be acquired. This allows for a more detailed capture of individual characteristics and improves the accuracy of individual identification.

[0065] The output destination device 400 is a device that receives and processes the individual identification results from the individual identification device 200 (200C), similar to the output destination device 400 described earlier.

[0066] The individual identification device 200 (200C) is configured to include a storage unit 210 (210C), a measurement unit 220, a data extraction unit 230, a data comparison unit 260, and an individual identification unit 270 (270C).

[0067] The storage unit 210 (210C) is configured to include a first storage unit 210-1 and a second storage unit 210-2. The first storage unit 210-1 and the second storage unit 210-2 each store the pre-registered registered individual data. If the sensor (first sensor) 100 (100-1) and the sensor (second sensor) 100 (100-2) are different types of sensors, for example, the first storage unit 210-1 stores the registered individual data corresponding to the sensor (first sensor) 100 (100-1), and the second storage unit 210-2 stores the registered individual data corresponding to the sensor (second sensor) 100 (100-2). If the sensor (first sensor) 100 (100-1) and the sensor (second sensor) 100 (100-2) are of different types, it is sufficient that either the first storage unit 210-1 or the second storage unit 210-2 is provided.

[0068] The measurement unit 220 outputs individual measurement data, including frequency and amplitude values, that indicate the measurement results of the objects to be identified by each of the sensors 100 (sensor (first sensor) 100 (100-1), sensor (second sensor) 100 (100-2)).

[0069] The data extraction unit 230 sequentially extracts the frequency and amplitude value of the frequency within the extraction range from the individual measurement data corresponding to each of the multiple sensors 100, and outputs them as extracted individual measurement data corresponding to each of the multiple sensors. The data extraction unit 230 also sequentially extracts the frequency and amplitude value of the frequency within the extraction range from the registered individual data that has been pre-registered for each of the multiple sensors 100, and outputs them as extracted registered individual data corresponding to each of the multiple sensors 100.

[0070] The data comparison unit 260 sequentially compares the measured individual data extracted by the data extraction unit 230 with the pre-stored registered individual data for each of the multiple sensors 100. The data comparison unit 260 sequentially compares the measured individual data extracted by the data extraction unit 230 with the registered individual data extracted by the data extraction unit 230 for each of the multiple sensors 100. The data comparison unit 260 sequentially outputs the difference values ​​of frequency and amplitude values ​​for each extraction range as the comparison result between the measured individual data extracted by the data extraction unit 230 and the registered individual data extracted by the data extraction unit 230 for each of the multiple sensors 100. The registered individual data used in the data comparison unit 260 shown in Figure 5 is the registered individual data extracted by the data extraction unit 230.

[0071] The individual identification unit 270 (270C) sequentially identifies the target to be identified based on the comparison results from the data comparison unit 260, corresponding to each of the multiple sensors 100. For example, the individual identification unit 270 sequentially determines whether the difference value as a comparison result from the data comparison unit 260 is less than or equal to a pre-stored difference threshold, corresponding to each of the multiple sensors 100. The individual identification unit 270 performs individual identification using the determination result. For example, the individual identification unit 270 determines that the individual is pre-registered if the difference value is less than or equal to the difference threshold, and determines that the individual is not pre-registered if the difference value exceeds the difference threshold. The individual identification unit 270 outputs the determination result (identification result) to the output destination device 400. The individual identification unit 270 (270C) further includes a comprehensive identification unit 275. The comprehensive identification unit 275 further identifies the target to be identified using the identification results corresponding to each of the multiple sensors 100. For example, the comprehensive identification unit 275 determines to adopt the most frequent identification result and performs individual identification. The individual identification unit 270 (270C) outputs the identification result from the integrated identification unit 275 to the output destination device 400.

[0072] An example of processing for an individual identification device according to Embodiment 1 of this disclosure will be described. Figure 6 is a flowchart showing an example of processing for an individual identification device according to Embodiment 3 of this disclosure. When the individual identification device 200 (200C) has completed all identification for each of the multiple sensors in the individual identification unit 270 (270C), it starts the processing shown in Figure 6 ("start").

[0073] The individual identification device 200 (200C) then performs a multiple identification result acquisition determination process (step ST5010 "Acquired multiple identification results?"). In the multiple identification result acquisition determination process of step ST5010, the comprehensive identification unit 275 in the individual identification unit 270 (270C) of the individual identification device 200 (200C) determines whether multiple identification results have been acquired.

[0074] If the individual identification device 200 (200C) determines in step ST5010 that it has obtained multiple identification results (step ST5010 "YES"), the individual identification device 200 (200C) then performs individual identification processing using the multiple identification results (step ST5020). In the processing of step ST5020, the comprehensive identification unit 275 in the individual identification unit 270 (270C) of the individual identification device 200 (200C) uses the multiple identification results to determine, for example, whether the object to be identified is a registered individual by deciding to adopt the most frequent identification result.

[0075] If the individual identification device 200 (200C) determines in the multiple identification result acquisition determination process of step ST5010 that it did not acquire multiple identification results (i.e., only one identification result was acquired) (step ST5010 "NO"), or if it performs individual identification processing using multiple identification results in step ST5020 and acquires an identification result, then the individual identification device 200 (200C) then performs identification result output processing (step ST5030). In the identification result output processing of step ST5030, the comprehensive identification unit 275 of the individual identification unit 270 (270C) of the individual identification device 200 (200C) outputs the identification result to the output destination device 400. The individual identification device 200 (200C) then terminates the process shown in Figure 6 (termination).

[0076] Herein, the individual identification device according to Embodiment 3 of the present disclosure may have the following configuration instead of the configuration described above. Next, a second configuration example of the individual identification device according to Embodiment 3 of the present disclosure will be described. Figure 7 is a diagram showing a second configuration example of an individual identification system including the individual identification device according to Embodiment 3 of the present disclosure. The individual identification system 1 (1C') shown in Figure 7 is composed of a plurality of sensors 100, an individual identification device 200 (200C'), and an output destination device 400. The output destination device 400 is a device that receives and processes the individual identification result from the individual identification device 200 (200A), similar to the output destination device 400 already described.

[0077] The sensors 100 shown in Figure 7 consist of two or more sensors. In the individual identification device 200 (200C) shown in Figure 5, there are two sensors 100 that acquire individual data, but this is not the only option, and the number of sensors 100 can be any number. The multiple sensors 100 shown in Figure 7 consist of sensors (first sensor) 100(100-1), ..., sensors (nth sensor (n≧2)) 100(100-n). In sensors (first sensor) 100(100-1), ..., sensors (nth sensor (n≧2)) 100(100-n), the types of sensors may be the same or different. An example of combining two different types of sensors is to use one sensor as a vibration sensor and the other as an ultrasonic sensor. In other words, the two or more sensors 100 (sensor (first sensor) 100(100-1), ..., sensor (nth sensor (n≧2)) 100(100-n)) may be configured to be of different types. When two or more sensors 100 of different types (sensor (first sensor) 100(100-1), ..., sensor (nth sensor (n≧2)) 100(100-n)) are used, multidimensional sensor data can be acquired. This allows for a more detailed capture of individual characteristics and improves the accuracy of individual identification.

[0078] The individual identification device 200 (200C') is configured to process individual identification corresponding to multiple sensors in parallel. The individual identification device 200 (200C') includes a storage unit 210 (210C'), a measurement unit 220 (220C), a data extraction unit 230 (230C), a data comparison unit 260 (260C), and an individual identification unit 270 (270C').

[0079] The memory unit 210 (210C') shown in Figure 7 comprises multiple memory units (first memory unit 210-1, ..., nth memory unit (n≧2) 210-n). When two or more sensors 100 of different types (sensor (first sensor) 100 (100-1), ..., sensor (nth sensor (n≧2)) 100 (100-n)) are used in the individual identification device 200 (200C'), the memory unit 210 (210C') is configured to comprise two or more units. The first memory unit 210-1 shown in Figure 7 corresponds to the sensor (first sensor) 100 (100-1), and the nth memory unit (n≧2) 210-n shown in Figure 7 corresponds to the sensor (nth sensor (n≧2)) 100 (100-n). In other words, two or more sensors 100 of different types (sensor (first sensor) 100 (100-1), ..., sensor (nth sensor (n≧2)) are configured so that each corresponds to a sensor 100 of a different type (sensor (first sensor) 100 (100-1), ..., sensor (nth sensor (n≧2))).

[0080] The measurement unit 220 (220C) shown in Figure 7 is composed of multiple measurement units (first measurement unit 220-1, ..., nth measurement unit (n≧2) 220-n)). The first measurement unit 220-1 corresponds to the sensor (first sensor) 100 (100-1), and the nth measurement unit (n≧2) 220-n is arranged to correspond to the sensor (nth sensor (n≧2)) 100 (100-n). Each of the first measurement units 220-1, ..., and nth measurement unit (n≧2) 220-n is configured in the same way as the measurement unit 220 already described.

[0081] The data extraction unit 230 (230C) is configured to include a storage data extraction unit 240 (240C) and a measurement data extraction unit 250 (250C). The storage data extraction unit 240 (240C) shown in Figure 7 is configured to include a plurality of storage data extraction units (first storage data extraction unit 240-1, ..., nth storage data extraction unit (n≧2) 240-n). The first storage data extraction unit 240-1 corresponds to the sensor (first sensor) 100 (100-1) and the first storage unit 210-1, and the nth storage data extraction unit (n≧2) 240-n is arranged to correspond to the sensor (nth sensor (n≧2)) 100 (100-n) and the nth storage unit (n≧2) 210-n. The first memory data extraction units 240-1, ..., and the nth memory data extraction units (n≧2) 240-n are each configured in the same manner as the memory data extraction unit 240 described above.

[0082] The measurement data extraction unit 250 (250C) shown in Figure 7 is composed of multiple measurement data extraction units (first measurement data extraction unit 250-1, ..., nth measurement data extraction unit (n≧2) 250-n). The first measurement data extraction unit 250-1 corresponds to the sensor (first sensor) 100 (100-1) and the first measurement unit 220-1, and the nth measurement data extraction unit (n≧2) 250-n is arranged to correspond to the sensor (nth sensor (n≧2)) 100 (100-n) and the nth measurement unit (n≧2) 220-n. Each of the first measurement data extraction units 250-1, ..., and nth measurement data extraction unit (n≧2) 250-n is configured in the same way as the measurement data extraction unit 250 already described.

[0083] The data comparison unit 260 (260C) shown in Figure 7 is composed of multiple data comparison units (first comparison unit 260-1, ..., nth comparison unit (n≧2) 260-n). The first comparison unit 260-1 corresponds to the first stored data extraction unit 240-1 and the first measured data extraction unit 250-1, and the nth comparison unit (n≧2) 260-n is arranged to correspond to the nth stored data extraction unit (n≧2) 240-n and the nth measured data extraction unit (n≧2) 250-n. Each of the first comparison units 260-1, ..., and the nth comparison unit (n≧2) 260-n is configured in the same way as the data comparison unit 260 already described.

[0084] The individual identification unit 270 (270C') shown in Figure 7 is composed of multiple individual identification units (first identification unit 270-1, ..., nth identification unit (n≧2) 270-n). The first identification unit 270-1 corresponds to the first comparison unit 260-1, and the nth identification unit (n≧2) 270-n is arranged to correspond to the nth comparison unit (n≧2) 260-n. Each of the first identification units 270-1, ..., and the nth identification unit (n≧2) 270-n is configured in the same way as the individual identification unit 270 already described. The integrated identification unit 275 further identifies the target of identification using the identification results corresponding to each of the multiple sensors. The integrated identification unit 275 shown in Figure 7 identifies the target of identification using the identification results from each of the first identification units 270-1, ..., and the nth identification unit (n≧2) 270-n. The integrated identification unit 275 identifies individual devices by, for example, determining which identification result is most frequent. The individual identification unit 270 (270C') outputs the identification result from the integrated identification unit 275 to the output destination device 400.

[0085] The individual identification device according to this embodiment can continue to identify individual objects even if, for example, one sensor fails, because other sensors will continue to operate. When different types of sensors are used, the individual identification device according to this embodiment can acquire multidimensional sensor data. This allows for a more detailed capture of individual characteristics, improving the accuracy of individual identification.

[0086] This embodiment further describes a configuration including the following: (5) The individual identification device according to any one of (1) to (4) above, characterized in that the sensors consist of two or more. This further provides the individual identification device in which individual identification can be continued even if a sensor fails. Furthermore, this disclosure can achieve the same effect as above by applying the above configuration to a system including an individual identification device or to the individual identification method described above.

[0087] This embodiment further describes a configuration including the following: (7) The individual identification device according to any one of (1) to (6) above, characterized by comprising a storage unit in which the registered individual data is stored. This further provides the individual identification device that can hold pre-registered individual data and can acquire registered individual data without communication. This disclosure also achieves the same effect as above by applying the above configuration to a system including an individual identification device or to the above method. (8) The individual identification device according to (7) above, characterized by comprising two or more of the storage units. This further provides the individual identification device that can continue individual identification even if a storage unit fails. This disclosure also achieves the same effect as above by applying the above configuration to a system including an individual identification device or to the above individual identification method.

[0088] This embodiment further describes a configuration including the following: (9) The individual identification device according to (8), characterized in that the two or more sensors are each of different types. This further provides an individual identification device that can acquire multidimensional sensor data, thereby capturing individual characteristics in more detail and improving the accuracy of individual identification. Furthermore, this disclosure can achieve the same effect as above by applying the above configuration to a system including an individual identification device or to the individual identification method described above.

[0089] Here, we will describe the hardware configuration for realizing the functions of the present disclosure. Figure 8 is a diagram showing a first example of the hardware configuration for realizing the functions according to the configuration of the present disclosure. Figure 9 is a diagram showing a second example of the hardware configuration for realizing the functions according to the configuration of the present disclosure. The individual identification devices 200 (200A, 200C, 200C') of the present disclosure are each realized by the hardware shown in Figure 8 or Figure 9.

[0090] Each individual identification device 200 (200A, 200C, 200C') is composed of, for example, a processor 10001, a memory 10002, an input / output interface 10003, and a communication circuit 10004, as shown in Figure 8. The processor 10001 and memory 10002 are, for example, those installed in a computer. Memory 10002 stores a program for causing the computer to function as a measurement unit 220 (220C), a first measurement unit 220-1, an nth measurement unit (n≧2) 220-n, a data extraction unit 230 (230C), a stored data extraction unit 240 (240C), a first stored data extraction unit 240-1, an nth stored data extraction unit (n≧2) 240-n, a measurement data extraction unit 250 (250C), a first measurement data extraction unit 250-1, an nth measurement data extraction unit (n≧2) 250-n, a data comparison unit 260 (260C), a first comparison unit 260-1, an nth comparison unit (n≧2) 260-n, an individual identification unit 270 (270C, 270C'), a first identification unit 270-1, an nth identification unit (n≧2) 270-n, a comprehensive identification unit 275, and a control unit (not shown). The processor 10001 reads and executes the program stored in memory 10002, thereby enabling the measurement unit 220 (220C), the first measurement unit 220-1, the nth measurement unit (n≧2) 220-n, the data extraction unit 230 (230C), the stored data extraction unit 240 (240C), the first stored data extraction unit 240-1, the nth stored data extraction unit (n≧2) 240-n, and the measurement data extraction unit 25 The functions of the nth measurement data extraction unit (n≧2) 250-n, the data comparison unit 260 (260C), the first comparison unit 260-1, the nth comparison unit (n≧2) 260-n, the individual identification unit 270 (270C, 270C'), the first identification unit 270-1, the nth identification unit (n≧2) 270-n, the comprehensive identification unit 275, and a control unit (not shown) are realized by memory 10002 or other memory (not shown), the storage unit 210 (210C, 210C'), the first storage unit 210-1, the second storage unit 210-2, the nth storage unit (n≧2) 210-n, and a storage unit (not shown). Furthermore, the functions of the communication unit (not shown) are realized by the communication circuit 10004.

[0091] The processor 10001 uses, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a microprocessor, a microcontroller, or a DSP (Digital Signal Processor). The memory 10002 may be a non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable Read Only Memory), or flash memory; it may be a magnetic disk such as a hard disk or flexible disk; it may be an optical disk such as a CD (Compact Disc) or DVD (Digital Versatile Disc); or it may be a magneto-optical disk. The processor 10001 and the memory 10002 or the communication circuit 10004 are connected in a manner that enables them to transmit data to each other. Furthermore, the processor 10001, the memory 10002, and the communication circuit 10004 are connected in a manner that allows them to mutually transmit data with other hardware via the input / output interface 10003.

[0092] Alternatively, in the individual identification device 200 (200A, 200C, 200C'), the measurement unit 220 (220C), the first measurement unit 220-1, the nth measurement unit (n≧2) 220-n, the data extraction unit 230 (230C), the stored data extraction unit 240 (240C), the first stored data extraction unit 240-1, the nth stored data extraction unit (n≧2) 240-n, the measurement data extraction unit 250 (250C), and the first measurement data extraction unit 250-1 The functions of the nth measurement data extraction unit (n≧2) 250-n, data comparison unit 260 (260C), first comparison unit 260-1, nth comparison unit (n≧2) 260-n, individual identification unit 270 (270C, 270C'), first identification unit 270-1, nth identification unit (n≧2) 270-n, overall identification unit 275, and the control unit (not shown) may be realized by a dedicated processing circuit 20001, as shown in Figure 9.

[0093] The processing circuit 20001 may be a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), an FPGA (Field-Programmable Gate Array), a SoC (System-on-a-Chip), or a system LSI (Large-Scale Integration), etc. A storage unit (not shown) is realized by memory 20002 or other memory (not shown). The memory 20002 may be a non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable Read Only Memory), or flash memory; it may be a magnetic disk such as a hard disk or flexible disk; it may be an optical disk such as a CD (Compact Disc) or DVD (Digital Versatile Disc); or it may be a magneto-optical disk. Furthermore, a communication unit (not shown) is realized by the communication circuit 20004. The processing circuit 20001 and the memory 20002 or the communication circuit 20004 are connected in a manner that allows them to transmit data to each other. Furthermore, the processing circuit 20001, the memory 20002, and the communication circuit 20004 are connected in a manner that allows them to transmit data to each other with other hardware via the input / output interface 20003.Furthermore, in the individual identification device 200 (200A, 200C, 200C'), the measurement unit 220 (220C), the first measurement unit 220-1, the nth measurement unit (n≧2) 220-n, the data extraction unit 230 (230C), the stored data extraction unit 240 (240C), the first stored data extraction unit 240-1, the nth stored data extraction unit (n≧2) 240-n, the measurement data extraction unit 250 (250C), the first measurement data extraction unit 250- The functions of the nth measurement data extraction unit (n≧2) 250-n, the data comparison unit 260 (260C), the first comparison unit 260-1, the nth comparison unit (n≧2) 260-n, the individual identification unit 270 (270C, 270C'), the first identification unit 270-1, the nth identification unit (n≧2) 270-n, the comprehensive identification unit 275, and the control unit (not shown) may be implemented by separate processing circuits, or they may be implemented together in a single processing circuit.

[0094] Alternatively, in the individual identification device 200 (200A, 200C, 200C'), the measurement unit 220 (220C), the first measurement unit 220-1, the nth measurement unit (n≧2) 220-n, the data extraction unit 230 (230C), the stored data extraction unit 240 (240C), the first stored data extraction unit 240-1, the nth stored data extraction unit (n≧2) 240-n, the measurement data extraction unit 250 (250C), and the first measurement data extraction unit 250-1 are the nth measurement data extraction unit (n≧ 2) It is also possible that some functions of the control unit (not shown), including 250-n, data comparison unit 260 (260C), first comparison unit 260-1, nth comparison unit (n≧2) 260-n, individual identification unit 270 (270C, 270C'), first identification unit 270-1, nth identification unit (n≧2) 270-n, comprehensive identification unit 275, and some functions of the control unit (not shown) are implemented by the processor 10001 and memory 10002, and the remaining functions are implemented by the processing circuit 20001.

[0095] Within the scope of this disclosure, it is possible to freely combine the embodiments, modify any component of each embodiment, or omit any component of each embodiment.

[0096] This disclosure can improve the accuracy of individual identification, and is therefore suitable for use in individual identification devices that perform individual identification of objects such as drones.

[0097] 1(1A, 1C, 1C') Individual identification system, 100 Sensor, 100(100-1) Sensor (first sensor), 100(100-2) Sensor (second sensor), 100(100-n) Sensor (nth sensor (n≧2)), 200(200A, 200C, 200C') Individual identification device, 210(210C, 210C') Memory unit, 210-1 First memory unit, 210-2 Second memory unit, 210-n nth memory unit (n≧2), 220(220C) Measurement unit, 220-1 First measurement unit, 220-n nth measurement unit (n≧2), 230(230C) Data extraction unit, 240(240C) Memory data extraction unit, 240-1 First memory data extraction unit, 240-n nth memory data extraction unit (n≧2), 250 (250C) Measurement data extraction unit, 250-1 First measurement data extraction unit, 250-n nth measurement data extraction unit (n≧2), 260 (260C) Data comparison unit, 260-1 First comparison unit, 260-n nth comparison unit (n≧2), 270 (270C, 270C') Individual identification unit, 270-1 First identification unit, 270-n nth identification unit (n≧2), 275 Overall identification unit, 400 Output destination device, 1000 Frequency of vibration source (fundamental frequency), 2000 Region with large changes in amplitude and frequency (region including harmonic frequencies), 3000 Region with large changes in amplitude and frequency (region including harmonic frequencies), 4000 Region with large changes in amplitude and frequency (region including harmonic frequencies), 10001 Processor, 10002 Memory, 10003 Input / Output Interface, 10004 Communication Circuit, 20001 Processing Circuit, 20002 Memory, 20003 Input / Output Interface, 20004 Communication Circuit.

Claims

A measurement unit that outputs individual measurement data including frequency and amplitude values ​​that indicate the measurement results of the object identified by the sensor, A data extraction unit extracts frequency and amplitude values ​​in an extraction range including the fundamental frequency and an extraction range including the harmonic frequencies from the measured individual data, based on the measured individual data, and outputs the extracted measured individual data. A data comparison unit compares the measured individual data extracted by the data extraction unit with the pre-stored registered individual data. An individual identification unit that identifies an individual target based on the comparison results from the data comparison unit, An individual identification device equipped with the following features. 　 The data extraction unit, The frequency and amplitude values ​​in the extraction range including the fundamental frequency and the extraction range including the harmonic frequencies of the vibration in the object to be identified are extracted from the measured individual data, and the extracted measured individual data is output. The individual identification device according to feature 1. 　 The extraction range, which includes the aforementioned harmonic frequencies, is calculated with the harmonic frequencies as the center. The individual identification device according to claim 1 or 2. 　 The aforementioned com is provided, The aforementioned sensor is a vibration sensor. The individual identification device according to any one of claims 1 to 3. 　 The aforementioned sensors consist of two or more units. The individual identification device according to any one of claims 1 to 4. 　 The data extraction unit further, Based on the pre-registered registered individual data, the frequency and amplitude values ​​in the extraction range including the fundamental frequency and the extraction range including the harmonic frequencies are extracted from the registered individual data, and the extracted registered individual data is output. The data comparison unit compares the measured individual data extracted by the data extraction unit with the registered individual data extracted by the data extraction unit. The individual identification device according to any one of claims 1 to 5. 　 The system includes a storage unit in which the registered individual data is stored. The individual identification device according to any one of claims 1 to 6. 　 The system comprises two or more of the aforementioned storage units. The individual identification device according to feature 7. 　 The two or more sensors mentioned above are each of different types of sensors. The individual identification device according to feature 8. 　 A method for individual identification using an individual identification device, The measurement unit of the individual identification device outputs measured individual data including frequency and amplitude values ​​that indicate the measurement results of the object to be identified by the sensor. The data extraction unit of the individual identification device extracts frequency and amplitude values ​​in the extraction range including the fundamental frequency and the extraction range including the harmonic frequency, respectively, from the measured individual data, and outputs the extracted measured individual data. The data comparison unit of the individual identification device compares the measured individual data extracted by the data extraction unit with the pre-stored registered individual data. The individual identification unit of the individual identification device identifies an individual object based on the comparison result from the data comparison unit. A method for identifying individuals characterized by the following features.

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