Learning model, signal processing device, flying body, and teacher data generation method

Abstract

To enable the detection of a target, such as a ground object, using RAW data.SOLUTION: A learning model causes a computer to function so as to receive a first synthetic aperture radar reception signal based on a reflected electromagnetic wave, which is reflection of an electromagnetic wave with which a learning region is irradiated, to learn using teacher data, where an output is first target information of a target in the learning region corresponding to a feature amount of the first synthetic aperture radar reception signal, and to output, in response to an input of a second synthetic aperture radar reception signal based on a reflected electromagnetic wave which is reflection of an electromagnetic wave with which a detection region is irradiated, second target information of the target in the detection region corresponding to a feature amount of the second synthetic aperture radar reception signal.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a learning model, a signal processing device, a flying object, and a program.

Background Art

[0002] Observation of the state of the earth's surface including land and sea using flying objects such as artificial satellites, aircraft, or drone devices is widely carried out. Observation methods using artificial satellites include an observation method that acquires a so-called SAR image, which is a radar image obtained using synthetic aperture radar (SAR) technology, and an observation method that acquires an optical image and a SAR image and combines the two images. Patent Document 1 describes detecting an object without performing SAR imaging processing using raw data (RAW data) obtained by synthetic aperture radar.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Regarding how to realize the detection of objects such as ground features using RAW data, various methods can be considered. Therefore, an object of the present invention is to provide a learning model, a signal processing device, a flying object, and a teacher data generation method that enable the detection of objects such as ground features using RAW data.

Means for Solving the Problems

[0005] The learning model according to one aspect of the present invention is trained using teacher data that takes, as input, the feature amount of a first synthetic aperture radar reception signal based on the reflected electromagnetic wave obtained by irradiating a learning area with an electromagnetic wave, and outputs, as output, first object information of an object in the learning area, and causes a computer to function so as to output second object information of an object in a detection area for an input of the feature amount of a second synthetic aperture radar reception signal based on the reflected electromagnetic wave obtained by irradiating the detection area with an electromagnetic wave.

[0006] According to this aspect, object information can be output based on the feature amount of the synthetic aperture radar reception signal, so that it is possible to detect an object such as a ground feature using RAW data.

[0007] In the above aspect, the feature amount of the first synthetic aperture radar reception signal may include the frequency spectrum of the first synthetic aperture radar reception signal, and the feature amount of the second synthetic aperture radar reception signal may include the frequency spectrum of the second synthetic aperture radar reception signal.

[0008] In the above aspect, the feature amount of the first synthetic aperture radar reception signal may include the signal intensity of the first synthetic aperture radar reception signal, and the feature amount of the second synthetic aperture radar reception signal may include the signal intensity of the second synthetic aperture radar reception signal.

[0009] In the above aspect, the feature amount of the first synthetic aperture radar reception signal may include the phase of the first synthetic aperture radar reception signal, and the feature amount of the second synthetic aperture radar reception signal may include the phase of the second synthetic aperture radar reception signal.

[0010] According to these aspects, object information based on the frequency spectrum, signal intensity, or phase can be output as the feature amount of the synthetic aperture radar reception signal, so that it is possible to detect an object such as a ground feature using RAW data.

[0011] In the above aspect, the first synthetic aperture radar reception signal is a signal corresponding to a partial region in the learning region where the feature amount satisfies a predetermined condition, and the feature amount of the first synthetic aperture radar reception signal may include a feature amount based on an object in the learning region.

[0012] According to this aspect, since the reception signal that is the source of the feature amount of the reception signal used as the teacher data of the learning model is a signal corresponding to a region where the feature amount satisfies a predetermined condition, the amount of data required for learning is reduced. In addition, since the feature amount of the reception signal includes a feature amount based on an object in the learning region, it is possible to detect an object such as a ground object using RAW data while reducing the amount of data.

[0013] In the above aspect, the second synthetic aperture radar reception signal is a signal corresponding to a partial region in the detection region where the feature amount satisfies a predetermined condition, and the feature amount of the second synthetic aperture radar reception signal may include a feature amount based on an object in the detection region.

[0014] According to this aspect, for the feature amount of the second synthetic aperture radar reception signal used in the inference using the learning model, it can also be based on a signal corresponding to a partial region where the feature amount satisfies a predetermined condition, so the amount of data required for inference is reduced. In addition, since the feature amount of the second synthetic aperture radar reception signal includes a feature amount based on an object in the learning region, it is possible to detect an object such as a ground object using RAW data while reducing the amount of data.

[0015] In the above aspect, the first object information may include information indicating the attribute of the object in the learning region, and the second object information may include information indicating the attribute of the object in the detection region.

[0016] According to this aspect, since it is possible to obtain information indicating the attribute of the object, it is possible to detect the object while obtaining more detailed information about the object.

[0017] An information processing apparatus according to another aspect of the present invention includes a storage unit that stores a learning model, a signal acquisition unit that acquires a second synthetic aperture radar reception signal, and an estimation unit that inputs the second synthetic aperture radar reception signal to the learning model and infers second object information.

[0018] An aircraft according to another aspect of the present invention includes a storage unit that stores a learning model, a signal acquisition unit that acquires a second synthetic aperture radar reception signal, an estimation unit that inputs the second synthetic aperture radar reception signal to the learning model and infers second object information, and a signal output unit that outputs an output signal based on the second object information to the outside.

[0019] A teacher data generation method according to another aspect of the present invention includes a computer acquiring a first synthetic aperture radar reception signal based on a reflected electromagnetic wave obtained by reflecting an electromagnetic wave irradiated on a learning area, extracting a feature amount of a learning signal corresponding to an area where an object exists in the learning area from the first synthetic aperture radar reception signal, acquiring object information of the object, and storing, as teacher data used for learning a learning model that causes the computer to function so as to output second object information of the object in a detection area corresponding to a feature amount of a second synthetic aperture radar reception signal based on a reflected electromagnetic wave obtained by reflecting an electromagnetic wave irradiated on the detection area, in response to an input of the feature amount of the second synthetic aperture radar reception signal.

Advantages of the Invention

[0020] According to the present invention, it is possible to detect an object such as a ground object using RAW data.

Brief Description of the Drawings

[0021]

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Mode for Carrying Out the Invention

[0022] With reference to the accompanying drawings, preferred embodiments of the present invention will be described. In each figure, those denoted by the same reference numerals have the same or similar configurations.

[0023] FIG. 1 shows a block diagram of an observation system 10 according to the present embodiment. The observation system 10 includes a flying object 100 and an observation device 200. The flying object 100 is arranged in the space above the Earth, and the observation device 200 is arranged on the Earth. In the present embodiment, the flying object 100 observes a detection area D on the Earth's surface by radar, and an observation signal O processed in the flying object 100 is transmitted to the observation device 200. The observation signal O is, for example, a signal indicating a received signal acquired by the flying object 100 or object information corresponding to the received signal and being information of an object such as a ground feature or a ship on the sea in the detection area D, as will be described later.

[0024] The flying object 100 includes a communication antenna 101, a radar device 102, and a signal processing device 103. The flying object 100 is an artificial satellite capable of acquiring and processing received signals, and is arranged in space to orbit around the Earth. Note that the flying object 100 may be a geostationary satellite. Also, the flying object 100 may be a device such as an aircraft, a helicopter, or a drone device that can be located above the Earth.

[0025] The communication antenna 101 is an antenna for the flying object 100 to communicate with an external device provided on the Earth or in space.

[0026] The radar device 102 is a device that irradiates an electromagnetic wave EM1, which is, for example, a microwave, onto a detection area D on the Earth's surface, and acquires a reflected electromagnetic wave EM2 reflected by an observation object in the detection area D. The radar device 102 is, for example, a synthetic aperture radar (SAR). The reflected electromagnetic wave EM2 is processed and recorded by the radar device 102 as a received signal (RAW data) based on the fluctuation of the electromagnetic wave that can be handled by the flying object 100. The received signal is recorded, for example, as a complex number signal corresponding to each coordinate of the detection area D. The radar device 102 transmits the observation signal O to the observation device 200 through the communication antenna 101.

[0027] The radar device 102 includes a processor for controlling the acquisition process of the received signal and a storage device storing a program necessary for the control.

[0028] The signal processing device 103 is an information processing device that processes the received signal acquired by the radar device 102. The signal processing device 103 has a storage area such as a memory, and is a computer that performs predetermined processing by a processor executing a program stored in the storage area.

[0029] The signal processing device 103 has a storage unit 104 and a control unit 105. The storage unit 104 is, for example, a semiconductor memory such as a RAM or an optical disk. The storage unit 104 stores various information used in the processing in the signal processing device 103.

[0030] A learning model 1041 is stored in the storage unit 104. The learning model 1041 is a program that is learned to take a received signal as an input and output object information corresponding to the feature amount of the received signal. Details of the feature amount and the learning model 1041 in the present embodiment will be described later.

[0031] The control unit 105 performs signal processing in the signal processing device 103. Further, the control unit 105 controls the transmission of the processing result of the received signal through the flying object 100. The control unit 105 has a signal acquisition unit 1051, an estimation unit 1052, and a signal output unit 1053.

[0032] The signal acquisition unit 1051 acquires a received signal from the radar device 102.

[0033] The estimation unit 1052 inputs the feature amount of the received signal acquired by the signal acquisition unit 1051 into the learning model 1041, and acquires object information from the learning model 1041.

[0034] The signal output unit 1053 outputs the object information acquired by the estimation unit 1052 as an observation signal O to the observation device 200 through the communication antenna 101. Further, the signal output unit 1053 may output the received signal corresponding to the object information as the observation signal O together with the object information.

[0035] The observation device 200 is a device that transmits a control signal for controlling the observation of the detection area D by the flying object 100 to the flying object 100 and acquires the observation signal O from the flying object 100. The observation device 200 includes a communication unit 201 including an antenna and a control unit that controls communication by the antenna. The transmission and reception of information with the flying object 100 are performed through the communication unit 201.

[0036] The signal processing unit 202 processes the observation signal O from the flying object 100. The signal processing unit 202 performs processing for visualizing, for example, the observation result in the detection area D as an image based on the observation signal O acquired from the flying object 100.

[0037] With reference to FIGS. 2 and 3, the learning of the learning model 1041 according to this embodiment will be described.

[0038] FIG. 2 is a diagram schematically illustrating the learning and inference of the learning model 1041. The learning model 1041 is learned using the learning data LD1 as teacher data. The learning data LD includes a set of a feature amount of a received signal R0 (first synthetic aperture radar received signal) acquired by irradiating an electromagnetic wave to a certain area (learning area) and object information TI0 (first object information) corresponding to the feature amount of the received signal R0. The learning model 1041 is learned with the feature amount of the received signal R0 as an input and the object information TI0 as an output. Here, the feature amount in this embodiment is information including the frequency spectrum, signal intensity, or phase of the received signal R0.

[0039] FIG. 3 shows the received signal R0 and the feature amount of the received signal when the learning area D0 in which two objects, object O1 and object O2, exist is observed.

[0040] When the learning area D0 is observed, the received signal R0 is obtained. Based on the received signal R0, the feature amount (frequency spectrum, signal intensity, or phase) of the received signal R0 is calculated. For associating the object information TI0 with the feature amount of the received signal R0, information processing is performed to make the object information TI0 corresponding to the received signal R0 understandable to the user.

[0041] For example, based on the received signal R0, a feature amount of the received signal R0 may be generated as a graph, and the user may associate the object information TI0 based on the graph. Also, using a learning model that associates the received signal R0 with the object information TI0, the computer may associate object information with the feature amount of the received signal R0. Further, the received signal R0 may be information understandable by the user through a predetermined conversion process for SAR imaging. Alternatively, the object information TI0 associated with the feature amount may be information obtained from another device that observes the learning region D0 without going through the above-described imaging. For example, when performing ship detection, the object information TI0 may be information obtained using an automatic identification system (AIS) for ships in addition to the information obtained from the processing of the received signal R0.

[0042] The object information TI0 is information associated with the feature amount (frequency spectrum, signal intensity, or phase) of the received signal R0. The received signal R0 is, for example, a signal obtained in the form of a complex number as I(t)+jQ(t) (j is the imaginary unit, t is time). When the received signal R0 is described in polar form, the received signal R0 can be expressed as A(t)e jθ(t) It can be expressed as. At this time, the signal intensity of the received signal R0 is A(t), the phase of the received signal R0 is θ(t), and the signal intensity and phase are obtained for each coordinate with respect to the detection region D0. Also, by performing frequency conversion on the received signal R0 in the time domain, the frequency spectrum X(ω) of the received signal R0 can be obtained.

[0043] The learning model 1041 is learned to output object information for an input of these feature amounts calculated from the received signal R0. For example, the learning model 1041 is learned such that the feature amount based on the received signal R0 is calculated in the control unit 105 and the object information is output using the feature amount as an input. Note that the learning model 1041 may be learned to receive the received signal R0 itself as an input and output the object information, and in this case, the calculation of the feature amount based on the received signal R0 may be performed in the learning model 1041.

[0044] The object information T10 includes information on the probability distribution of the probability that an object exists in each pixel of the learning area. Further, the object information TI0 includes, for example, information on the number of objects existing in the learning area and the attributes of the objects. The attributes of the objects are various information regarding the objects, such as, for example, the type of the object (building, moving body, terrain, natural object, etc.), the size of the object, the moving speed of the object, etc. The information on the attributes of the objects may be the probability of classifying the objects into certain attribute items.

[0045] The learning model 1041 is trained by using general machine learning methods such as using a neural network, etc., with the learning data LD1 prepared as described above. Note that the learning model 1041 may be configured as a single learning model, or may be configured as a learning model combining a plurality of learning models.

[0046] Based on the electromagnetic wave irradiated on the detection area by the flying object 100, when the feature amount of the received signal R1 (second synthetic aperture radar received signal) acquired by the flying object 100 is input to the learned learning model 1041, the learning model 1041 outputs object information TI1 (second object information).

[0047] With reference to FIGS. 4 and 5, the processing by the flying object 100 will be described. In the processing in FIGS. 4 and 5, it is assumed that the object information output by the learning model 1041 is information corresponding to the frequency spectrum of the received signal R1.

[0048] In step S401 of FIG. 4, the radar device 102 irradiates the detection area D1 with the electromagnetic wave EM1. The irradiation timing may be the timing controlled by the observation device 200, or may be the timing specified in advance in the flying object 100. As shown in FIG. 5, objects O3, O4, O5 exist in the detection area D1.

[0049] In step S402, the signal acquisition unit 1051 acquires the received signal R1 based on the reflected electromagnetic wave EM2 detected by the radar device 102 from the radar device 102.

[0050] In step S403, the estimation unit 1052 inputs the feature amount of the received signal R1 into the learning model 1041. At this time, the estimation unit 1052 performs an operation to calculate the feature amount (for example, frequency spectrum) of the received signal R1.

[0051] In step S404, the estimation unit 1052 acquires the object information MD1 corresponding to the feature amount of the received signal R1 from the learning model 1041. The object information includes, as an example, the object information TI1a and the object information TI1b shown in FIG. 5. The object information TI1a is a probability distribution of the probability that an object exists in the detection area. In the object information TI1a, areas A1, A2, and A3 are shown as areas where the probability of the existence of an object is high. The object information TI1b is the number of objects and the attributes of the objects in the detection area. In this example, three objects are detected.

[0052] In step S405, the signal output unit 1053 outputs an output signal based on the object information TI1 to the observation device 200. The output signal based on the object information TI1 is a signal that transmits all or part of the object information TI1. Alternatively, the output signal may be a signal that transmits information that is the result of information processing performed on the object information.

[0053] With reference to FIGS. 6 and 7, the case where the object information output by the learning model 1041 is information based on the signal intensity of the received signal R1 will be described. As shown in FIG. 7, it is assumed that objects O3, O4, and O5 exist in the detection area D1 in the same manner as in the case of FIG. 5.

[0054] In step S601 of FIG. 6, the radar device 102 irradiates the detection area D1 with the electromagnetic wave EM1. In step S602, the signal acquisition unit 1051 acquires the received signal R1 based on the reflected electromagnetic wave EM2 detected by the radar device 102 from the radar device 102.

[0055] In step S603, the estimation unit 1052 inputs the signal strength of the received signal R1 as a feature quantity of the received signal R1 into the learning model 1041. The received signal R1 is, for example, a signal capable of generating an image IG1 shown in FIG. 7. The image IG1 can take various forms according to the signal. As an example, in the image IG1, the signal strength obtained by detecting each object may be plotted, and the change in the signal strength may be shown like a pattern. The estimation unit 1052 acquires object information MD1 corresponding to the signal strength of the received signal R1 from the learning model 1041. The object information includes, as an example, object information TI1a and object information TI1b as shown in FIG. 7 and similar to the case shown in FIG. 5.

[0056] In step S605, the signal output unit 1053 outputs an output signal based on the object information TI1 to the observation device 200.

[0057] Referring to FIG. 8, the case where the object information output by the learning model 1041 is information based on the phase of the received signal R1 will be described. In step S801 of FIG. 8, the radar device 102 irradiates the detection area D1 with the electromagnetic wave EM1. In step S602, the signal acquisition unit 1051 acquires the received signal R1 based on the reflected electromagnetic wave EM2 detected by the radar device 102 from the radar device 102. In step S803, the estimation unit 1052 inputs the phase of the received signal R1 into the learning model 1041. In step S804, the estimation unit 1052 acquires object information MD1 corresponding to the signal strength of the received signal R1 from the learning model 1041. In step S605, the signal output unit 1053 outputs an output signal based on the object information TI1 to the observation device 200.

[0058] As described so far, when inferring object information using the learning model 1041, the learning model 1041 is learned to output object information with the frequency spectrum, intensity, or phase as a feature quantity of the received signal as input, and inference using the learning model 1041 is performed. At this time, it is possible to combine the frequency spectrum, intensity, or phase as a feature quantity of the received signal.

[0059] Referring to FIGS. 9 and 10, another learning method of the learning model 1041 according to this embodiment will be described.

[0060] FIG. 9 is a diagram schematically illustrating the learning and inference of the learning model 1041. The learning model 1041 is learned using the learning data LD2 as teacher data. The learning data LD2 includes a set of a received signal R0a (first synthetic aperture radar received signal) obtained by irradiating electromagnetic waves to a certain area (learning area) and object information TI0 (first object information) corresponding to the feature amount of the received signal R0a, where the feature amount satisfies a predetermined condition.

[0061] The predetermined condition for the feature amount is a condition that enables extraction of a received signal including the feature amount based on the object in the learning area, and can be changed according to the purpose of observation or the like. For example, when extracting the received signal R0a from the received signal R0, a received signal whose signal intensity satisfies a predetermined condition can be extracted. For example, the predetermined condition for the signal intensity is that the signal intensity is greater than a predetermined threshold. Also, for example, the predetermined condition for the frequency spectrum is that the frequency is within a predetermined frequency band. Also, for example, the predetermined condition for the phase is that the phase is within a predetermined range.

[0062] The learning model 1041 is learned with the feature amount of the received signal R0a as the input and the object information TI0 as the output.

[0063] Referring to FIG. 10, the generation of the learning data LD2 will be described. In the example of FIG. 10, the case of generating the learning data LD2 using the observation results of the learning area D0 in which two objects, object O1 and object O2, exist is shown.

[0064] When the learning area D0 is observed, the received signal R0 is obtained. When the received signal R0 is plotted as a graph or imaged such as being converted into a SAR image, the image IG0 is obtained. The image IG0 can be associated with the signal intensity of the received signal R0. Among the received signals R0, the received signals whose signal intensity becomes greater than a predetermined threshold value are extracted from the received signal R0. For example, a received signal R0a including a received signal R0a1 corresponding to a partial area in the image IG0 and a received signal R0a2 corresponding to another partial area in the image IG0 is extracted. When the received signal R0a1 is imaged, the image IG2 is obtained, and when the received signal R0a2 is imaged, the image IG3 is obtained. The images IG2 and IG3 can be associated with the respective signal intensities of the received signals R0a1 and R0a2.

[0065] Object information TI0 based on the image IG0 is respectively associated with the feature amounts of each received signal of the received signal R0a including the received signal R0a1 and the received signal R0a2. The object information includes, as an example, object information TI0a and object information TI0b. The object information TI0a is a probability distribution of the probability that an object exists in the detection area. In the object information TI0a, areas A4 and A5 are shown as areas where the probability that an object exists is high. The object information TI0b is the number of objects and the attributes of the objects in the detection area. In this example, two objects are detected. The learning data LD2 is generated as a set of the received signal R0a and the object information TI0.

[0066] The learning model 1041 is learned by a general machine learning method such as a method using a neural network using the learning data LD2 prepared as described above.

[0067] With reference to FIGS. 11 and 12, the processing by the flying object 100 will be described.

[0068] In step S1101 of FIG. 11, the radar device 102 irradiates the detection area D1 with the electromagnetic wave EM1. As shown in FIG. 12, objects O3, O4, and O5 exist in the detection area D1.

[0069] In step S1102, the signal acquisition unit 1051 acquires, from the radar device 102, a reception signal R1 based on the reflected electromagnetic wave EM2 detected by the radar device 102.

[0070] In step S1103, the estimation unit 1052 extracts a reception signal R1a that satisfies a condition of a predetermined signal intensity from the reception signal R1. The feature amount of the extracted reception signal R1a corresponds to, for example, an image IG4 in which a part of an image IG1 based on the reception signal R1 is cut out, as shown in FIG. 12.

[0071] In step S1104, the estimation unit 1052 inputs the reception signal R1a to the learning model 1041.

[0072] In step S1105, the estimation unit 1052 acquires, from the learning model 1041, object information MD1 based on the feature amount of the reception signal R1a. The object information includes, as an example, object information TI1a and object information TI1b shown in FIG. 12. In the object information TI1a, regions A1, A2, and A3 are shown as regions where the probability of the presence of an object is high, as in the case of FIG. 5. The object information TI1b is the number of objects in the detection region and the attributes of the objects. In this example, three objects are detected.

[0073] In step S1106, the signal output unit 1053 outputs an output signal based on the object information TI1 to the observation device 200.

[0074] In this way, by being able to output object information based on the feature amount of the entire reception signal from the feature amount of the partial reception signal, the data amount of the data that becomes the input used for inference by the learning model 1041 can be reduced. Furthermore, since the calculation time using the learning model 1041 can be shortened, the real-time performance of observation is improved. In addition, since the calculation load by the learning model 1041 can be reduced, it becomes possible to acquire object information using a lighter processing device.

[0075] FIG. 13 shows a block diagram showing the observation system 10A as another aspect. As shown in FIG. 13, the signal processing device 103 can also be provided to be included in the observation device 200A. The reception signal acquired by the radar device 102 is transmitted to the observation device 200A by the control unit 1301 of the flying object 100A. The above-described processing can also be performed by the signal processing device 103 of the observation device 200A.

[0076] The embodiments described above are for facilitating the understanding of the present invention and are not for limiting and interpreting the present invention. Each element and its conditions etc. included in the embodiments are not limited to those exemplified and can be changed as appropriate. Also, it is possible to partially replace or combine different configurations.

Description of Reference Numerals

[0077] 10…Observation system, 100…Flying object, 101…Communication antenna, 102…Radar device, 103…Signal processing device, 104…Storage unit, 1041…Learning model, 105…Control unit, 1051…Signal acquisition unit, 1052…Estimation unit, 1053…Signal output unit, 200…Observation device, 201…Communication unit, 202…Signal processing unit

Claims

1. It is trained using teacher data that takes, as input, the feature amount of a first synthetic aperture radar reception signal based on the reflected electromagnetic wave obtained by reflecting the electromagnetic wave irradiated to the learning area, and outputs the first object information of the object in the learning area. A learning model that causes a computer to function so as to output second object information of an object in the detection area in response to an input of the feature amount of a second synthetic aperture radar reception signal based on the reflected electromagnetic wave obtained by reflecting the electromagnetic wave irradiated to the detection area.

2. The learning model according to Claim 1, wherein the feature amount of the first synthetic aperture radar reception signal includes the frequency spectrum of the first synthetic aperture radar reception signal, and the feature amount of the second synthetic aperture radar reception signal includes the frequency spectrum of the second synthetic aperture radar reception signal.

3. The learning model according to Claim 1, wherein the feature amount of the first synthetic aperture radar reception signal includes the signal intensity of the first synthetic aperture radar reception signal, and the feature amount of the second synthetic aperture radar reception signal includes the signal intensity of the second synthetic aperture radar reception signal.

4. The learning model according to Claim 1, wherein the feature amount of the first synthetic aperture radar reception signal includes the phase of the first synthetic aperture radar reception signal, and the feature amount of the second synthetic aperture radar reception signal includes the phase of the second synthetic aperture radar reception signal.

5. The learning model according to Claim 1, wherein the first synthetic aperture radar reception signal is a signal corresponding to a partial area in the learning area where the feature amount satisfies a predetermined condition, and the feature amount of the first synthetic aperture radar reception signal includes the feature amount based on the object in the learning area.

6. The learning model according to Claim 5, wherein the second synthetic aperture radar reception signal is a signal corresponding to a partial area in the detection area where the feature amount satisfies the predetermined condition, and the feature amount of the second synthetic aperture radar reception signal includes the feature amount based on the object in the detection area.

7. The learning model according to Claim 1, wherein the first object information includes information indicating the attribute of the object in the learning area, and the second object information includes information indicating the attribute of the object in the detection area.

8. A storage unit storing the learning model according to any one of Claims 1 to 7, and a signal acquisition unit that acquires the second synthetic aperture radar reception signal. A signal processing apparatus comprising: an estimation unit that inputs the second synthetic aperture radar reception signal to the learning model and infers the second object information.

9. A storage unit that stores the learning model according to any one of claims 1 to 7; A signal acquisition unit that acquires the second synthetic aperture radar reception signal; An estimation unit that inputs the second synthetic aperture radar reception signal to the learning model and infers the second object information; An aircraft comprising: a signal output unit that outputs an output signal based on the second object information to the outside.

10. A computer, acquiring a first synthetic aperture radar reception signal based on a reflected electromagnetic wave obtained by reflecting an electromagnetic wave irradiated on a learning area; extracting a learning signal corresponding to an area where an object exists in the learning area from the first synthetic aperture radar reception signal; acquiring object information of the object; storing, as teacher data used for learning of a learning model that causes the computer to function so as to output second object information of the object in the detection area corresponding to a feature amount of the second synthetic aperture radar reception signal in response to the feature amount of the second synthetic aperture radar reception signal, for an input of a combination of the feature amount of the learning signal and the object information; A teacher data generation method including the above.

Images