JP7534744B1 - Learning model, signal processing device, flying object, and teacher data generation method - Google Patents

Abstract

[Problem] To enable detection of objects such as ground features using RAW data. [Solution] A learning model that learns using teacher data in which a first synthetic aperture radar reception signal based on electromagnetic waves reflected from an electromagnetic wave irradiated to a learning area is input, and first object information of the object in the learning area corresponding to the feature amount of the first synthetic aperture radar reception signal is output, and causes a computer to function in such a way that in response to an input of a second synthetic aperture radar reception signal based on electromagnetic waves reflected from an electromagnetic wave irradiated to a detection area, second object information of the object in the detection area corresponding to the feature amount of the second synthetic aperture radar reception signal. [Selected Figure] Figure 1

Description

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

Observations of the state of the Earth's surface, including land and sea, are widely carried out using flying objects such as artificial satellites, aircraft, and drones. Satellite observation methods include observation methods that acquire radar images, so-called SAR images, obtained using synthetic aperture radar (SAR) technology, and observation methods that acquire optical images and SAR images and combine the two. Patent Document 1 describes the use of raw data acquired by a synthetic aperture radar to detect objects without undergoing SAR imaging processing.

JP 2023-000897 A

There are various possible methods for detecting objects such as features using RAW data. Therefore, the present invention aims to provide a learning model, a signal processing device, a flying object, and a training data generation method that enable the detection of objects such as features using RAW data.

A learning model according to one aspect of the present invention is trained using teacher data in which the input is a feature of a first synthetic aperture radar received signal based on a reflected electromagnetic wave irradiated to a learning area, and the output is first object information of an object in the learning area, and causes a computer to function in such a way that in response to an input of the feature of a second synthetic aperture radar received signal based on a reflected electromagnetic wave irradiated to a detection area, the computer outputs second object information of an object in the detection area.

According to this aspect, object information can be output based on the features of the synthetic aperture radar received signal, making it possible to detect objects such as ground features using RAW data.

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

In the above aspect, the feature of the first synthetic aperture radar received signal may include the signal strength of the first synthetic aperture radar received signal, and the feature of the second synthetic aperture radar received signal may include the signal strength of the second synthetic aperture radar received signal.

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

According to these aspects, target information based on the frequency spectrum, signal strength, or phase can be output as a feature of the synthetic aperture radar received signal, making it possible to detect targets such as ground features using RAW data.

In the above aspect, the first synthetic aperture radar received signal is a signal corresponding to a portion of the learning area whose feature quantity satisfies a predetermined condition, and the feature quantity of the first synthetic aperture radar received signal may include a feature quantity based on an object in the learning area.

According to this aspect, the received signal that is the source of the features of the received signal that are used as training data for the learning model is a signal that corresponds to an area where the features satisfy a predetermined condition, so the amount of data required for learning is small. In addition, the features of the received signal include features based on objects in the learning area, so it is possible to detect objects such as ground features using RAW data while reducing the amount of data.

In the above aspect, the second synthetic aperture radar reception signal is a signal corresponding to a portion of the detection area whose feature quantity satisfies a predetermined condition, and the feature quantity of the second synthetic aperture radar reception signal may include a feature quantity based on an object in the detection area.

According to this aspect, the feature quantities of the second synthetic aperture radar reception signal used in inference using the learning model can also be based on signals corresponding to a portion of the area where the feature quantities satisfy a predetermined condition, so the amount of data required for inference is reduced. In addition, the feature quantities of the second synthetic aperture radar reception signal include feature quantities based on objects in the learning area, so that it is possible to detect objects such as ground features using RAW data while reducing the amount of data.

In the above aspect, the first object information may include information indicating attributes of the object in the learning area, and the second object information may include information indicating attributes of the object in the detection area.

According to this aspect, it is possible to obtain information indicating the attributes of an object, making it possible to detect the object while obtaining more detailed information about the object.

An information processing device according to another aspect of the present invention includes a memory unit in which a learning model is stored, 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.

A flying object according to another aspect of the present invention includes a memory unit in which a learning model is stored, 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.

A method for generating teacher data according to another aspect of the present invention includes a computer acquiring a first synthetic aperture radar reception signal based on electromagnetic waves reflected from an electromagnetic wave irradiated to a learning area, extracting from the first synthetic aperture radar reception signal a feature of the learning signal corresponding to an area of the learning area in which an object is present, acquiring object information of the object, and storing a pair of the feature of the learning signal and the object information as teacher data used in learning a learning model that causes the computer to function in such a way that, in response to an input of a feature of a second synthetic aperture radar reception signal based on electromagnetic waves reflected from an electromagnetic wave irradiated to a detection area, second object information of the object in the detection area corresponding to the feature of the second synthetic aperture radar reception signal is output.

The present invention makes it possible to detect objects such as ground features using RAW data.

FIG. 1 is a block diagram of an observation system according to an embodiment of the present invention. FIG. 2 is a diagram illustrating a learning model according to the present embodiment. FIG. 2 is a diagram illustrating learning of a learning model according to the present embodiment. 11 is a flowchart illustrating an example of processing in a flying object according to the present embodiment. FIG. 2 is a diagram illustrating an example of inference using a learning model according to the present embodiment. 10 is a flowchart illustrating another example of processing in the flying object according to the present embodiment. FIG. 11 is a diagram illustrating another example of inference using the learning model according to the present embodiment. 10 is a flowchart illustrating another example of processing in the flying object according to the present embodiment. FIG. 11 is a diagram illustrating another example of a learning model according to the present embodiment. 11 is a diagram illustrating an example of a method for generating teacher data used for learning a learning model according to the present embodiment. FIG. 10 is a flowchart illustrating another example of processing in the flying object according to the present embodiment. FIG. 11 is a diagram illustrating another example of inference using the learning model according to the present embodiment. FIG. 2 is a block diagram showing another aspect of the observation system.

A preferred embodiment of the present invention will be described with reference to the attached drawings. In each drawing, the same reference numerals are used to denote the same or similar configurations.

Figure 1 shows a block diagram of an observation system 10 according to this embodiment. The observation system 10 includes an air vehicle 100 and an observation device 200. The air vehicle 100 is placed in the space above the Earth, and the observation device 200 is placed on the Earth. In this embodiment, the air vehicle 100 observes a detection area D on the Earth's surface using a radar, and an observation signal O processed by the air vehicle 100 is transmitted to the observation device 200. The observation signal O is, for example, a received signal acquired by the air vehicle 100, which will be described later, or a signal corresponding to the received signal, indicating object information, which is information on objects such as land features and ships at sea in the detection area D.

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 placed in space and orbits the Earth. Note that the flying object 100 may be a geostationary satellite. The flying object 100 may also be any device capable of being positioned above the Earth, such as an aircraft, helicopter, or drone device.

The communication antenna 101 is an antenna that enables the flying object 100 to communicate with an external device installed on Earth or in outer space.

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

The radar device 102 includes a processor for controlling the acquisition process of the received signal and a storage device that stores the programs necessary for that control.

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 is a computer that has a storage area such as a memory, and performs predetermined processing by having a processor execute a program stored in the storage area.

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

The memory unit 104 stores a learning model 1041. The learning model 1041 is a program that is trained to receive a received signal as input and output object information according to the feature quantities of the received signal. Details of the feature quantities and the learning model 1041 in this embodiment will be described later.

The control unit 105 performs signal processing in the signal processing device 103. The control unit 105 also controls the transmission of the processing results 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.

The signal acquisition unit 1051 acquires the received signal from the radar device 102.

The estimation unit 1052 inputs the features 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.

The signal output unit 1053 outputs the object information acquired by the estimation unit 1052 to the observation device 200 as an observation signal O via the communication antenna 101. The signal output unit 1053 may also output a received signal corresponding to the object information as the observation signal O together with the object information.

The observation device 200 is a device that transmits a control signal to the flying object 100 to control the observation of the detection area D by the flying object 100, and acquires an observation signal O from the flying object 100. The observation device 200 has a communication unit 201 that includes an antenna and a control unit that controls communication by the antenna. Information is transmitted and received with the flying object 100 via the communication unit 201.

The signal processing unit 202 processes the observation signal O from the flying object 100. Based on the observation signal O acquired from the flying object 100, the signal processing unit 202 performs processing to visualize the observation results in the detection area D, for example, using an image.

The learning of the learning model 1041 according to this embodiment will be described with reference to Figures 2 and 3.

Figure 2 is a diagram that illustrates the learning and inference of the learning model 1041. The learning model 1041 learns using the learning data LD1 as teacher data. The learning data LD includes a set of features 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 features of the received signal R0. The learning model 1041 learns using the features of the received signal R0 as input and the object information TI0 as output. Here, the features in this embodiment are information including the frequency spectrum, signal intensity, or phase of the received signal R0.

Figure 3 shows the received signal R0 and the features of the received signal when a learning area D0 containing two objects, object O1 and object O2, is observed.

When the learning area D0 is observed, a received signal R0 is obtained. Based on the received signal R0, the features of the received signal R0 (frequency spectrum, signal strength, or phase) are calculated. In order to associate the feature of the received signal R0 with the object information TI0, information processing is performed to enable the user to grasp the object information TI0 corresponding to the received signal R0.

For example, the feature quantity of the received signal R0 may be generated as a graph based on the received signal R0, and the user may associate the object information TI0 based on the graph. Also, a computer may associate the object information with the feature quantity of the received signal R0 using a learning model that associates the received signal R0 with the object information TI0. Also, the received signal R0 may be converted into information that is understandable by the user through a predetermined conversion process for SAR imaging. Alternatively, the object information TI0 associated with the feature quantity may be information obtained from another device observing the learning area D0 without undergoing the above-mentioned imaging. For example, when detecting a ship, the object information TI0 may be information obtained from processing the received signal R0, as well as information obtained using an automatic identification system (AIS).

The object information TI0 is information associated with the feature quantity (frequency spectrum, signal strength, or phase) of the received signal R0. The received signal R0 is a signal obtained in the form of a complex number, for example, as I(t)+jQ(t) (j is an 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) . At this time, the signal strength of the received signal R0 is A(t), the phase of the received signal R0 is θ(t), and the signal strength and phase are obtained for each coordinate relative to the detection area D0. In addition, the frequency spectrum X(ω) of the received signal R0 can be obtained by performing a frequency conversion on the received signal R0 in the time domain.

The learning model 1041 is trained to output object information in response to the input of these feature quantities calculated from the received signal R0. For example, the learning model 1041 is trained to output object information using the feature quantities as input, with the feature quantities calculated in the control unit 105 based on the received signal R0. Note that the learning model 1041 may also be trained to receive the received signal R0 itself as input and output object information, in which case the learning model 1041 may be configured to calculate the feature quantities based on the received signal R0.

The object information T10 includes information on the probability distribution of the probability that an object exists in each pixel of the learning area. The object information TI0 also includes, for example, information on the number of objects present in the learning area and the attributes of the objects. The attributes of an object include various information related to the object, such as the type of object (building, moving object, terrain, natural object, etc.), the size of the object, the moving speed of the object, etc. The information on the attributes of an object may be the probability that the object is classified into a certain attribute item.

The learning model 1041 is trained by a general machine learning method, such as a method using a neural network, using 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 that combines multiple learning models.

When the features of the received signal R1 (second synthetic aperture radar received signal) acquired by the flying object 100 based on the electromagnetic waves irradiated to the detection area by the flying object 100 are input to the trained learning model 1041, the learning model 1041 outputs the object information TI1 (second object information).

The processing by the flying object 100 will be described with reference to Figures 4 and 5. In the processing in Figures 4 and 5, the target information output by the learning model 1041 is assumed to be information corresponding to the frequency spectrum of the received signal R1.

In step S401 of FIG. 4, the radar device 102 irradiates the detection area D1 with electromagnetic waves EM1. The timing of irradiation may be controlled by the observation device 200, or may be a timing designated in advance by the flying object 100. As shown in FIG. 5, objects O3, O4, and O5 are present in the detection area D1.

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

In step S403, the estimation unit 1052 inputs the feature quantity of the received signal R1 to the learning model 1041. At this time, the estimation unit 1052 performs a calculation to calculate the feature quantity (e.g., frequency spectrum) of the received signal R1.

In step S404, the estimation unit 1052 acquires 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, object information TI1a and object information TI1b shown in FIG. 5. Object information TI1a is a probability distribution of the probability that an object exists in the detection area. In object information TI1a, areas A1, A2, and A3 are shown as areas where the object is highly likely to exist. Object information TI1b is the number of objects in the detection area and the attributes of the objects. In this example, three objects are detected.

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 of the object information TI1 or a part of the object information TI1. Alternatively, the output signal may be a signal that transmits information resulting from information processing performed on the object information.

With reference to Figures 6 and 7, a case will be described in which the object information output by the learning model 1041 is information based on the signal strength of the received signal R1. As shown in Figure 7, it is assumed that objects O3, O4, and O5 are present in the detection area D1, as in the case of Figure 5.

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

In step S603, the estimation unit 1052 inputs the signal strength of the received signal R1 to the learning model 1041 as a feature of the received signal R1. The received signal R1 is, for example, a signal capable of generating the image IG1 shown in FIG. 7. The image IG1 can take various forms depending on 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 as 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. As an example, the object information includes object information TI1a and object information TI1b as shown in FIG. 7, similar to the case shown in FIG. 5.

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

Referring to FIG. 8, a case will be described where the object information output by the learning model 1041 is information based on the phase of the received signal R1. In step S801 of FIG. 8, the radar device 102 irradiates the detection area D1 with electromagnetic waves EM1. In step S602, the signal acquisition unit 1051 acquires the received signal R1 based on the reflected electromagnetic waves 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 to the learning model 1041. In step S804, the estimation unit 1052 acquires the 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.

As described above, when inferring object information using the learning model 1041, the learning model 1041 is trained to output object information using the frequency spectrum, intensity, or phase as the feature of the received signal as input, and inference is performed using the learning model 1041. At this time, it is possible to combine the frequency spectrum, intensity, or phase as the feature of the received signal.

With reference to Figures 9 and 10, we will explain other learning methods for the learning model 1041 according to this embodiment.

Figure 9 is a diagram that illustrates the learning and inference of the learning model 1041. The learning model 1041 learns using the learning data LD2 as teacher data. The learning data LD2 includes a pair of a received signal R0a, the feature of which satisfies a predetermined condition, among received signals R0 (first synthetic aperture radar received signals) acquired by irradiating an area (learning area) with electromagnetic waves, and object information TI0 (first object information) corresponding to the feature of the received signal R0a.

The predetermined conditions for the features are conditions that make it possible to extract a received signal that includes features based on the object in the learning area, and can be changed according to the purpose of observation, etc. For example, when extracting received signal R0a from received signal R0, it is possible to extract a received signal whose signal strength satisfies a predetermined condition. For example, the predetermined condition for the signal strength is that the signal strength is greater than a predetermined threshold. Also, for example, the predetermined condition for the frequency spectrum is that the frequency is in a predetermined frequency band. Also, for example, the predetermined condition for the phase is that the phase is within a predetermined range.

The learning model 1041 learns by inputting the features of the received signal R0a and outputting the object information TI0.

The generation of learning data LD2 will be described with reference to FIG. 10. The example of FIG. 10 shows the generation of learning data LD2 using the observation results of a learning area D0 in which two objects, object O1 and object O2, exist.

When the learning area D0 is observed, a received signal R0 is obtained. When the received signal R0 is plotted as a graph or imaged by converting it into a SAR image, an image IG0 is obtained. The image IG0 can be associated with the signal strength of the received signal R0. A received signal whose signal strength is greater than a predetermined threshold is extracted from the received signal R0. For example, a received signal R0a including a received signal R0a1 corresponding to a part of the area in the image IG0 and a received signal R0a2 corresponding to another part of the area in the image IG0 is extracted. When the received signal R0a1 is imaged, an image IG2 is obtained, and when the received signal R0a2 is imaged, an image IG3 is obtained. The images IG2 and IG3 can be associated with the signal strengths of the received signals R0a1 and R0a2, respectively.

The feature quantities of each received signal R0a, including received signal R0a1 and received signal R0a2, are associated with object information TI0 based on image IG0. 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 object is highly likely to exist. The object information TI0b is the number of objects in the detection area and the attributes of the objects. 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.

The learning model 1041 is trained using the learning data LD2 prepared as described above, for example, by a general machine learning method such as a method using a neural network.

The processing performed by the flying object 100 will be described with reference to Figures 11 and 12.

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

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

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

In step S1104, the estimation unit 1052 inputs the received signal R1a to the learning model 1041.

In step S1105, the estimation unit 1052 acquires object information MD1 based on the features of the received signal R1a from the learning model 1041. The object information includes, as an example, object information TI1a and object information TI1b shown in FIG. 12. In the object information TI1a, similar to the case of FIG. 5, areas A1, A2, and A3 are shown as areas where there is a high probability that objects exist. The object information TI1b is the number of objects in the detection area and the attributes of the objects. In this example, three objects are detected.

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

In this way, by making it possible to output object information based on the features of the entire received signal from the features of a partial received signal, it is possible to reduce the amount of data that is input to the learning model 1041 for inference. Furthermore, since the calculation time using the learning model 1041 can be shortened, the real-time nature of observation is improved. Furthermore, since it is possible to reduce the calculation load of the learning model 1041, it becomes possible to obtain object information using a lighter processing device.

Figure 13 shows a block diagram of an observation system 10A as another embodiment. As shown in Figure 13, the signal processing device 103 can also be provided so as to be included in the observation device 200A. The control unit 1301 of the flying object 100A transmits the received signal acquired by the radar device 102 to the observation device 200A. The above-mentioned processing can also be performed by the signal processing device 103 of the observation device 200A.

The above-described embodiment is intended to facilitate understanding of the present invention, and is not intended to limit the present invention. The elements and conditions of the embodiment are not limited to those exemplified, and can be modified as appropriate. Different configurations can also be partially substituted or combined.

10...observation system, 100...aircraft vehicle, 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 (13)

A learning model,
learning is performed using teacher data that is extracted based on a predetermined condition from a first synthetic aperture radar received signal based on a reflected electromagnetic wave that is irradiated onto a learning area, a first feature value corresponding to a portion of the learning area is input, and first object information of an object in the learning area is output, the predetermined condition being a condition that the first feature value includes a feature value based on the object in the learning area;
A learning model that causes a computer to function in such a way that, in response to an input of a second feature extracted from a second synthetic aperture radar received signal based on a reflected electromagnetic wave that is irradiated to a detection area, the computer outputs second object information of the object in the detection area. The learning model according to claim 1,
A learning model , wherein the specified condition is that, when the first feature is a frequency spectrum of the first synthetic aperture radar received signal, the frequency spectrum is within a specified frequency band. The learning model according to claim 1,
A learning model , wherein the specified condition is that when the first feature is the signal strength of the first synthetic aperture radar received signal, the signal strength is greater than a specified threshold. The learning model according to claim 1,
A learning model , wherein the specified condition is that when the first feature is the phase of the first synthetic aperture radar received signal, the phase is within a specified range. The learning model according to claim 1,
The second feature is a learning model extracted from the second synthetic aperture radar received signal based on the specified condition . A learning model,
learning is performed using teacher data in which a first feature amount including a frequency spectrum of a first synthetic aperture radar reception signal based on an electromagnetic wave irradiated to a learning area and reflected therefrom is input, and first object information of an object in the learning area is output;
A learning model that causes a computer to function in response to an input of a second feature including a frequency spectrum of a second synthetic aperture radar received signal based on a reflected electromagnetic wave that is irradiated to a detection area and then reflected, to output second object information of an object in the detection area. The learning model according to claim 6 ,
The first characteristic amount further includes a signal strength of the first synthetic aperture radar received signal,
A learning model , wherein the second feature further includes a signal strength of the second synthetic aperture radar received signal . The learning model according to claim 6 or 7 ,
the first feature value further includes a phase of the first synthetic aperture radar received signal,
A learning model , wherein the second feature further includes a phase of the second synthetic aperture radar received signal . The learning model according to claim 1 or 6 ,
the first object information includes information indicating an attribute of an object in the learning area,
A learning model, wherein the second object information includes information indicating attributes of the object in the detection area. A storage unit in which the learning model according to claim 1 or 6 is stored;
A signal acquisition unit that acquires the second synthetic aperture radar received signal;
and an estimation unit that inputs the second synthetic aperture radar received signal to the learning model and infers the second object information. A storage unit in which the learning model according to claim 1 or 6 is stored;
A signal acquisition unit that acquires the second synthetic aperture radar received signal;
an estimation unit that inputs the second synthetic aperture radar received signal to the learning model and infers the second object information;
A flying object comprising: a signal output unit that outputs an output signal based on the second object information to the outside. The computer
acquiring a first synthetic aperture radar received signal based on a reflected electromagnetic wave that is reflected from an electromagnetic wave irradiated onto a learning area;
extracting a learning signal from the first synthetic aperture radar received signal, the first feature amount of which corresponds to a portion of the learning region, based on a predetermined condition in which the first feature amount of the first synthetic aperture radar received signal includes a feature amount based on an object in the learning region ;
acquiring object information of the object;
storing a pair of the feature amount of the learning signal and the object information as teacher data used for learning a learning model that causes a computer to function in such a way that, in response to an input of a feature amount of a second synthetic aperture radar reception signal based on a reflected electromagnetic wave of an electromagnetic wave irradiated to a detection area, a second object information of the object in the detection area corresponding to the feature amount of the second synthetic aperture radar reception signal is output;
A method for generating teacher data, comprising: The computer
acquiring, as a learning signal, a first synthetic aperture radar reception signal based on a reflected electromagnetic wave that is irradiated onto a learning region and reflected therefrom;
acquiring object information of an object in the learning area;
storing a set of the feature amount including the frequency spectrum of the learning signal and the object information as teacher data used for learning a learning model that causes a computer to function in such a way that, in response to an input of the feature amount including the frequency spectrum of a second synthetic aperture radar reception signal based on a reflected electromagnetic wave that is irradiated to a detection area, the computer outputs second object information of the object in the detection area according to the feature amount of the second synthetic aperture radar reception signal;
A method for generating teacher data, comprising:

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