LEARNING DEVICE, LEARNING METHOD, RECORDING MEDIUM, AND RADAR DEVICE

The learning device learns a tracking model used for a radar device. A first acquisition unit acquires, from a radar device, a target detection information indicating a position of a target detected based on a received wave, and a track of the target calculated based on the target detection information. A second acquisition unit acquires a target position information indicating a position of the target. A learning data generation unit generates learning data using the target detection information, the track, and the target position information. A learning processing unit learns a tracking model for performing tracking processing of the target based on the target detection information, using the learning data.

TECHNICAL FIELD

The present invention relates to a monitoring technique using a radar.

BACKGROUND ART

There is known a technique for monitoring a moving object such as an aircraft using radar. Patent Document 1 discloses a method for monitoring a moving target such as an aircraft or a vehicle by a radar device.

PRECEDING TECHNICAL REFERENCES

Patent Document

Patent Document 1: Japanese Patent Application Laid-Open under No. 2016-151416

SUMMARY

Problem to Be Solved by The Invention

In a radar device having a tracking function, it is necessary to use a tracking filter having both high tracking accuracy and high trackability in order to track a high mobility target performing steep turning or the like. However, it is difficult to realize such a tracking filter actually. In addition, when tracking a target, correlation processing is performed in which the target and the plots are associated. However, in an area where a large amount of clutter exists, there is a fear that the erroneously detected clutter may be associated with the target, resulting in a decrease in tracking accuracy.

One object of the present invention is to realize a radar device capable of performing tracking processing with high tracking accuracy and high trackability.

Means for Solving The Problem

According to an example aspect of the present invention, there is provided a learning device comprising:a first acquisition unit configured to acquire, from a radar device, a target detection information indicating a position of a target detected based on a received wave, and a track of the target calculated based on the target detection information;a second acquisition unit configured to acquire a target position information indicating a position of the target;a learning data generation unit configured to generate learning data using the target detection information, the track, and the target position information; anda learning processing unit configured to learn a tracking model for performing tracking processing of the target based on the target detection information, using the learning data.

According to another example aspect of the present invention, there is provided a learning method comprising:acquiring, from a radar device, a target detection information indicating a position of a target detected based on a received wave, and a track of the target calculated based on the target detection information;acquiring a target position information indicating a position of the target;generating learning data using the target detection information, the track, and the target position information; andlearning a tracking model for performing tracking processing of the target based on the target detection information, using the learning data.

According to still another example aspect of the present invention, there is provided a recording medium recording a program, the program causing a computer to execute processing of:acquiring, from a radar device, a target detection information indicating a position of a target detected based on a received wave, and a track of the target calculated based on the target detection information;acquiring a target position information indicating a position of the target;generating learning data using the target detection information, the track, and the target position information; andlearning a tracking model for performing tracking processing of the target based on the target detection information, using the learning data.

According to still another example aspect of the present invention, there is provided a radar device comprising:a target detection unit configured to transmit a transmission wave, and generate a target detection information indicating a position of the target based on a reception wave corresponding to the transmission wave; anda tracking processing unit configured to perform tracking processing of the target based on the target detection information using a tracking model, the tracking model being learned using learning data generated based on the target detection information, the track of the target calculated based on the target detection information, and the target position information indicating the position of the target.

Effect of The Invention

According to the present invention, it is possible to realize a radar device capable of performing tracking processing with high tracking accuracy and high trackability.

EXAMPLE EMBODIMENTS

Preferred example embodiments of the present invention will be described with reference to the accompanying drawings. The radar device in the example embodiments can be used in a monitoring system of moving objects present in the surroundings. Specifically, the radar device detects a moving object (hereinafter, also referred to as a “target”) by emitting transmission waves to the surroundings and receiving the reflected waves thereof, and tracks the target if necessary. Targets include, for example, aircrafts flying in the air, vehicles traveling on the ground, and ships traveling over the sea. In the following example embodiments, for convenience of description, it is supposed that radar device is used for air traffic control and the target is primarily an aircraft.

Basic Configuration of Radar Device

First, the basic configuration of the radar device will be described.FIG.1is a block diagram showing a basic configuration of a radar device. The radar device100includes an antenna unit101, a transceiver unit102, a signal processing unit103, a beam control unit104, a target detection unit105, a tracking processing unit106, and a display operation unit107.

The antenna unit101amplifies an electric signal inputted from the transceiver unit102(hereinafter, also referred to as “transmission signal”), and emits a transmission wave (referred to as “beam”) in the transmission direction instructed by the beam control unit104. Also, the antenna unit101converts the reflected wave of the emitted transmission wave reflected by the target to an electric signal (hereinafter, also referred to as “reception signal”), synthesizes the electric signals and outputs a synthesized signal to the transceiver unit102.

In this example embodiment, the radar device100emits a beam (referred to as a “scan beam”) that constantly scans all directions (ambient 360° ) to monitor the presence of a target in the surroundings. Also, if a target is detected, the radar device100emits a beam (referred to as a “tracking beam”) to track that target and tracks the trajectory of the target (referred to as a “track”). From this point, the antenna unit101is constituted by an antenna capable of changing the transmission direction instantaneously, such as an array antenna comprising a plurality of antenna elements. Specifically, a plurality of planar array antennas may be arranged to cover all directions, or a cylindrical array antenna may be used. Thus, it is possible to emit the tracking beam in the direction of the target when the target is detected, while constantly emitting the scan beam in all directions.

The transceiver unit102generates the electric signal based on the transmission wave specification instructed by the beam control unit104(hereinafter, also referred to as beam specification), and outputs the electric signal to the antenna unit101. The beam specification includes the pulse width of the transmission wave, the transmission timing, and the like. Also, the transceiver unit102A/D-converts the reception signal inputted from the antenna unit101, removes the unnecessary frequency band therefrom, and outputs it to the signal processing unit103as a reception signal.

The signal processing unit103applies demodulation processing and integration processing to the reception signal inputted from the transceiver unit102, and outputs the reception signal after the processing (hereinafter, also referred to as “processed signal”) to the target detection unit105.FIG.2is a block diagram showing a configuration of the signal processing unit103. The signal processing unit103includes a demodulation processing unit110, and a coherent integration unit111. The demodulation processing unit110demodulates (performs pulse compression of) the reception signal inputted from the transceiver unit102. Essentially, sharp transmission waves (transmission pulses) with high power are required to detect distant targets by radar, but there is a limit to power enhancement due to constraints such as hardware. Therefore, at the time of emitting the beam, the transceiver unit102generates the transmission waves of long duration by frequency-modulating the transmission signals having a predetermined pulse width, and transmits them from the antenna unit101. Correspondingly, the demodulation processing unit110demodulates the reception signal inputted from the transceiver unit102to generate the sharp reception pulses, and outputs them to the coherent integration unit111.

The coherent integration unit111removes noise by coherently integrating the plural pulses inputted from the demodulation processing unit110, thereby to improve the SNR. The radar device100emits a plurality of pulses in the same direction (in the same azimuth and the same elevation angle) in order to detect the target with high accuracy. The number of pulses emitted in the same direction is called “hit number”. The coherent integration unit111integrates the reception signal (the reception pulses) of the beam of a predetermined hit number emitted in the same direction, and thereby improves the SNR of the reception signal. Incidentally, the number of the reception pulses integrated by the coherent integration unit111is also referred to as “integration pulse number”. The integration pulse number is basically equal to the hit number of the emitted beam.

Returning toFIG.1, the target detection unit105detects the target from the processed signal inputted from the signal processing unit103using a predetermined threshold. The target detection unit105measures the distance, the azimuth, and the elevation of the target, and outputs them as the target detection result (hereinafter, referred to as “plot”) to the tracking processing unit106. The plot includes the distance, the azimuth, the elevation, and the SNR of the target. Further, the target detection unit105sets the threshold value for detecting the target, based on the threshold setting value inputted from the display operation unit107.

The tracking processing unit106performs tracking processing for a plurality of plots inputted from the target detection unit105and calculates the track of the target. Specifically, the tracking processing unit106predicts the position of the target at the current time (referred to as “estimated target position”) based on the plurality of plots, and outputs it to the display operation unit107. Further, the tracking processing unit106calculates the predicted position of the target (referred to as “predicted target position”) based on the plurality of plots and outputs it to the beam control unit104. The predicted target position indicates the position where the radar device100irradiates the tracking beam next.

Specifically, the tracking processing unit106performs correlation processing and tracking filtering. The correlation processing is processing of associating a plurality of plots acquired by the target detection unit105with the target. When multiple targets are detected at the same time, it is determined which target each of the acquired multiple plots corresponds to, and each plot is associated with each target. The tracking filtering calculates the track of the target using the plots associated with the target. Thus, the estimated target position indicating the current position of the target and the predicted target position indicating the predicted position of the target in the future are obtained.

The beam control unit104determines the transmission direction and the beam specification of the scan beam according to a preset beam schedule. Further, the beam control unit104determines the transmission direction and the beam specification of the tracking beam based on the predicted target position inputted from the tracking processing unit106. Then, the beam control unit104outputs the transmission directions of the scan beam and the tracking beam to the antenna unit101, and outputs the beam specification of the scan beam and the tracking beam to the transceiver unit102.

The display operation unit107includes a display unit such as a display, and an operation unit such as a keyboard, a mouse, and operation buttons. The display operation unit107displays the positions of the plurality of plots inputted from the target detection unit105, and the predicted target position inputted from the tracking processing unit106. This allows the operator to see the current position and/or the track of the detected target. Further, by operating the display operation unit107, the operator can input the threshold used for the target detection to the target detection unit105or input the clutter determination result that the signal processing unit103uses for demodulation processing to the signal processing unit103. Incidentally, the “clutter” is a signal generated by the emitted radar reflected by the object other than the target. Out of the plurality of plots displayed on the display operation unit107, the operator can determine an area that is considered to be clutter due to experience, and operate the display operation unit107to designate the area. This is called “clutter determination”.

With the above configuration, the radar device100detects the target by constantly emitting the scan beam in all directions, and emits the tracking beam to the predicted target position to track the target when the target is detected.

First Example Embodiment

In a radar device having a tracking function, it is necessary to use a tracking filter having both high tracking accuracy and high trackability in order to track a high mobility target performing steep turning or the like. However, it is difficult to realize such a tracking filter actually. Also, when tracking a target, correlation processing is performed in which the target and the plots are associated. However, in an area where a large amount of clutter exists, there is a fear that the erroneously detected clutter may be associated with the target, resulting in a decrease in tracking accuracy. In this view, in the present example embodiment, tracking processing is performed using a model generated by machine learning. Specifically, the tracking model is learned using the plots obtained by the target detection unit105and the teacher labels (correct labels) for the plots, and the learned tracking model is applied to the tracking processing unit. Thus, it becomes possible to improve the tracking performance while suppressing the cost.

Configuration at The Time of Learning

Whole Configuration

FIG.3shows a configuration of a radar device300and a learning device200according to the present example embodiment. At the time of learning a tracking model, a radar device300including a primary radar (PSR: Primary Surveillance Radar) and a secondary radar (SSR: Secondary Surveillance Radar) is used. As shown, the radar device300includes a PSR antenna unit301, a PSR transceiver unit302, a PSR signal processing unit303, a beam control unit304, a PSR target detection unit305, a tracking processing unit306, and a display operation unit307. These units have the same configuration as the antenna unit101, the transceiver unit102, the signal processing unit103, the beam control unit104, the target detection unit105, the tracking processing unit106, and the display operation unit107shown inFIG.1, and operate in the same manner. Further, the radar device300includes an SSR antenna unit308, an SSR transceiver unit309, and an SSR target detection unit310.

The SSR transceiver unit309outputs an interrogation signal to the SSR antenna unit308, and the SSR antenna unit308transmits an interrogation wave to the target. Further, the SSR antenna unit308receives the reply wave to the interrogation wave from the target and outputs the reply signal to the SSR transceiver unit309. The SSR transceiver unit309performs A/D conversion or the like of the reply signal and outputs it to the SSR target detection unit310. Normally, the reply signal includes the position information of the target, and the SSR target detection unit310generates a plot D2of the target (referred to as an “SSR plot”) based on the reply signal and outputs it to the tracking processing unit306. The tracking processing unit306generates a track D3of the target using the plot D1of the target (referred to as a “PSR plot”) detected by the PSR target detection unit305and the SSR plot D2. The PSR plot is an example of the primary radar plot, and the SSR plot is an example of the secondary radar plot.

The learning device200is provided to learn a tracking model to be applied to the tracking processing unit. The learning device200includes a learning data generation unit201, a data collection unit202, and a learning processing unit204. The learning data generation unit201receives the PSR plot D1from the PSR target detection unit305, receives the SSR plot D2from the SSR target detection unit310, and receives the track D3from the tracking processing unit306. The learning data generation unit201generates a teacher label relating to the track of the target using the SSR plot D2and the track D3. Specifically, the teacher label includes the position, speed, and acceleration of the target, as well as information indicating true target/false target (true/false of the target). Incidentally, “true target” refers to a correct target such as an aircraft, and “false target” refers to an object misrecognized as a target, such as a clutter.

As described above, since the tracking processing unit306performs the correlation processing and the tracking filtering as the tracking processing, the tracking model to be learned is also configured as a model for performing the correlation processing and the tracking filtering. The learning data generation unit201first generates a teacher label indicating whether each PSR plot is the true target or the false target using the SSR plot D2. Since the SSR plot D2is obtained for the target that replied to the interrogation signal, the learning data generation unit201basically assigns a teacher label of “a true target” to the PSR plot D1for which the corresponding SSR plot D2exists, and assigns a teacher label of “a false target” to the PSR plot D1for which the corresponding SSR plot D2does not exist. By using the generated true/false target teacher labels in the learning, the tracking model becomes possible to determine whether the inputted PSR plot D1is a true target or a false target. Thus, it is possible to prevent that the plot obtained by erroneously detecting a clutter or the like is associated with the target in the correlation processing, thereby enabling stable tracking.

Further, the learning data generation unit201generates a teacher label such as the position, the speed, and the acceleration of the target for each PSR plot D1on the basis of the SSR plot D2and the track D3. If a target such as an aircraft has transmitted a reply signal including its own position, the position can be used as a fairly accurate target position. Further, the learning data generation unit201can determine the speed, acceleration, and the like of the target corresponding to the PSR plot D1on the basis of the track D3. By learning the model using the teacher labels such as the position, speed, and acceleration of the target thus generated, the tracking model can learn the motion performed by various targets, and it becomes possible to perform the tracking processing with high tracking accuracy and high trackability.

The learning data generation unit201uses a pair of the PSR plot and the teacher label generated for the PSR plot in the above manner as the learning data and outputs the pair to the data collection unit202. The data collection unit202stores the learning data inputted from the learning data generation unit201. The data collection unit202stores the learning data to which the teacher labels of the position, speed, acceleration, and true/false of the target are given, for each PSR plot. The learning processing unit204acquires the learning data from the data collection unit202to learn the tracking model and generates the learned tracking model.

Hardware Configuration of Learning Device

FIG.4is a block diagram illustrating a hardware configuration of the learning device200illustrated inFIG.3. As illustrated, the learning device200includes an input IF (InterFace)21, a processor22, a memory23, a recording medium24, and a database (DB)25.

The input IF21inputs and outputs data to and from the radar device300. Specifically, the input IF21acquires the PSR plot S1, the SSR plot D2and the track D3from the radar device300. The processor22is a computer including a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and the like, and controls the entire learning device200by executing a program prepared in advance. The processor22functions as the learning data generation unit201and the learning processing unit204shown inFIG.3.

The memory23is composed of ROM (Read Only Memory), RAM (Random Access Memory), and the like. The memory23stores various programs to be executed by the processor22. The memory23is also used as a work memory during the execution of various processes by the processor22.

The recording medium24is a non-volatile, non-transitory recording medium such as a disk-shaped recording medium, a semiconductor memory, or the like, and is configured to be detachable from the learning device200. The recording medium24records various programs to be executed by the processor22. When the learning device200performs processing, a program recorded on the recording medium24is loaded into the memory23and executed by the processor22.

The DB25stores data inputted through the input IF21and data generated by the learning device200. Specifically, the DB25stores the PSR plots D1, the SSR plots D2and the tracks D3inputted from the radar device300, as well as the learning data generated by the learning data generation unit201.

Learning Processing

FIG.5is a flowchart of learning processing performed by the learning device200. This processing can be implemented by the processor22shown inFIG.4, which executes a program prepared in advance and operates as each element shown inFIG.3.

First, the learning data generation unit201acquires the PSR plots D1outputted by the PSR target detecting unit305of the radar device300(step S11). Also, the learning data generation unit201acquires the SSR plots D2outputted by the SSR target detecting unit310and the tracks D3outputted by the tracking processing unit306(step S12). Next, the learning data generation unit201generates the learning data including the PSR plots D1and the teacher labels corresponding to the PSR plots D1using the PSR plots D1, the SSR plots D2, and the tracks D3, and stores the learning data in the data collection unit202(step S13). Next, the learning processing unit204learns the tracking model using the inputted learning data (step S14).

Next, the learning processing unit204determines whether or not a predetermined learning end condition is satisfied (step S15). An example of the learning end condition is that learning using a predetermined amount of learning data or learning of a predetermined number of times has been completed. The learning processing unit204repeats the learning until the learning end condition is satisfied. When the learning end condition is satisfied, the processing ends.

Other Examples of Learning Devices

FIG.6shows another example of the learning device. The learning device200shown inFIG.3generates the learning data using the PSR plots, the SSR plots and the tracks, that is, the actual data generated in the actual radar device300. Instead, learning data may be prepared by simulation and learning may be performed.FIG.6shows a learning device200ausing simulation data. The learning device200aincludes a simulation data generation unit205and a learning processing unit204. The simulation data generation unit205generates learning data (simulation data) including the PSR plots in various situations and the teacher labels corresponding thereto by simulation and inputs the learning data to the learning processing unit204. The learning processing unit204is basically the same as that shown inFIG.3and learns the tracking model using the simulation data. Incidentally, the example ofFIG.3and this example may be combined to learn one tracking model by using the learning data generated based on the actual data of the radar device300together with the simulation data.

Radar Device Using The Tracking Model

Configuration

FIG.7is a block diagram showing a configuration of a radar device300xto which a learned tracking model is applied. As can be seen from comparison withFIG.3, the radar device300xincludes a tracking processing unit314instead of the tracking processing unit306inFIG.3. Since the configuration other than the tracking processing unit314is the same as that inFIG.3, the description thereof will be omitted.

A learned tracking model generated by the learning processing described above is set to the tracking processing unit314. The tracking processing unit314detects the target from the inputted PSR plots using the learned tracking model. Specifically, the tracking processing unit314calculates the track of the target from the PSR plots using the tracking model. Then, the tracking processing unit314outputs the estimated target position to the display operation unit307and outputs the predicted target position to the beam control unit304.

Tracking Processing

FIG.8is a flowchart of tracking processing by the radar device300x. First, the tracking processing unit314acquires the PSR plots from the PSR target detection unit305(step S21). Next, the tracking processing unit314calculates the track of the target using the learned tracking model based on the acquired PSR plots (step S22), and outputs the track of the detected target (step S23).

As described above, in the present example embodiment, by learning the tracking model using the teacher labels of the true target/false target for the PSR plots, the tracking processing unit314can determine whether the inputted PSR plot is a true target or a false target. Thus, it is possible to prevent that the plot obtained by erroneously detecting a clutter or the like is associated with the target in the correlation process. Further, by learning the tracking model using the teacher labels such as the position, speed, and acceleration of the target with respect to the PSR plots, the tracking processing unit314can learn the motion performed by the various targets, and both tracking accuracy and trackability may be improved.

In the example ofFIG.7, the learned tracking model is applied to the radar device including the SSR shown inFIG.3. Instead, the learned tracking model may be applied to the radar device only having the PSR as shown inFIG.1.FIG.9shows an example in which a learned tracking model is applied to the radar device100xonly for PSR. As can be understood by comparing withFIG.1, the radar device100xis the same as the radar device100illustrated inFIG.1except that a learned tracking model is used in the tracking processing unit114.

Generation of Learning Data

SNR Deterioration

Learning of the tracking model may be performed using learning data with deteriorated SNR, for example, by intentionally adding noise to the PSR plots used as input data when learning the tracking model. This makes it possible to generate a tracking model with high accuracy even in environments with low SNR.

Method of Generating Teacher Labels

When the learning data is generated in the learning device200, the teacher label indicating the target position or the true target/false target can be generated in the following manner.

(A)Use of Secondary Radar

As described in the above-described example embodiment, first, the learning data generation unit201may generate the teacher label based on the reply signal received from the target when the target has responded to the interrogation signal transmitted from the secondary radar. Specifically, the learning data generation unit201may use the PSR plot corresponding to the target that has transmitted the reply signal as the true target and use the self-position of the target included in the reply signal as the target position.

In the case of using the secondary radar, it is not ensured that the reply is acquired from all targets. In the case of an air defense radar or the like, aircrafts detected as the targets include military aircrafts or the like in addition to passenger aircrafts. An aircraft whose identity has been identified, such as a passenger aircraft and a military aircraft of their own country (hereinafter referred to as “a friendly aircraft”) responds to the interrogation signal, but an aircraft whose identity cannot be identified, such as a military aircraft of other countries (hereinafter referred to as “an unknown aircraft”) do not respond to the interrogation signal. Therefore, a teacher label cannot be generated for the reception signal including the unknown aircraft as the target. However, in the case of the air defense radar or the like, what we really want to detect and track is the unknown aircraft rather than the friendly aircraft.

In this view, a correct answer is generated for the unknown aircraft by the following method. As a premise, it is assumed that the targets that can be detected as the targets from the reception signals are classified to three classes: “clutter (including noise)”, “friendly aircraft” and “unknown aircraft”. It is noted that, when the target is “clutter”, it means that the target does not actually exist but the clutter is erroneously detected as the target. Here, the class that replies to the interrogation signal of the secondary radar and is given the teacher label (given the correct answer) is only the “friendly aircraft”. In addition, the characteristics of the reception signals are similar for both “unknown aircraft” and “friendly aircraft” because they are actually aircrafts.

Under the above premise, the unknown aircraft is detected by the following procedure.

First, by using the reception signals of “friendly aircraft”, the learning data generation unit201generates a model for extracting the reception signals of “clutter”, “friendly aircraft”, and “unknown aircraft” from all the reception signals (Process 1).

Next, using the reception signals of “clutter”, “friendly aircraft”, and “unknown aircraft” thus extracted, the learning data generation unit201determines the reception signal having a characteristic close to the “friendly aircraft” among the reception signals that are not determined to be “friendly aircraft” (i.e., the reception signals determined to be “clutter” or “unknown aircraft”) to be “unknown aircraft”, and generates “unknown aircraft label” (Process 2).

Then, the learning data generation unit201generates a model for detecting “unknown aircraft” from the reception signals using the reception signals determined to be “unknown aircraft” and the “unknown aircraft label” (Process 3).

By this model, it becomes possible to detect unknown aircrafts which do not reply to the interrogation signal of the secondary radar from the reception signals.

In reality, it is conceivable that the accuracy of the “unknown aircraft label” generated in the above-described Process 2 becomes a problem. In that case, the “unknown aircraft label” may be given by hand of an operator or the like. By this method, it is sufficient to manually perform labelling of “unknown aircraft” only for the reception signals having a characteristic close to the “friendly aircraft” among the reception signals determined to be “clutter” or “unknown aircraft” extracted in the above-described Process 2. In other words, it is sufficient to manually perform labelling after narrowing down the reception signals to those having a high possibility of “unknown aircraft” by Process 1 and Process 2. Therefore, compared with the case where manual labeling is performed on the reception signals including all of “clutter”, “friendly aircraft”, and “unknown aircraft”, the amount of manual work can be remarkably reduced.

(B) Use of Other Radar Devices

In the above example embodiment, SSR is used to acquire the position of the target and generate the teacher labels. However, when there are multiple radar devices, the learning data generation unit201may generate the teacher labels using the plots and the tracks acquired from other radar devices. Further, the learning data generation unit201may generate the teacher labels using the track (passive track) of the passive radar that only performs reception. Incidentally, the “passive track” is a result of tracking the jamming transmitter based on the jamming wave, and the learning data generation unit201can generate the estimated position of the jamming transmitter as the teacher label using the passive track.

(C) Use of Other Measurement Device

If the target aircraft is equipped with a positioning device such as GPS, the output may be received to generate a teacher label. The same applies when the target is a drone. In addition, a stereo camera or the like may be used to estimate the position of the target from the captured image of the target to generate the teacher label. Incidentally, when the target is a ship, the ship information may be received from the automatic vessel identification device (AIS: Automatic Identification System), and the position of the target may be acquired to generate the teacher label.

The operator may apply a teacher label by viewing the plots, track, or the like displayed on the display operation unit107.

In Case of Radar Mounted on Mobile Body

In the above example embodiment, it is assumed that the radar device is installed on the ground. However, the method of the present example embodiment is also applicable to a radar device mounted on a mobile body such as an aircraft or a ship. In that case, as an input parameter used by the tracking model, the mobile body information (the position, the posture, the speed, the course and the like of the mobile body itself) relating to the mobile body on which the radar device is mounted may be used. Specifically, the mobile body information is inputted to the learning data generating device201, and the learning processing unit204performs learning of the model using the mobile body information as the learning data, in addition to the PSR plots. In the radar device100xor300xto which the learned model is applied, the mobile body information may be inputted to the tracking processing unit114or314, and the tracking processing unit114or314may perform tracking processing using the mobile body information.

Efficient Data Collection by Radar Device

As mentioned previously, it is difficult to collect the learning data necessary for learning of the tracking model for rarely occurring situations. Therefore, the radar device300performs beam control for collection of learning data during the beam schedule. Particularly, if the pre-specified condition is satisfied, the radar device300performs the beam control intensively. The content of the beam control is changed to match the data to be collected.

FIG.10shows a configuration to perform the beam control for collection of learning data. The radar device300has the same configuration as inFIG.3. Meanwhile, the learning device200includes a data collection control unit215in addition to the configuration shown inFIG.3. The data collection control unit215stores a condition in which the learning data is insufficient, and outputs a data collection request D5including the condition of the data to be collected to the beam control unit304of the radar device300. During the beam schedule, the beam control unit304controls the antenna unit301to emit a beam under the condition indicated by the data collection request D5. The radar device300constantly monitors all directions by the scan beam and tracks the target by the tracking beam when the target is detected. Therefore, the beam control unit304can emit a beam for collecting learning data, when a target is not detected or when there is no need to track the target, for example. The reflected wave corresponding to the emitted beam is received by the antenna unit301, and the reception signal is inputted to the PSR target detection unit305through the PSR transceiver unit302and the PSR signal processing unit303. The PSR plots generated by the PSR target detection unit305are outputted to the learning data generation unit201. Thus, the learning device200can collect data corresponding to the condition in which data is insufficient.

Application of Learned Model

When the learned tracking model (hereinafter, simply referred to as a “learned model”) generated by the learning device200is actually applied to the radar device100, the operation of the radar device100needs to be stopped because rewriting the program or the like occurs. However, the radar device performing important monitoring cannot be stopped. Therefore, the learned model cannot be applied, and the on-line learning is difficult.

In this view, the control/data processing unit of the radar device is doubled in advance. For convenience of explanation, description will be given of the case where the learned model is applied to the radar device only having a PSR radar.FIG.11shows a configuration of a radar device and a learning device for performing on-line learning. As illustrated, the radar device100aincludes an antenna unit101, a transceiver unit102, a switching unit120, and two control/data processing units121aand121b. The control/data processing units121aand121bare units including a signal processing unit103, a beam control unit104, a target detection unit105, a tracking processing unit106, and a display operation unit107of the radar device shown inFIG.1. The switching unit120selectively connects one of the control/data processing units121aand121bto the antenna unit101and the transceiver unit102. In addition, the switching unit120outputs the data D6including the reception signals, the plots, the track, and the like to the learning data generation unit201of the learning device200afrom the control/data processing unit121aor121bin operation.

The learning device200aincludes a learning result evaluation unit220and a learning result application unit221in addition to the learning data generation unit201, the data collection unit202, and the learning processing unit204. The learning result evaluation unit220evaluates the learned model generated by the learning processing unit204, and outputs the learned model determined to be applicable to the radar device100ato the learning result application unit221. The learning result application unit221applies the learned model determined to be applicable to the control/data processing units121aand121b.

It is now assumed that the control/data processing unit121ais in the active state, i.e., during the actual monitoring operation, and the control/data processing unit121bis in the standby state. Namely, the switching unit120is connecting the control/data processing unit121ato the antenna unit101and the transceiver unit102. In this case, the learning device200alearns the tracking model using the data D6outputted from the control/data processing unit121ain the active state. During this time, the learning result applying unit221applies the learned model determined to be applicable to the control/data processing unit121bin the standby state and rewrites the program.

Next, the switching unit120sets the control/data processing unit121bto the active state, sets the control/data processing unit121ato the standby state, and applies a new learned model to the control/data processing unit121ain the standby state. In this way, it is possible to learn the tracking model while continuing the monitoring operation on one of the control/data processing units121aand121band apply the learned model to the other of the control/data processing units121aand121b. Namely, it becomes possible to apply the learned model and to carry out the on-line learning.

Evaluating Model Validity

In the on-line learning, it is difficult to judge how much the learning should be made to ensure the appropriate radar function, i.e., the validity. Further, there is a fear that the tracking processing unit to which the learned model is applied may operate in an unexpected manner, e.g., it erroneously detects a clutter that is not erroneously detected by a conventional processing, and recovery at that time is required. Therefore, the validity of the learned model is judged by operating the control/data processing unit to which the learned model is applied and the control/data processing unit in which the conventional processing is performed in parallel and comparing the processing results of them.

FIG.12shows a configuration of a radar device and a learning device for performing validity evaluation of the learned model. As shown, the radar device100bincludes an antenna unit101, a transceiver unit102, a validity evaluation unit130, and two control/data processing units131and132. The control/data processing unit131performs the conventional processing, and the control/data processing unit132performs processing using the learned model. The control/data processing units131and132include a signal processing unit103, a beam control unit104, a target detection unit105, a tracking processing unit106, and a display operation unit107of the radar device shown inFIG.1. The learning device200ais the same as that shown inFIG.11.

The validity evaluation unit130compares the processing result of the conventional processing performed by the control/data processing unit131with the processing result of the learned model performed by the control/data processing unit132to determine the validity of the processing result of the learned model. When it is determined that the processing result of the learned model is not appropriate, the validity evaluation unit130outputs the processing result of the conventional processing to the antenna unit101and the transceiver unit102. On the other hand, when it is determined that the processing result of the learned model is appropriate, the validity evaluation unit130outputs the processing result of the learned model to the antenna unit101and the transceiver unit102. Even when it is determined that the processing result of the learned model is appropriate, the validity evaluation unit130may interpolate the processing result of the learned model with the processing result of the conventional processing to prevent an unexpected operation from occurring. Further, the validity evaluation unit130may be generated using machine learning or the like. Further, it is not necessary that the processing of the validity evaluation unit130is fully automatic, and the operator may be interposed. For example, the operator may determine the validity of the processing result of the learned model based on the information displayed on the display operation unit107.

Suppressing Operational Fluctuation in Using The Learned Model

When the learned model is applied to the target detection unit, the operation of the radar device100may change significantly. Therefore, the control/data processing unit of the radar device100is doubled in advance, the learned model is applied with intentionally shifting the time of applying the learned model, and the results of the processing of the two control/data processing units are integrated to be adopted as a formal processing result.

FIG.13shows a configuration of a radar device and a learning device for suppressing operational fluctuation by the learned model. As illustrated, the radar device100cincludes an antenna unit101, a transceiver unit102, an integration unit140, and two control/data processing units141aand141b. The control/data processing unit141auses the old model, and the control/data processing unit141buses the new model to perform processing. The control/data processing units141aand141bare units including the signal processing unit103, the beam control unit104, the target detection unit105, the tracking processing unit106, and the display operation unit107of the radar device shown inFIG.1. The learning device200ais the same as that shown inFIG.11.

The integration unit140integrates the processing results of the control/data processing units141aand141band employs the integrated result as a formal processing result. For example, the integrating unit140adds the processing results from the control/data processing units141aand141b, divides the result of the addition by 2, and employs the result as the processing result. Thus, it becomes possible to suppress that the operation of the radar device fluctuates greatly when a new learned model is applied.

Second Example Embodiment

FIG.14Ais a block diagram illustrating a functional configuration of a learning device according to a second example embodiment. A learning device50of the second example embodiment includes a first acquisition unit51, a second acquisition unit52, a learning data generation unit53, and a learning processing unit54. The first acquisition unit51acquires, from a radar device, a target detection information indicating a position of a target detected based on a received wave, and a track of the target calculated based on the target detection information. The second acquisition unit52acquires a target position information indicating a position of the target. The learning data generation unit53generates learning data using the target detection information, the track, and the target position information. The learning processing unit54learns a tracking model for performing tracking processing of the target based on the target detection information, using the learning data.

FIG.14Bis a block diagram illustrating a functional configuration of a radar device according to a second example embodiment. The radar device60includes a target detection unit61, and a tracking processing unit62. The target detection unit61transmits a transmission wave, and generates a target detection information indicating a position of the target based on a received wave corresponding to the transmission wave. The tracking processing unit62performs tracking processing of the target based on the target detection information using a tracking model. The tracking model is learned using learning data generated based on the target detection information, the track of the target calculated based on the target detection information, and the target position information indicating the position of the target.

A part or all of the example embodiments described above may also be described as the following supplementary notes, but not limited thereto.

Supplementary Note 1

A learning device comprising:a first acquisition unit configured to acquire, from a radar device, a target detection information indicating a position of a target detected based on a received wave, and a track of the target calculated based on the target detection information;a second acquisition unit configured to acquire a target position information indicating a position of the target;a learning data generation unit configured to generate learning data using the target detection information, the track, and the target position information; anda learning processing unit configured to learn a tracking model for performing tracking processing of the target based on the target detection information, using the learning data.

Supplementary Note 2

The learning device according to Supplementary note 1, wherein the learning data generation unit generates a teacher label indicating whether the target indicated by the target detection information is a true target or a false target, based on the target position information.

Supplementary Note 3

The learning device according to Supplementary note 1 or 2, wherein the learning data generation unit generates a teacher label including a position, a speed, and an acceleration of the target indicated by the target detection information, based on the target position information and the track.

Supplementary Note 4

The learning device according to any one of Supplementary notes 1 to 3,wherein the target detection information is a primary radar plot generated by a primary radar of the radar device, andwherein the target position information is a secondary radar plot generated by a secondary radar.

Supplementary Note 5

The learning device according to Supplementary note 4, wherein the secondary radar plot is generated by the radar device that has generated the primary radar plot.

Supplementary Note 6

The learning device according to Supplementary note 4, wherein the secondary radar plot is generated by a radar device different from the radar device that generated the primary radar plot.

Supplementary Note 7

The learning device according to any one of Supplementary notes 1 to 6, wherein the target position information is self-position information of the target included in a received signal from the target.

Supplementary Note 8

The learning device according to any one of Supplementary notes 1 to 7, wherein the target position information is generated based on a captured image of the target by a stereo camera.

Supplementary Note 9

The learning device according to any one of Supplementary notes 1 to 8, further comprising a request unit configured to transmit a transmission wave matching a predetermined condition and request the radar device to generate the target detection information corresponding to the condition.

Supplementary Note 10

The learning device according to any one of Supplementary notes 1 to 9,wherein the radar device is mounted on a mobile body,wherein the learning device further comprises a third acquisition unit configured to acquire mobile body information including a position and movement information of the mobile body, andwherein the learning data generation unit generates the learning data using the mobile body information.

Supplementary Note 11

A learning method comprising:acquiring, from a radar device, a target detection information indicating a position of a target detected based on a received wave, and a track of the target calculated based on the target detection information;acquiring a target position information indicating a position of the target;generating learning data using the target detection information, the track, and the target position information; andlearning a tracking model for performing tracking processing of the target based on the target detection information, using the learning data.

Supplementary Note 12

A recording medium recording a program, the program causing a computer to execute processing of:acquiring, from a radar device, a target detection information indicating a position of a target detected based on a received wave, and a track of the target calculated based on the target detection information;acquiring a target position information indicating a position of the target;generating learning data using the target detection information, the track, and the target position information; andlearning a tracking model for performing tracking processing of the target based on the target detection information, using the learning data.

Supplementary Note 13

A radar device comprising:a target detection unit configured to transmit a transmission wave, and generate a target detection information indicating a position of the target based on a reception wave corresponding to the transmission wave; anda tracking processing unit configured to perform tracking processing of the target based on the target detection information using a tracking model, the tracking model being learned using learning data generated based on the target detection information, the track of the target calculated based on the target detection information, and the target position information indicating the position of the target.

While the present invention has been described with reference to the example embodiments and examples, the present invention is not limited to the above example embodiments and examples. Various changes which can be understood by those skilled in the art within the scope of the present invention can be made in the configuration and details of the present invention.

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