Radar device, signal processing device, and signal processing method

Abstract

To provide a radar device that reduces noise and improves the detection performance of a moving target.SOLUTION: According to an embodiment, a radar device includes an antenna unit, a transmission / reception unit, and a signal processing unit. The transmission / reception unit transmits a radar pulse from the antenna unit, receives radio waves arriving at the antenna unit, and generates a reception signal. The signal processing unit processes the reception signal. The signal processing unit includes a pulse compression processing unit, a first high-sensitivity processing unit, a second high-sensitivity processing unit, and a detection processing unit. The pulse compression processing unit pulse-compresses the reception signal and generates a compression pulse signal. The first high-sensitivity processing unit reduces noise in the compression pulse signal and generates a first high-sensitivity signal. The second high-sensitivity processing unit reduces noise of the first high-sensitivity signal and generates a second high-sensitivity signal. The detection processing unit detects a target from the second high-sensitivity signal.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] An embodiment of the present invention relates to a radar device, a signal processing device, and a signal processing method. [Background technology]

[0002] For example, radar devices are known that are mounted on aircraft to detect approaching targets. For this type of radar device, it has been proposed to suppress clutter using a pattern classifier that applies machine learning, for example. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2021-015079 A Summary of the Invention [Problem to be solved by the invention]

[0004] When a target is far away or the RCS (Radar Cross Section) is small, the S / N (Signal to Noise ratio) of the received signal becomes small. Increasing the aperture area of ​​the antenna of the radar device or increasing the transmission power is effective for increasing the S / N, but the scale of the hardware increases. In addition, the S / N can be increased by increasing the number of pulses used for coherent integration, but the update rate may increase or the detection area may be limited. Furthermore, in the case of a target moving at high speed, the detection performance is significantly reduced. Therefore, it is necessary to improve the detection performance of a target moving at high speed without increasing the hardware scale of the radar antenna by using a signal processing method using machine learning that is different from the conventional method. However, in order to handle targets of various speeds, it is necessary to train a pattern classifier that can handle various speeds, which increases the scale of machine learning.

[0005] SUMMARY OF THE DISCLOSURE An object of the present invention is to provide a radar device, a signal processing device, and a signal processing method that reduce noise and have high performance in detecting moving targets. [Means for solving the problem]

[0006] According to an embodiment, a radar device includes an antenna unit, a transceiver unit, and a signal processor. The transceiver unit transmits radar pulses from the antenna unit and receives radio waves arriving at the antenna unit to generate a received signal. The signal processor processes the received signal. The signal processor includes a pulse compression processor, a first sensitivity enhancement processor, a second sensitivity enhancement processor, and a detection processor. The pulse compression processor pulse compresses the received signal to generate a compressed pulse signal. The first sensitivity enhancement processor reduces noise in the compressed pulse signal to generate a first sensitivity enhancement signal. The second sensitivity enhancement processor reduces noise in the first sensitivity enhancement signal to generate a second sensitivity enhancement signal. The detection processor detects a target from the second sensitivity enhancement signal. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a functional block diagram showing an example of a radar device according to each embodiment. [Diagram 2] FIG. 2 is a functional block diagram illustrating an example of a signal processing unit according to the first embodiment. [Diagram 3] FIG. 3 is a block diagram showing an example of the configuration of the sensitivity enhancement processing section 122 and the sensitivity enhancement processing section 123. As shown in FIG. [Figure 4] FIG. 4 is a flowchart illustrating an example of a target detection process by the signal processing unit in the first embodiment. [Diagram 5] FIG. 5 is a diagram for explaining the operation of the sensitivity enhancement processors 122 and 123. As shown in FIG. [Figure 6] FIG. 6 is a functional block diagram illustrating an example of a signal processing unit according to the second embodiment. [Figure 7] FIG. 7 is a functional block diagram illustrating an example of a signal processing unit according to the third embodiment. [Figure 8]FIG. 8 is a flowchart showing an example of a target detection process by the signal processing unit in the third embodiment. [Figure 9] FIG. 9 is a functional block diagram illustrating an example of a signal processing unit according to the fourth embodiment. [Figure 10] FIG. 10 is a functional block diagram illustrating an example of a signal processing unit according to the fifth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, the embodiments will be described with reference to the drawings. FIG. 1 is a functional block diagram showing an example of a radar device according to each embodiment. In the embodiments, a pulse radar is assumed. As shown in FIG. 1, the radar device 1 includes an antenna unit 10, a transmitting / receiving unit 11, and a signal processing unit 12. The radar device 1 may also include a display unit 13 for visually displaying the output of the signal processing unit 12 to present it to a user. Here, the user is, for example, a watchman who monitors a target using the radar device 1. The target is, for example, an object that moves in the air at a relatively high speed, such as a flying object or an aircraft. The target may or may not have a person on board. The target is not necessarily limited to an object that moves in the air.

[0009] The antenna unit 10 is, for example, an array antenna having a plurality of antenna elements arranged in a regular pattern. The antenna unit 10 transmits radio waves (radar waves) into space and receives the radio waves including reflected waves (received signals) from a target.

[0010] The transmission / reception unit 11 generates radar pulses and sends them to the antenna unit 10. The transmission / reception unit 11 also generates a received signal by performing processing such as amplification and analog / digital (A / D) conversion on the radio waves arriving at the antenna unit 10. The transmission / reception unit 11 sends the generated received signal to the signal processing unit 12.

[0011] The signal processing unit 12 is, for example, a computer equipped with a memory and a processor. The signal processing unit 12 performs various signal processing on the received signal from the transceiver unit 11. This signal processing includes, for example, clutter suppression, radar video generation, and target detection. The signal processing unit 12 sends information on the detected targets to the display unit 13.

[0012] The display unit 13 visually presents the target information to the user based on the target information input from the signal processing unit 12 .

[0013] [First embodiment] A first embodiment will be described. Fig. 2 is a functional block diagram showing an example of a signal processing unit according to the first embodiment. The signal processing unit 12 includes a pulse compression processing unit 121, a sensitivity enhancement processing unit 122, a sensitivity enhancement processing unit 123, and a detection processing unit .

[0014] The pulse compression processing unit 121 generates a compressed pulse signal by pulse-compressing the received signal from the transmitting / receiving unit 11. The pulse compression processing unit 121 outputs the compressed pulse signal to the sensitivity increasing processing unit 122. The compressed pulse signal is three-dimensional data including, for example, distance, azimuth, and elevation angle as axes. The compressed pulse signal may be four- or more-dimensional data including other elements such as Doppler.

[0015] The sensitivity enhancement processor 122 performs a first sensitivity enhancement process on the compressed pulse signal from the pulse compression processor 121 to generate a first sensitivity enhancement signal. The sensitivity enhancement processor 122 outputs the first sensitivity enhancement signal to the sensitivity enhancement processor 123. The first sensitivity enhancement signal is a signal in which signals other than a target signal component in the compressed pulse signal are reduced as noise. As will be described later, the sensitivity enhancement processor 122 performs a process of reducing noise in the compressed pulse signal using a trained model. Here, the compressed pulse signal may be input one pulse at a time to the sensitivity enhancement processor 122, or multiple compressed pulse signals, for example, in units of CPI (Coherent Processing Interval), may be input together.

[0016] The high sensitivity processing unit 123 performs a second high sensitivity processing on the first high sensitivity signal from the high sensitivity processing unit 122 to generate a second high sensitivity signal. The high sensitivity processing unit 123 outputs the second high sensitivity signal to the detection processing unit 124. The second high sensitivity signal is a signal in which signals other than the target signal component in the first high sensitivity signal have been reduced as noise. As will be described later, the high sensitivity processing unit 123 performs processing to reduce noise in the first high sensitivity signal using a trained model in the same way as the high sensitivity processing unit 122.

[0017] Detection processing unit 124 detects the target from the second high sensitivity signal from high sensitivity processing unit 123. In the second high sensitivity signal output from high sensitivity processing unit 123, the levels of signal components other than the target are reduced. Therefore, detection processing unit 124 can extract only the target.

[0018] 3 is a block diagram showing an example of the configuration of the high-sensitivity processing unit 122 and the high-sensitivity processing unit 123. The high-sensitivity processing unit 122 and the high-sensitivity processing unit 123 are, for example, computers having a physical form as hardware. The high-sensitivity processing unit 122 and the high-sensitivity processing unit 123 may be configured by a single computer, or may be configured by separate computers. In the following, the high-sensitivity processing unit 122 and the high-sensitivity processing unit 123 will be described as being configured by a single computer. The computer configuring the high-sensitivity processing unit 122 and the high-sensitivity processing unit 123 includes a processor 21 and a storage unit 24. Furthermore, the computer includes a ROM 22, a RAM 23, and a communication unit 25.

[0019] The processor 21 is a calculation device such as a central processing unit (CPU) or a micro processing unit (MPU). The processor 21 loads a program stored in the ROM 22 or the storage unit 24 into the RAM 23 and executes the program loaded into the RAM 23 to operate as the sensitivity enhancement processing unit 122 and the sensitivity enhancement processing unit 123.

[0020] The ROM 22 is a non-volatile memory such as a flash memory, and operates as a program memory. The ROM 22 stores programs executed by the processor 21, control data used by the processor 21, and the like.

[0021] The RAM 23 is a volatile memory such as a DRAM, and operates as a working memory that temporarily stores data. Programs are loaded into the RAM 23. The RAM 23 also temporarily stores data to be processed by the processor 21.

[0022] The storage unit 24 is a storage configured by a non-volatile memory such as an SSD (Solid State Drive). The storage unit 24 stores a program 24a executed by the processor 21, data 24b used by the processor 21, a trained model 24c, and a trained model 24d. The trained model 24c is a trained model for the sensitivity enhancement processing unit 122. The trained model 24c is a trained model in which the weights of each layer are set so as to input a compressed pulse signal and output a first high sensitivity signal in which noise components other than the target component contained in the compressed pulse signal are reduced. That is, the training of the trained model 24c is performed so as to distinguish between a target signal component and a noise signal component contained in the input compressed pulse signal and to output a signal in which the intensity of the signal component identified as noise is reduced. Limited training may be performed so that a margin is included in the signal range identified as the target in the compressed pulse signal. The trained model 24d is a trained model in which the weights of each layer are set so as to receive the first high-sensitivity signal as an input and output a second high-sensitivity signal in which noise components other than the target component contained in the first high-sensitivity signal are reduced. That is, the trained model 24d is trained to distinguish between the target signal components and noise signal components contained in the input first high-sensitivity signal and to output a signal in which the intensity of the signal components identified as noise is reduced. Limited training may be performed so that a margin is included in the signal range identified as the target in the first high-sensitivity signal. The margin of the signal range identified as the target in the first high-sensitivity signal may be narrower than the margin of the signal range identified as the target in the compressed pulse signal.

[0023] The communication unit 25 is connected to the pulse compression processing unit 121, and acquires a compressed pulse signal from the pulse compression processing unit 121. The communication unit 25 is also connected to the detection processing unit 124, and outputs a second sensitivity-enhanced signal to the detection processing unit 124.

[0024] The processor 21 also includes a learning unit 21a and a learning unit 21b. The processor 21 operates as the learning unit 21a and the learning unit 21b by executing processes according to instructions written in a program 24a.

[0025] The learning unit 21a generates a trained model by repeating machine learning using, as training data, the compressed pulse signal acquired from the pulse compression processing unit 121 via the communication unit 25. The generated trained model is stored in the storage unit 24 as the trained model 24c.

[0026] The learning unit 21b repeats machine learning using the first sensitivity-enhanced signal, which is the output of the sensitivity-enhancing processing unit 122 generated by the learning unit 21a, as training data to generate a trained model. A signal simulating the first sensitivity-enhanced signal may be used as the training data. The generated trained model is stored in the storage unit 24 as trained model 24d.

[0027] For example, the trained model 24c and the trained model 24d can be generated by repeatedly providing training data to a Convolutional Neural Network (CNN). This type of machine learning is called deep learning. In the first embodiment, the learning unit 21a repeats supervised machine learning using a compressed pulse signal as training data to generate the trained model 24c. The training data in this case is, for example, data of a compressed pulse signal including only a target. Also, in the first embodiment, the learning unit 21b repeats supervised machine learning using a first sensitivity-enhanced signal as training data to generate the trained model 24d. The training data in this case is, for example, data of a first sensitivity-enhanced signal including only a target.

[0028] Here, the learning data used in the learning for generating the trained model 24c may be created in consideration of the movement of the target between the compressed pulse signals. Similarly, the learning data used in the learning for generating the trained model 24d may be created in consideration of the movement of the target between the first high sensitivity signals.

[0029] Furthermore, the learning data used in learning to generate the trained models 24c and 24d may be data for each target velocity assumed for each Doppler filter bank.

[0030] The high sensitivity processing unit 122 and the high sensitivity processing unit 123 may be implemented as software. In this case, the high sensitivity processing unit 122 and the high sensitivity processing unit 123 may be configured as a program that is loaded into a RAM and executed by a processor.

[0031] In addition, the pulse compression processing unit 121 and the detection processing unit 124 may also be configured as dedicated hardware configured to execute the above-mentioned operations, or may be configured as software that causes a processor to execute the above-mentioned operations.

[0032] Next, the operation of the signal processing unit 12 of the first embodiment will be described. Fig. 4 is a flowchart showing an example of a target detection process by the signal processing unit 12 in the first embodiment. In step S1, the pulse compression processing unit 121 of the signal processing unit 12 performs pulse compression on the received signal from the transmitting / receiving unit 11 to generate a compressed pulse signal. When a high S / N signal is detected in the compressed pulse signal by a previous threshold process or the like, the target signal with this high S / N may be replaced with noise.

[0033] In step S2, the sensitivity enhancement processor 122 generates a first sensitivity enhancement signal from the compressed pulse signal. Specifically, the sensitivity enhancement processor 123 inputs the compressed pulse signal to the trained model 24c to obtain the first sensitivity enhancement signal.

[0034] In step S3, the sensitivity enhancement processor 123 generates a second sensitivity enhancement signal from the first sensitivity enhancement signal. Specifically, the sensitivity enhancement processor 123 inputs the first sensitivity enhancement signal to the trained model 24d to obtain the second sensitivity enhancement signal.

[0035] In step S4, the detection processing unit 124 detects a target from the second highly sensitive signal. For example, the detection processing unit 124 detects a signal range in the second highly sensitive signal having an intensity equal to or greater than a threshold as the target.

[0036] FIG. 5 is a diagram for explaining the operation of the sensitivity enhancement processing units 122 and 123. In FIG. 5, compressed pulse signals are input to the sensitivity enhancement processing unit 122 in a time series. The sensitivity enhancement processing unit 122 groups the input compressed pulse signals into a plurality of units. The compressed pulse signals of the plurality of units are, for example, M compressed pulse signals X(T), X(T-1), ..., X(T-M+1) including the compressed pulse signal X(T) at time T shown in FIG. 5. After grouping the compressed pulse signals into a plurality of units in this way, the sensitivity enhancement processing unit 122 performs a high sensitivity processing on the compressed pulse signals for each unit. (Equation 1) shows a first high sensitivity signal Y1(T) which is an output of the sensitivity enhancement processing unit 122 for M compressed pulse signals from time T. D1(X(T), X(T-1), ..., X(T-M+1)) in (Equation 1) shows that a noise reduction processing is performed on the input compressed pulse signals as a high sensitivity processing using the learned model 24c.

number

[0037] As described above, the sensitivity enhancement processor 122 may perform noise reduction processing as sensitivity enhancement processing on each compressed pulse signal, without grouping the input compressed pulse signals into a plurality of units.

[0038] After the high sensitivity processing in the high sensitivity processing unit 122, the high sensitivity processing unit 123 calculates the first high sensitivity signals Y1(T), Y1(TT s1 ),…,Y1(TT sN-1 ) is subjected to high sensitivity processing. (Equation 2) shows the second high sensitivity signal Y2(T) which is the output of the high sensitivity processing unit 123 for the first high sensitivity signal over the past N times. D2(Y1(T), Y1(TT) in (Equation 2) s1),…,Y1(TT sN-1 )) indicates that noise reduction processing is performed on the input first sensitivity-enhanced signal as sensitivity-enhancing processing using the trained model 24d.

number

[0039] As described above, according to the first embodiment, a noise reduction process is performed as a high sensitivity process on the compressed pulse signal, and a noise reduction process is further performed as a high sensitivity process on the first high sensitivity signal that has been made high sensitivity by the noise reduction process. In this way, in the first embodiment, a two-stage high sensitivity process is performed on the compressed pulse signal, so that a signal with higher sensitivity than that of the existing technology can be generated. A target detection process is performed using the high sensitivity signal, so that a target can be detected with a smaller number of pulse hits than that of the existing technology. Since a target can be detected with a smaller number of pulse hits, it is also suitable for detecting a target moving at high speed. Furthermore, a target detection process is performed by increasing the sensitivity of a low S / N signal. Therefore, it is not necessary to increase the S / N by increasing the aperture area or transmission power of the antenna. Therefore, the ease of installation is not impaired. In other words, in the first embodiment, when the RCS detects the same target, the same detection accuracy as that of the existing technology can be maintained even if the hardware scale is smaller than that of the existing technology.

[0040] Here, in the first embodiment, padding may be performed on the boundary portions of each compressed pulse signal. The padding is a process of expanding the boundary portions of the compressed pulse signal by a predetermined value, for example, data of 0. However, when padding is performed, there is a possibility that the performance of the high sensitivity processing on the compressed pulse signal may differ between the center portion and the boundary portion of the compressed pulse signal. Therefore, instead of expanding the boundary portion by data of 0, the boundary portion may be expanded by noise data. By expanding the boundary portion by noise data, the performance of the high sensitivity processing on the compressed pulse signal is made uniform between the center portion and the boundary portion of the compressed pulse signal. Here, the noise data may be generated by, for example, pseudorandom numbers that match the noise level of the received signal. Moreover, the noise data may be data of a fixed pattern that is stored in advance in the ROM 22 or the like. Furthermore, the noise data may be noise data obtained by performing noise measurement.

[0041] [Second embodiment] A second embodiment will be described. Fig. 6 is a functional block diagram showing an example of a signal processing unit according to the second embodiment. The signal processing unit 12 in the second embodiment includes a pulse compression processing unit 121, a sensitivity improvement processing unit 122, a sensitivity improvement processing unit 123, a detection processing unit 124, and an integration processing unit 125. Here, in Fig. 6, the same elements as in Fig. 2 are given the same reference symbols as in Fig. 2. Descriptions of the elements given the same reference symbols as in Fig. 2 will be simplified or omitted.

[0042] In the second embodiment, the pulse compression processing unit 121 outputs a compressed pulse signal to the sensitivity enhancement processing unit 122 and the integration processing unit 125 .

[0043] The sensitivity enhancement processor 122 is the same as that in the first embodiment. That is, the sensitivity enhancement processor 122 performs a first sensitivity enhancement process on the compressed pulse signal from the pulse compression processor 121 to generate a first sensitivity enhancement signal. The sensitivity enhancement processor 122 outputs the first sensitivity enhancement signal to the sensitivity enhancement processor 123.

[0044] The integration processing unit 125 generates an integration pulse signal by pulse integrating the compressed pulse signal from the pulse compression processing unit 121. The integration processing unit 125 outputs the integration pulse signal to the sensitivity improvement processing unit 123. For example, coherent integration may be used for the integration processing in the integration processing unit 125.

[0045] The high sensitivity processing unit 123 performs second high sensitivity processing on the first high sensitivity signal from the high sensitivity processing unit 122 and the integral pulse signal from the integration processing unit 125 to generate a second high sensitivity signal. The high sensitivity processing unit 123 outputs the second high sensitivity signal to the detection processing unit 124. The high sensitivity processing unit 123 performs processing to reduce noise in the first high sensitivity signal using a trained model, similar to the high sensitivity processing unit 122. Note that the high sensitivity processing unit 123 in the second embodiment may also be realized by the computer shown in FIG. 3.

[0046] In the second embodiment, the trained model 24d used in the sensitivity enhancement processing unit 123 is generated by repeating supervised machine learning using the first sensitivity enhancement signal and the integral pulse signal as training data. The training data in this case is, for example, data of the first sensitivity enhancement signal and the integral pulse signal including only the target.

[0047] The detection processing unit 124 is the same as that in the first embodiment. That is, the detection processing unit 124 detects a target from the second high sensitivity signal from the high sensitivity processing unit 123.

[0048] As described above, according to the second embodiment, the noise reduction process and the integration process are performed on the compressed pulse signal as the high sensitivity process, and the first high sensitivity signal and the integration pulse signal are input to perform the noise reduction process as the high sensitivity process. As with the first high sensitivity signal, the integration pulse signal can be said to be a high sensitivity compressed pulse signal. These two types of high sensitivity signals are used as learning data to perform learning in the high sensitivity processing unit 123. That is, the learning data can be increased more than in the first embodiment. In addition, by performing the high sensitivity process in the trained model 24d according to these two types of high sensitivity signals, it is expected that the sensitivity will be further increased than in the first embodiment.

[0049] Here, in the second embodiment as well, padding may be applied to the boundary portions of the respective compressed pulse signals, or the boundary portions may be expanded with noise data.

[0050] [Third embodiment] A third embodiment will be described. Fig. 7 is a functional block diagram showing an example of a signal processing unit according to the third embodiment. The signal processing unit 12 in the third embodiment includes a pulse compression processing unit 121, a sensitivity enhancement processing unit 122, a detection processing unit 124, and a probability output unit 126. Here, in Fig. 7, the same elements as in Fig. 2 are given the same reference symbols as in Fig. 2. Descriptions of the elements given the same reference symbols as in Fig. 2 will be simplified or omitted.

[0051] In the third embodiment, the sensitivity enhancement processing unit 123 is replaced with a probability output unit 126. Also, in the third embodiment, the compressed pulse signal generated by the pulse compression processing unit 121 is output to the detection processing unit 124 in addition to the sensitivity enhancement processing unit 122. The compressed pulse signal may be input to the sensitivity enhancement processing unit 122 and the detection processing unit 124 on a pulse-by-pulse basis, or a plurality of compressed pulse signals, for example, in CPI units, may be input collectively.

[0052] The probability output unit 126 performs probability output processing on the first sensitivity-enhanced signal from the sensitivity-enhancing processing unit 122 to generate a probability signal. The probability signal is a signal that indicates the distribution of target-likeliness in the first sensitivity-enhanced signal as a distribution of the probability that the corresponding signal component is a target signal component. The probability output unit 126 performs processing to generate the probability signal using a trained model. The probability output unit 126 may also be realized by the computer shown in FIG. 3.

[0053] The learning unit 21b in the third embodiment repeats machine learning using, as learning data, the first sensitivity-enhanced signal that is the output of the sensitivity-enhancing processing unit 122 generated by the learning unit 21a, to generate a trained model 24d. The teacher data in this case is, for example, target map data indicating a target signal range in the input first sensitivity-enhanced signal and noise map data indicating a noise signal range in the input first sensitivity-enhanced signal.

[0054] The detection processing unit 124 detects a target by extracting a signal component of the target from the compressed pulse signal from the pulse compression processing unit 121 based on the probability signal from the probability output unit 126. The detection processing unit 124 generates a binary signal by binarizing the probability signal, for example, using a threshold value. The binary signal is, for example, a signal that is 1 when the probability of being the target is equal to or greater than the threshold value, and is 0 when the probability is less than the threshold value. Then, the detection processing unit 124 matches the binary signal with the compressed pulse signal by calculating the product of the binary signal and the compressed pulse signal. The detection processing unit 124 detects a signal range in which the product of the binary signal and the compressed pulse signal is not 0 as a target.

[0055] Here, the threshold value used when binarizing the probability signal may be set appropriately. For example, if the threshold value is small, the signal range detected as a target is likely to be wide. Therefore, the target is likely to be extracted from the compressed pulse signal. On the other hand, if it is necessary to further improve the target detection accuracy, the threshold value may be set to a large value.

[0056] Next, the operation of the signal processing unit 12 of the third embodiment will be described. Fig. 8 is a flowchart showing an example of a target detection process by the signal processing unit 12 in the third embodiment. In step S11, the pulse compression processing unit 121 of the signal processing unit 12 performs pulse compression on the received signal from the transmission / reception unit 11 to generate a compressed pulse signal.

[0057] In step S12, the sensitivity enhancement processor 122 generates a first sensitivity enhancement signal from the compressed pulse signal. Specifically, the sensitivity enhancement processor 123 inputs the compressed pulse signal to the trained model 24c to obtain the first sensitivity enhancement signal.

[0058] In step S13, the probability output unit 126 generates a probability signal from the first sensitivity-enhanced signal. Specifically, the sensitivity-enhancing processing unit 123 inputs the first sensitivity-enhanced signal to the trained model 24d to obtain a probability signal.

[0059] In step S14, the detection processing unit 124 detects a target from the compressed pulse signal and the probability signal. The detection processing unit 124 calculates the product of the compressed pulse signal and a binarized signal of the probability signal, for example. Then, the detection processing unit 124 detects a signal range in which the product is not 0 as a target.

[0060] As described above, according to the third embodiment, a noise reduction process is performed as a high sensitivity process on the compressed pulse signal, and a probability signal output process is performed to represent a distribution of target likelihood in the first high sensitivity signal that has been high sensitivity through the noise reduction process. Then, a target is detected using the compressed pulse signal and the probability signal. In this way, in the third embodiment, a probability output process is performed on the compressed pulse signal instead of the second-stage high sensitivity process. Even in this case, a target can be detected with a smaller number of pulse hits than in the existing technology, as in the first embodiment. Since a target can be detected with a smaller number of pulse hits, it is also suitable for detecting a target moving at high speed. Furthermore, a target detection process is performed by increasing the sensitivity of a low S / N signal. Therefore, it is not necessary to increase the S / N by increasing the aperture area or transmission power of the antenna. Therefore, the ease of installation is not impaired. In other words, in the first embodiment, when the RCS detects the same target, the same detection accuracy as that of the existing technology can be maintained even if the hardware scale is smaller than that of the existing technology.

[0061] Here, in the third embodiment as well, padding may be applied to the boundary portions of the respective compressed pulse signals, or the boundary portions may be expanded with noise data.

[0062] [Fourth embodiment] A fourth embodiment will be described. Fig. 9 is a functional block diagram showing an example of a signal processing unit according to the fourth embodiment. The signal processing unit 12 in the fourth embodiment includes a pulse compression processing unit 121, a sensitivity enhancement processing unit 122, a detection processing unit 124, and a probability output unit 126a. Here, in Fig. 9, the same elements as in Fig. 2 are given the same reference symbols as in Fig. 2. Descriptions of the elements given the same reference symbols as in Fig. 2 will be simplified or omitted.

[0063] In the fourth embodiment, a sensitivity enhancement processor 122 and a probability output section 126a are provided between a pulse compression processor 121 and a detection processor .

[0064] The sensitivity enhancement processor 122 in the fourth embodiment generates a first sensitivity enhancement signal from the compressed pulse signal. The sensitivity enhancement processor 122 outputs the first sensitivity enhancement signal to the detection processor .

[0065] The probability output unit 126a generates a probability signal by performing a probability output process on the compressed pulse signal from the pulse compression processing unit 121. The probability output unit 126a performs a process of generating a probability signal using a trained model. The probability output unit 126a may be realized by the computer shown in FIG.

[0066] The learning unit 21b in the fourth embodiment repeats machine learning using the compressed pulse signal as learning data to generate a learned model 24d. In this case, the teacher data is, for example, target map data indicating a target signal range in the input compressed pulse signal and noise map data indicating a noise signal range in the input compressed pulse signal.

[0067] The detection processing unit 124 detects a target by extracting a signal component of the target from the first high sensitivity signal from the high sensitivity processing unit 122 based on the probability signal from the probability output unit 126a. The detection processing unit 124 generates a binary signal by binarizing the probability signal using, for example, a threshold value. Then, the detection processing unit 124 matches the binary signal with the first high sensitivity signal by calculating the product of the binary signal and the first high sensitivity signal. The detection processing unit 124 detects a signal range in which the product of the binary signal and the first high sensitivity signal is not zero as a target.

[0068] Here, the threshold value used when binarizing the probability signal may be set appropriately. For example, if the threshold value is small, the signal range detected as a target is likely to be wide. Therefore, the target is likely to be extracted from the compressed pulse signal. On the other hand, if it is necessary to further improve the detection accuracy, the threshold value may be set to a large value.

[0069] As described above, according to the fourth embodiment, even in the case where a target is detected by matching the first high-sensitivity signal with a probability signal, the target can be detected with a smaller number of pulse hits than the existing technology, as in the first embodiment. Since the target can be detected with a smaller number of pulse hits, it is also suitable for detecting targets moving at high speed. Furthermore, the target detection process is performed by increasing the sensitivity of a low S / N signal. Therefore, it is not necessary to increase the S / N by increasing the aperture area or transmission power of the antenna. Therefore, the ease of installation is not impaired. In other words, in the first embodiment, when the RCS detects the same target, the same detection accuracy as the existing technology can be maintained even if the hardware scale is smaller than that of the existing technology.

[0070] Here, in the fourth embodiment, the probability output section 126a generates a probability signal from a compressed pulse signal. In contrast, the probability output section 126a may be configured to generate a probability signal from a first high sensitivity signal, as in the third embodiment. Also, the probability output section 126a may be configured to generate a probability signal from an integral pulse signal generated by the integral processing section 125 described in the second embodiment.

[0071] Furthermore, the detection processing unit 124 may detect a target by matching the second sensitivity-enhanced signal with the probability signal instead of the first sensitivity-enhanced signal. In this case, the sensitivity-enhanced processing unit 123 is provided between the sensitivity-enhanced processing unit 122 and the detection processing unit 124.

[0072] Furthermore, in the fourth embodiment as well, padding may be applied to the boundary portions of the respective compressed pulse signals, or the boundary portions may be expanded with noise data.

[0073] [Fifth embodiment] A fifth embodiment will be described. Fig. 10 is a functional block diagram showing an example of a signal processing unit according to the fifth embodiment. The signal processing unit 12 in the fifth embodiment includes a pulse compression processing unit 121, a detection processing unit 124, and a probability output unit 126a. Here, in Fig. 10, the same elements as in Fig. 2 are given the same reference symbols as in Fig. 2. Descriptions of the elements given the same reference symbols as in Fig. 2 will be simplified or omitted.

[0074] In the fifth embodiment, a probability output section 126a is provided between the pulse compression processing section 121 and the detection processing section .

[0075] The probability output section 126a performs probability output processing on the compressed pulse signal from the pulse compression processing section 121 to generate a probability signal, similar to the probability output section 126a in the fourth embodiment.

[0076] The detection processing unit 124 detects a target by extracting a signal component of the target from the compressed pulse signal from the pulse compression processing unit 121 based on the probability signal from the probability output unit 126a. The detection processing unit 124 may detect a target by performing threshold processing on the probability signal from the probability output unit 126a.

[0077] Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, and are included in the scope of the invention and its equivalents described in the claims. [Explanation of symbols]

[0078] 1...radar device, 10...antenna unit, 11...transmitter / receiver unit, 12...signal processing unit, 13...display unit, 21...processor, 21a...learning unit, 21b...learning unit, 22...ROM, 23...RAM, 24...memory unit, 24a...program, 24b...data, 24c...learned model, 24d...learned model, 25...communication unit, 121...pulse compression processing unit, 122...high sensitivity processing unit, 123...high sensitivity processing unit, 124...detection processing unit, 125...integration processing unit, 126...probability output unit, 126a...probability output unit.

Claims

1. An antenna portion; a transceiver unit that transmits radar pulses from the antenna unit and receives radio waves arriving at the antenna unit to generate a received signal; A signal processing unit that processes the received signal; Equipped with The signal processing unit includes: a pulse compression processing unit that performs pulse compression on the received signal to generate a compressed pulse signal; a first sensitivity enhancement processor that reduces noise in the compressed pulse signal to generate a first sensitivity enhancement signal; a second sensitization processor that reduces noise in the first sensitized signal to generate a second sensitized signal; a detection processing unit that detects a target from the second sensitivity-enhanced signal; A radar device comprising:

2. The first sensitivity enhancement processing unit includes a first learning unit that generates a first trained model by repeating machine learning using the compressed pulse signal, The second sensitivity enhancement processing unit includes a second learning unit that generates a second trained model by repeating machine learning using the first sensitivity enhancement signal. The radar device according to claim 1 .

3. The signal processing unit further includes an integration processing unit that performs pulse integration on the compressed pulse signal to generate an integrated pulse signal, the second sensitivity enhancement processing unit generates the second sensitivity enhancement signal from the integral pulse signal and the first sensitivity enhancement signal; The radar device according to claim 1 .

4. 4. The radar device according to claim 1, wherein a boundary portion of the compressed pulse signal is expanded by a noise signal.

5. An antenna portion; a transceiver unit that transmits radar pulses from the antenna unit and receives radio waves arriving at the antenna unit to generate a received signal; a signal processing unit for processing the received signal; Equipped with The signal processing unit includes: a pulse compression processing unit that performs pulse compression on the received signal to generate a compressed pulse signal; a sensitization processing unit that reduces noise in the compressed pulse signal to generate a sensitized signal; a probability output unit that calculates a probability of the sensitized signal being a target and generates a probability signal; a detection processing unit that detects a target from the compressed pulse signal and the probability signal; A radar device comprising:

6. The sensitivity enhancement processing unit includes a first learning unit that generates a first trained model by repeating machine learning using the compressed pulse signal, The probability output unit includes a second learning unit that generates a second trained model by repeating machine learning using the sensitized signal. The radar device according to claim 5 .

7. An antenna portion; a transceiver unit that transmits radar pulses from the antenna unit and receives radio waves arriving at the antenna unit to generate a received signal; a signal processing unit for processing the received signal; Equipped with The signal processing unit includes: a pulse compression processing unit that performs pulse compression on the received signal to generate a compressed pulse signal; a sensitization processing unit that reduces noise in the compressed pulse signal to generate a sensitized signal; a probability output unit that calculates a probability of the compressed pulse signal being a target and generates a probability signal; a detection processing unit that detects a target from the high sensitivity signal and the probability signal; A radar device comprising:

8. The sensitivity enhancement processing unit includes a first learning unit that generates a first trained model by repeating machine learning using the compressed pulse signal, The probability output unit includes a second learning unit that generates a second trained model by repeating machine learning using the compressed pulse signal. The radar device according to claim 7.

9. The radar device according to any one of claims 2, 6 and 8, wherein the machine learning is deep learning in which learning data is repeatedly provided to a convolutional neural network to generate the first trained model and the second trained model.

10. In a signal processing device for processing a received signal of a radar device, a pulse compression processing unit that performs pulse compression on the received signal to generate a compressed pulse signal; a first sensitivity enhancement processor that reduces noise in the compressed pulse signal to generate a first sensitivity enhancement signal; a second sensitization processor that reduces noise in the first sensitized signal to generate a second sensitized signal; a detection processing unit that detects a target from the second sensitivity-enhanced signal; A signal processing device comprising:

11. In a signal processing device for processing a received signal of a radar device, a pulse compression processing unit that performs pulse compression on the received signal to generate a compressed pulse signal; a sensitization processing unit that reduces noise in the compressed pulse signal to generate a sensitized signal; a probability output unit that calculates a probability of the sensitized signal being a target and generates a probability signal; a detection processing unit that detects a target from the compressed pulse signal and the probability signal; A signal processing device comprising:

12. A signal processing method for processing a received signal of a radar device by a computer, comprising: the computer pulse compressing the received signal to generate a compressed pulse signal; The computer reduces noise in the compressed pulse signal to generate a first sensitized signal; the computer reducing noise in the first sensitized signal to generate a second sensitized signal; detecting a target from the second sensitized signal; A signal processing method comprising:

13. A signal processing method for processing a received signal of a radar device by a computer, comprising: the computer pulse compressing the received signal to generate a compressed pulse signal; The computer reduces noise in the compressed pulse signal to generate a sensitized signal; The computer calculates a probability of the compressed pulse signal being a target to generate a probability signal; said computer detecting a target from said sensitized signal and said probability signal; A signal processing method comprising:

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