Signal processing method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a signal processing method and device, electronic equipment and a storage medium. The method comprises the following steps: receiving a current radar echo signal, and identifying current interference characteristic information of a current interference signal in the current radar echo signal; inputting the current interference feature information into a pre-trained strategy determination model, and based on an output result of the strategy determination model, obtaining a current anti-interference algorithm strategy for processing the current interference signal, so as to carry out anti-interference processing on the current radar echo signal according to the current anti-interference algorithm strategy; wherein the strategy determination model comprises a deep reinforcement learning model. According to the technical scheme provided by the embodiment of the invention, the current anti-interference algorithm strategy can be quickly and accurately determined without manual participation, and the current radar echo signal is subjected to anti-interference processing according to the current anti-interference algorithm strategy, so that the efficiency and accuracy of anti-interference processing are improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of signal processing technology, and in particular to a signal processing method, device, electronic device and storage medium. Background Art

[0002] With the evolution of electromagnetic spectrum interference technology becoming more complex, smart and intelligent, the interference scenarios faced by radar systems have become more complex and changeable. It is very important for radar systems to perform anti-interference processing on received signals.

[0003] In the prior art, an anti-interference strategy is usually determined manually, so that the received echo signal is subjected to anti-interference processing by the determined anti-interference strategy. However, in the process of implementing the present invention, it is found that the prior art has at least the following technical problems: determining the anti-interference strategy manually results in low interference suppression efficiency and is prone to errors, resulting in poor accuracy of the determined anti-interference strategy, which affects the anti-interference effect. Summary of the invention

[0004] The embodiments of the present invention provide a signal processing method, device, electronic device and storage medium, so as to quickly and accurately determine the current anti-interference algorithm strategy through a strategy determination model, which is conducive to improving the efficiency and accuracy of anti-interference processing.

[0005] According to one aspect of the present invention, there is provided a signal processing method, comprising:

[0006] Receiving a current radar echo signal, and identifying current interference feature information of a current interference signal in the current radar echo signal;

[0007] Inputting the current interference feature information into a pre-trained strategy determination model, and acquiring a current anti-interference algorithm strategy for processing the current interference signal based on an output result of the strategy determination model, so as to perform anti-interference processing on the current radar echo signal according to the current anti-interference algorithm strategy;

[0008] Wherein, the strategy determination model includes a deep reinforcement learning model.

[0009] According to another aspect of the present invention, there is provided a signal processing device, comprising:

[0010] A signal receiving module, used to receive the current radar echo signal and identify the current interference feature information of the current interference signal in the current radar echo signal;

[0011] An information input module is used to input current interference feature information into a pre-trained strategy determination model, and based on the output result of the strategy determination model, obtain the current anti-interference algorithm strategy for processing the current interference signal, so as to perform anti-interference processing on the current radar echo signal according to the current anti-interference algorithm strategy; wherein the strategy determination model includes a deep reinforcement learning model.

[0012] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the signal processing method described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the signal processing method described in any embodiment of the present invention when executed.

[0017] The technical solution of the embodiment of the present invention receives a current radar echo signal and identifies current interference feature information of a current interference signal in the current radar echo signal; by inputting the current interference feature information into a pre-trained strategy determination model, based on the output result of the strategy determination model, a current anti-interference algorithm strategy for processing the current interference signal is obtained; wherein the strategy determination model includes a deep reinforcement learning model; through the strategy determination model, the current anti-interference algorithm strategy can be determined quickly and accurately without human intervention, and the current radar echo signal can be subjected to anti-interference processing according to the current anti-interference algorithm strategy, which is beneficial to improving the efficiency and accuracy of anti-interference processing.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 is a flow chart of a signal processing method provided according to an embodiment of the present invention;

[0021] Figure 2 is a flowchart of another signal processing method provided according to an embodiment of the present invention;

[0022] Figure 3 A radar interference suppression training framework diagram based on deep reinforcement learning is provided according to an embodiment of the present invention;

[0023] Figure 4 is a structural schematic diagram of a signal processing device provided according to an embodiment of the present invention;

[0024] Figure 5 It is a schematic diagram of the structure of an electronic device for implementing the signal processing method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "etc." and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Figure 1 1 is a flow chart of a signal processing method provided according to an embodiment of the present invention. This embodiment is applicable to the case of performing anti-interference processing on a received radar echo signal. The method can be executed by a signal processing device, which can be implemented in the form of hardware and / or software.

[0028] like Figure 1 As shown, the method of this embodiment may specifically include:

[0029] S110: Receive a current radar echo signal, and identify current interference feature information of a current interference signal in the current radar echo signal.

[0030] It should be noted that the radar system can transmit the current radar transmission signal to the surrounding environment. The current radar transmission signal is reflected after encountering a target in the surrounding environment. The radar system determines the received reflected echo signal as the current radar echo signal. The current radar echo signal includes echo signals of multiple targets, interference signals and environmental noise. The current interference feature information is the feature information of the interference signal contained in the current radar echo signal.

[0031] In a specific implementation, the current radar echo signal can be received by the radar system, and the interference signal in the current radar echo signal can be identified, and the characteristic information of the interference signal in the current radar echo signal can be used as the current interference characteristic information. The received current radar echo signal u(t) can be expressed as:

[0032]

[0033] Among them, N ant Indicates the number of receiving antennas. For example, it may include 8 main antennas and 4 auxiliary antennas, for a total of 12 antennas. k,m (t) represents the echo signal of the kth target on the mth antenna. i,m (t) represents the signal of the i-th interference signal on the m-th antenna. m (t) represents the ambient noise on the mth antenna. tar Indicates the number of targets. N int Indicates the number of interference sources.

[0034] Optionally, identifying current interference characteristic information of the current interference signal in the current radar echo signal includes: determining the corresponding multiple signal classification spectrum in the current radar echo signal based on a multiple signal classification algorithm; identifying the current interference type and current characteristic parameters of the current interference signal in the current radar echo signal; and determining the multiple signal classification spectrum, the current interference type and the current characteristic parameters as the current interference characteristic information.

[0035] The current interference type includes at least one of the main-side lobe interference type, the suppression-forwarding interference type and the pulse-continuous wave interference type; the current characteristic parameter includes frequency and / or amplitude. The main-side lobe interference type can be used to distinguish whether the interference signal is the main lobe interference or the side lobe interference, the suppression-forwarding interference type is used to distinguish whether the interference signal is the suppression interference or the forwarding interference, and the pulse-continuous wave interference type is used to distinguish whether the interference signal is the pulse interference or the continuous interference.

[0036] Specifically, the Multiple Signal Classification (MUSIC) algorithm solves the arrival angle information of each radiation source and outputs a multiple signal classification spectrum, which includes the received signal strength information at the angle corresponding to the radiation source arrival angle information.

[0037] Furthermore, when determining the interference type of the current interference signal, the main lobe and side lobe interference types can be determined first. For the main lobe and side lobe interference types, the difference in directivity between the main and auxiliary beams of the radar is used to set the main and auxiliary antenna receiving gains of the side lobe arrival signal to make an interference decision. Exemplarily, the ratio of the first average power of the auxiliary beam receiving interference to the second average power of the main beam receiving interference is determined. If the ratio is greater than or equal to the preset judgment threshold value, the type of the interference signal is determined to be side lobe interference; if the ratio is less than the preset judgment threshold value, the type of the interference signal is determined to be main lobe interference.

[0038] When determining whether the current interference signal is a continuous wave interference type, the first signal over-threshold ratio of the current interference signal can be determined based on the average amplitude of the base noise and the absolute threshold of continuous wave interference detection, and the first signal over-threshold ratio is compared with the preset over-absolute threshold ratio. When the first signal over-threshold ratio is greater than or equal to the preset over-absolute threshold ratio, it is determined that the current interference signal has continuous wave interference, and the interference type can be a continuous wave interference type; when the first signal over-threshold ratio is less than the preset over-absolute threshold ratio, it is determined that the current interference signal does not have continuous wave interference. When determining whether the current interference signal is a pulse interference, the current interference signal can be background normalized first. And, based on the pulse detection relative background threshold, the second signal over-threshold ratio of the current interference signal is determined, and the second signal over-threshold ratio is compared with the preset pulse interference judgment ratio threshold. If the second signal over-threshold ratio is greater than or equal to the pulse interference judgment ratio threshold, it is determined that the current interference signal has pulse interference, and the interference type can be determined to be a pulse interference type. If the second signal over-threshold ratio is less than the pulse interference judgment threshold, it is determined that the current interference pulse signal does not have pulse interference.

[0039] Furthermore, forwarding interference is manifested in the form of pulses in the pulse pressure output domain. Therefore, for the detection and classification of dense forwarding interference, it is only necessary to perform the same processing as pulse interference detection on the pulse pressure output. Based on the relative background threshold of dense forwarding interference detection, the signal relative threshold ratio is determined. If the signal relative threshold ratio is greater than or equal to the preset relative threshold ratio, it is determined that there is dense forwarding interference in the current interference signal; if the signal relative threshold ratio is less than the preset relative threshold ratio, it is determined that there is no dense forwarding interference in the current interference signal.

[0040] In this embodiment, the determination result of the current interference type can be reflected in the form of a combination sequence, which includes a logical value corresponding to each interference type, that is, the main lobe interference type, the side lobe interference type, the suppressed interference type, the forwarding interference type, the pulse interference type, and the continuous interference type each correspond to a logical value, and the logical value is used to determine whether the current interference signal has this type of interference. Exemplarily, the logical value includes two values, 1 or 0, 1 indicates that the current interference signal has this interference type, and 0 indicates that the current interference signal does not have this interference type. For example, when the logical value corresponding to the main lobe interference type is 1, it means that the main lobe interference exists in the current interference signal; the logical value corresponding to the suppressed interference type is 0, which means that the suppressed interference does not exist in the current interference signal.

[0041] In this embodiment, the multiple signal classification spectrogram, the current interference type and the current characteristic parameters are determined as the current interference characteristic information, so as to facilitate a more comprehensive understanding of the characteristics of the interference signal. By using the multiple signal classification algorithm, it is convenient to accurately identify the source and characteristics of the interference signal, provide more abundant information for subsequent interference suppression, and help improve the accuracy of interference identification and the universality of the interference identification method, so as to facilitate the response to complex and changeable electromagnetic environments.

[0042] S120. Input the current interference feature information into a pre-trained strategy determination model, and based on the output result of the strategy determination model, obtain the current anti-interference algorithm strategy for processing the current interference signal, so as to perform anti-interference processing on the current radar echo signal according to the current anti-interference algorithm strategy; wherein the strategy determination model includes a deep reinforcement learning model.

[0043] In a specific implementation, the current interference feature information includes a multiple signal classification spectrum, a current interference type, and a current feature parameter. The multiple signal classification spectrum, the current interference type, and the current feature parameter can be respectively input into the trained deep reinforcement learning model. The hidden layer of the deep reinforcement learning model includes a convolutional neural network layer for extracting feature information of the multiple signal classification spectrum, and a fully connected layer for extracting feature information of the current interference type and the current feature parameter. The fully connected layer outputs the extracted features to the connection layer as a feature channel output layer. The connection layer integrates different types of output feature information and transmits it to subsequent hidden layers for further feature extraction. The final output layer is a fully connected layer for outputting the output result of the strategy determination model. The output result includes the current anti-interference algorithm strategy for processing the current interference signal. Exemplarily, when there is pulse interference in the current interference signal, the current anti-interference algorithm strategy is a strategy for processing pulse interference.

[0044] In this embodiment, before inputting the current interference characteristic information into the pre-trained strategy determination model, it also includes: obtaining the current state information of the radar corresponding to the current radar echo signal; inputting the current interference characteristic information into the pre-trained strategy determination model, including: inputting the current state information and the current interference characteristic information into the pre-trained strategy determination model to obtain the next radar transmission parameters based on the output result of the strategy determination model, and in response to the transmission trigger operation of the next radar transmission signal, transmitting the next radar transmission signal according to the next radar transmission parameters.

[0045] The current status information includes at least one of the working mode, the transmission carrier frequency, the transmission bandwidth, the radar antenna elevation angle and the sector range.

[0046] In order to reduce the interference of the state information of the radar when transmitting the signal to the echo signal, the current state information of the radar corresponding to the current radar echo signal can be obtained before the current interference feature information is input into the strategy determination model, and the current state information and the current interference feature information are input into the strategy determination model to obtain the output result. The output result may include the current anti-interference algorithm strategy and the radar transmission parameter adjustment information; the current state information can be adjusted by the radar transmission parameter adjustment information to obtain the next radar transmission parameter. Among them, the next radar transmission parameter is the state information corresponding to the radar before the next signal is transmitted. Alternatively, the output result may include the current anti-interference algorithm strategy and the next radar transmission parameter.

[0047] In a specific implementation, the next radar transmission parameter can be determined based on the output result. In the scenario of continuous signal transmission, in response to the transmission trigger operation of the next radar transmission signal, the next transmission signal can be generated based on the adjustment of the next radar transmission parameter, and the next transmission signal can be transmitted. In addition, the current interference signal is processed by the determined current anti-interference algorithm strategy.

[0048] This embodiment uses the current interference feature information and the current state information as input, which can provide a more comprehensive observation space state for the strategy determination model, so that the strategy determination model can comprehensively consider the current interference feature information and the current state information, thereby not only being able to determine the current anti-interference algorithm strategy, but also being able to determine the next radar transmission parameters, thereby improving the adaptability and flexibility of the interference suppression strategy, and being able to more effectively cope with complex and changing electromagnetic environments. In addition, it helps the radar to quickly respond to new interference situations, optimizes the transmission and reception of radar signals, and significantly enhances the intelligence level and dynamic adaptability of the radar system.

[0049] The technical solution of the embodiment of the present invention receives a current radar echo signal and identifies current interference feature information of a current interference signal in the current radar echo signal; by inputting the current interference feature information into a pre-trained strategy determination model, based on the output result of the strategy determination model, a current anti-interference algorithm strategy for processing the current interference signal is obtained; wherein the strategy determination model includes a deep reinforcement learning model; through the strategy determination model, the current anti-interference algorithm strategy can be determined quickly and accurately without human intervention, and the current radar echo signal can be subjected to anti-interference processing according to the current anti-interference algorithm strategy, which is beneficial to improving the efficiency and accuracy of anti-interference processing.

[0050] Figure 2 is a flow chart of another signal processing method provided according to an embodiment of the present invention. In this embodiment, before inputting the current interference feature information into the pre-trained strategy model, the sample interference feature information corresponding to the sample radar echo signal is input into the deep reinforcement learning model to be trained to obtain the strategy determination model. Among them, the explanations of the terms that are the same or corresponding to the above embodiments are not repeated here.

[0051] like Figure 2 As shown, the method includes:

[0052] S210. Input the sample interference feature information corresponding to the sample radar echo signal into the deep reinforcement learning model to be trained for action decision-making; perform anti-interference processing on the sample radar echo signal based on the training anti-interference algorithm strategy output by the deep reinforcement learning model to be trained to obtain a processed echo signal; determine the target reward value corresponding to the training anti-interference algorithm strategy based on the processed echo signal and a preset target reward function.

[0053] It should be noted that the strategy determination model can be trained based on the jammer and the electromagnetic environment simulation system. The radar simulation system generates a sample radar transmission signal and transmits the sample radar transmission signal. The jammer generates interference so that the radar simulation system receives the interfered sample radar echo signal.

[0054] In a specific implementation, the sample interference feature information corresponding to the sample radar echo signal can be determined, and the sample interference feature information includes a multiple signal classification spectrum, interference type and feature parameters corresponding to the sample radar echo signal. The sample interference feature information can be input into the deep reinforcement learning model to be trained to make action decisions, and output the training anti-interference algorithm strategy corresponding to the sample radar echo signal. The sample radar echo signal is subjected to anti-interference processing according to the training anti-interference algorithm strategy, and the signal obtained after the anti-interference processing is used as the processed echo signal.

[0055] In this embodiment, based on the processed echo signal, the sample radar echo signal and the preset target reward function, the target reward value corresponding to the training anti-interference algorithm strategy is determined, including: determining the detection target point information and the signal receiving power corresponding to the processed echo signal; based on the detection target point information, the signal receiving power, the actual target point information of the predetermined sample radar echo signal, the interference signal power, the noise signal power and the target reward function, determining the target reward value corresponding to the training anti-interference algorithm strategy.

[0056] Among them, the detected target point information is the position information of the target object predicted by the processed echo signal, and the detected target point information includes the predicted trajectory position of the target point, the predicted azimuth of the target point, the predicted pitch angle of the target point, and the predicted radial velocity of the target. The actual target point information is the position information of the target object actually set when arranging the simulation scene, and the actual target point information includes the actual trajectory position of the target point, the actual azimuth of the target point, the actual pitch angle of the target point, and the actual radial velocity of the target. The target reward value is used to reflect the prediction accuracy of the detected target point information. The higher the target reward value, the higher the prediction accuracy of the detected target point information; conversely, the lower the prediction accuracy of the detected target point information. It should be noted that the target reward function can be set to different functions according to different tasks.

[0057] In a specific implementation, the interference signal power and the noise signal power can be determined according to the generation parameters of the interference signal set by the jammer. The target reward value corresponding to the training anti-interference algorithm strategy is determined by detecting the target point information, the signal receiving power, the actual target point information of the predetermined sample radar echo signal, the interference signal power, the noise signal power and the target reward function. Exemplarily, the target reward function includes at least one of sparse reward, dense reward, shape reward and potential reward.

[0058] This embodiment provides an effective method for determining a target reward value, which can quickly and accurately determine the target reward value, thereby improving the accuracy of the model training results.

[0059] S220. Adjust model parameters in the deep reinforcement learning model to be trained based on the target reward value until the training is completed when preset training conditions are met, and determine the deep reinforcement learning model after the training as the strategy determination model.

[0060] The preset training condition may be that the loss function converges or the number of training times reaches a preset threshold.

[0061] In a specific implementation, the model parameters can be adjusted based on the determined target reward value, and the deep reinforcement learning model can be updated based on the adjusted model parameters, so as to maximize the target reward value corresponding to the updated deep reinforcement learning model. In addition, each time the target reward value is determined, it can be determined whether the preset training conditions are met, that is, whether the current number of training times reaches the preset number threshold; or whether the loss function corresponding to the deep learning model obtained by training converges; if the preset number threshold is reached, or the loss function converges, it can be determined that the training is over, and the deep reinforcement learning model obtained after the training is determined as the strategy determination model.

[0062] This embodiment adjusts the training parameters by determining the target reward function, and obtains the strategy determination model when the preset training conditions are met, which is conducive to determining an accurate and effective strategy determination model.

[0063] Furthermore, model parameters in the deep reinforcement learning model to be trained are adjusted based on the target reward value until the training is terminated when the preset training conditions are met, including: adjusting the current radar transmission parameters based on the training radar scheduling parameters output by the deep reinforcement learning model to be trained, and transmitting the sample transmission signal according to the adjusted current radar transmission parameters; updating the interference characteristic information of the received radar echo signal corresponding to the sample transmission signal to sample interference characteristic information, and inputting the updated sample interference characteristic information into the deep reinforcement learning model after adjusting the model parameters for action decision-making, until the training is terminated when the preset training conditions are met.

[0064] It should be noted that during the training process of the deep reinforcement learning model, it is necessary to continuously send sample transmission signals to obtain radar echo signals, and continuously adjust the model parameters of the deep reinforcement learning model by determining the sample interference feature information of the radar echo signal. Therefore, it is necessary to continuously adjust the transmission parameters of the sample transmission signal to realize the training of the deep reinforcement learning model.

[0065] Optionally, the deep reinforcement learning model can also output training radar scheduling parameters, which are the transmission parameters required to indicate the next radar transmission during the training process. The training radar scheduling parameters can be updated to the current radar transmission parameters to transmit the sample transmission signal according to the updated current radar transmission parameters, so that the radar echo signal corresponding to the sample transmission signal is received, and the interference feature information of the radar echo signal corresponding to the sample transmission signal is extracted as the sample interference feature information, and the updated sample interference feature information is input into the deep reinforcement learning model after adjusting the model parameters for action decision, so as to obtain the training anti-interference algorithm strategy, so as to perform anti-interference processing on the radar echo signal through the training anti-interference algorithm strategy according to the above steps, and adjust the model parameters of the deep reinforcement learning model based on the processed echo signal, so as to complete the training of the deep reinforcement learning model. Among them, the current radar transmission parameters include at least one of the radar's working mode sequence number, the transmission carrier frequency point sequence number, the transmission bandwidth, the radar antenna pitch angle and the sector range.

[0066] This embodiment adjusts the current radar transmission parameters through the training radar scheduling parameters output by the deep reinforcement learning model to continuously adjust the sample transmission signal and quickly and efficiently complete the training of the deep reinforcement learning model.

[0067] S230: Receive a current radar echo signal, and identify current interference feature information of a current interference signal in the current radar echo signal.

[0068] S240. Input the current interference feature information into a pre-trained strategy determination model, and based on the output result of the strategy determination model, obtain the current anti-interference algorithm strategy for processing the current interference signal, so as to perform anti-interference processing on the current radar echo signal according to the current anti-interference algorithm strategy; wherein the strategy determination model includes a deep reinforcement learning model.

[0069] This embodiment uses deep reinforcement learning and artificial neural networks to generate anti-interference strategies without human intervention, thereby improving the real-time performance of interference suppression decisions and improving the efficiency and accuracy of interference suppression.

[0070] The embodiments corresponding to the signal processing method are described in detail above. In order to make the technical scheme of the present method further clear to those skilled in the art, the training process of the strategy determination model required for signal processing is described in detail below.

[0071] Figure 3 is a radar interference suppression training framework diagram based on deep reinforcement learning provided according to an embodiment of the present invention, such as Figure 3As shown, the training process of the deep reinforcement learning model includes four parts: electromagnetic environment simulation, radar system simulation, deep reinforcement learning network and reward function calculation. In the specific implementation, the radar simulation system is used to generate a sample transmission signal, and transmit the sample transmission signal to the simulation environment, which includes the interference signal provided by the jammer and the preset target. After the sample transmission signal is reflected in the simulation environment, a sample radar echo signal is generated, and the radar system receives the sample radar echo signal. Among them, the sample transmission signal and the sample radar echo signal are both transmitted through the channel. Further, the sample radar echo signal is subjected to interference identification to obtain sample interference feature information. The sample interference feature information is sent to the deep reinforcement learning model to be trained, and the decision is made by the intelligent agent to output the training anti-interference algorithm strategy and the training radar scheduling parameters. The sample radar echo signal is processed by the training anti-interference algorithm strategy to obtain a processed echo signal, and the processed echo signal is sent to the radar system for signal processing to obtain the detection target point information and the signal receiving power. The target reward value is determined by detecting the target point information, the signal receiving power, the actual target point information, the interference signal power and the noise signal power. The detected target point information includes the predicted trajectory position of the target point, the predicted azimuth of the target point, the predicted pitch angle of the target point and the predicted radial velocity of the target. The actual target point information includes the real trajectory position of the target point, the real azimuth of the target point, the real pitch angle of the target point and the real radial velocity of the target.

[0072] Specifically, the specific process of determining the target reward value includes: 1. Determine the absolute value of the difference between the predicted trajectory position of the target point and the actual trajectory position of the target point as the point track distance error; 2. Determine the absolute value of the difference between the predicted azimuth of the target point track and the actual azimuth of the target point track as the point track azimuth error; 3. Determine the absolute value of the difference between the predicted pitch angle of the target point track and the actual pitch angle of the target point track as the point track pitch angle error; 4. Determine the absolute value of the difference between the predicted radial velocity of the target and the actual radial velocity of the target as the point track radial velocity error. Determine the signal to interference plus noise ratio SINR, the calculation formula of the signal to interference plus noise ratio SINR is:

[0073] SINR = P1 / (P2+P3)

[0074] Among them, P1 is the signal receiving power, P2 is the interference signal power, and P3 is the noise signal power.

[0075] 5. Determine the monitoring point distance L between the detection target domain and the real target. The calculation formula of the monitoring point distance L is as follows:

[0076] L=μ1ΔR+μ2Δα+μ3Δθ+μ4Δυ

[0077] Among them, μ1 is the preset weight coefficient corresponding to the point track distance error, μ2 is the preset weight coefficient corresponding to the point track azimuth error, μ3 is the preset weight coefficient corresponding to the point track pitch angle error, Δυ is the preset weight coefficient corresponding to the point track radial velocity error. ΔR is the point track distance error, Δα is the point track azimuth error, Δθ is the point track pitch angle error, and Δυ is the point track radial velocity error.

[0078] 6. By comparing the set distance threshold w with the distance L, we can get the real target capture judgment result logic (L <= w). This formula shows that when L is less than or equal to w, the logic value is 1; when L is greater than w, the logic value is 0. If no target is detected, the target reward value R wd = 0. If the target is detected, the target reward value R is determined based on the logic value, SINR, ΔR, Δα, Δθ and Δυ wd Specifically, R wd is determined as follows:

[0079] R wd =g(+logic(L <w),+SINR,-ΔR,-Δα,-Δθ,-Δυ)

[0080] Among them, "+" indicates positive correlation and "-" indicates negative correlation.

[0081] Furthermore, the model parameters of the deep reinforcement learning model are adjusted by the determined target reward value. And the model parameters in the deep reinforcement learning model are adjusted based on the obtained target reward value. Moreover, the current radar transmission parameters are adjusted based on the training radar scheduling parameters output from the deep reinforcement learning model, and a radar scheduling instruction is generated so that the radar system performs radar scheduling operations based on the radar scheduling instructions, generates a sample transmission signal corresponding to the current radar transmission parameters, and repeats the above steps of signal transmission, signal reception, interference identification, and determination of the target reward value until the training is completed when the preset training conditions are met, and the deep reinforcement learning model after the training is determined as the strategy determination model.

[0082] This embodiment outputs the anti-interference algorithm strategy and radar scheduling instructions together through a deep reinforcement learning model, which not only realizes passive interference suppression at the signal processing end, but also adjusts the transmission waveform parameters through active radar scheduling instructions to achieve active anti-interference measures. This method can more comprehensively consider the dynamic changes of the radar system in a complex electromagnetic environment, and actively respond to interference by adjusting the radar's operating mode and parameters in real time, such as the transmission carrier frequency, transmission bandwidth, and radar antenna pitch angle. In addition, it can more effectively improve the adaptability and flexibility of the radar system when facing variable interference sources, and enhance its anti-interference ability in complex environments.

[0083] This embodiment also determines the target reward value, and weights the signal-to-noise ratio and the monitoring point distance to integrate multi-dimensional evaluation indicators. By assigning corresponding weight coefficients to different indicators, the effectiveness of radar detection can be evaluated more comprehensively and objectively. The signal-to-noise ratio reflects the relative strength of the signal and the noise, while the monitoring point distance measures the proximity between the detection result and the real target. By weighting these indicators, the accuracy and reliability of radar detection can be quantified more accurately, which not only improves the radar system's adaptability to interference and anti-interference ability, but also enhances the real-time feedback and optimization capabilities of the detection results.

[0084] Figure 4 1 is a schematic diagram of the structure of a signal processing device provided according to an embodiment of the present invention, and the device is used to execute the signal processing method provided in any of the above embodiments. The device and the signal processing method of the above embodiments belong to the same inventive concept, and the details not described in detail in the embodiment of the signal processing device can refer to the embodiment of the above signal processing method. Figure 4 As shown, the device comprises:

[0085] The signal receiving module 10 is used to receive the current radar echo signal and identify the current interference feature information of the current interference signal in the current radar echo signal;

[0086] The information input module 11 is used to input the current interference feature information into a pre-trained strategy determination model, and based on the output result of the strategy determination model, obtain the current anti-interference algorithm strategy for processing the current interference signal, so as to perform anti-interference processing on the current radar echo signal according to the current anti-interference algorithm strategy; wherein the strategy determination model includes a deep reinforcement learning model.

[0087] Based on any optional technical solution in the embodiment of the present invention, optionally, the signal receiving module 10 includes:

[0088] The spectrum determination submodule is used to determine the corresponding multiple signal classification spectrum in the current radar echo signal based on the multiple signal classification algorithm;

[0089] A parameter identification submodule, used to identify the current interference type and current characteristic parameters of the current interference signal in the current radar echo signal; wherein the current interference type includes at least one of the main lobe and side lobe interference type, the suppression and forwarding interference type, and the pulse continuous wave interference type; the current characteristic parameters include frequency and / or amplitude;

[0090] The current interference characteristic information determination submodule is used to determine the multiple signal classification spectrogram, the current interference type and the current characteristic parameter as the current interference characteristic information.

[0091] Based on any optional technical solution in the embodiments of the present invention, optionally, the following further includes:

[0092] An information input module, used for inputting sample interference feature information corresponding to the sample radar echo signal into the deep reinforcement learning model to be trained for action decision before inputting the current interference feature information into the pre-trained strategy determination model;

[0093] An anti-interference processing module is used to perform anti-interference processing on the sample radar echo signal based on the training anti-interference algorithm strategy output by the deep reinforcement learning model to be trained, so as to obtain a processed echo signal;

[0094] A target reward value determination module is used to determine the target reward value corresponding to the training anti-interference algorithm strategy based on the processed echo signal and the preset target reward function;

[0095] The strategy determination model determination module is used to adjust the model parameters in the deep reinforcement learning model to be trained based on the target reward value until the training is completed when the preset training conditions are met, and the deep reinforcement learning model after the training is determined as the strategy determination model.

[0096] Based on any optional technical solution in the embodiments of the present invention, optionally, the strategy determination model determination module includes:

[0097] A sample transmission signal transmission submodule, used to adjust the current radar transmission parameters based on the training radar scheduling parameters output by the deep reinforcement learning model to be trained, and transmit the sample transmission signal according to the adjusted current radar transmission parameters;

[0098] The sample interference feature information updating submodule is used to update the interference feature information of the received radar echo signal corresponding to the sample transmission signal to the sample interference feature information, and input the updated sample interference feature information into the deep reinforcement learning model after adjusting the model parameters for action decision-making, until the training is completed when the preset training conditions are met.

[0099] Based on any optional technical solution in the embodiments of the present invention, optionally, the target reward value determination module includes:

[0100] The target reward value determination submodule is used to determine the detection target point information and signal reception power corresponding to the processed echo signal; based on the detection target point information, signal reception power, the actual target point information of the predetermined sample radar echo signal, the interference signal power, the noise signal power and the target reward function, the target reward value corresponding to the training anti-interference algorithm strategy is determined.

[0101] Based on any optional technical solution in the embodiments of the present invention, optionally, the following further includes:

[0102] A current state information acquisition module, used to acquire the current state information of the radar corresponding to the current radar echo signal before inputting the current interference feature information into the pre-trained strategy determination model;

[0103] The information input module 11 includes:

[0104] The transmission parameter acquisition submodule is used to input the current state information and the current interference characteristic information into the pre-trained strategy determination model to obtain the next radar transmission parameter based on the output result of the strategy determination model, and respond to the transmission trigger operation of the next radar transmission signal to transmit the next radar transmission signal according to the next radar transmission parameter.

[0105] Based on any optional technical solution in the embodiments of the present invention, optionally, the current status information includes at least one of a working mode, a transmission carrier frequency, a transmission bandwidth, a radar antenna elevation angle and a sector range.

[0106] The technical solution of the embodiment of the present invention receives a current radar echo signal and identifies current interference feature information of a current interference signal in the current radar echo signal; by inputting the current interference feature information into a pre-trained strategy determination model, based on the output result of the strategy determination model, a current anti-interference algorithm strategy for processing the current interference signal is obtained; wherein the strategy determination model includes a deep reinforcement learning model; through the strategy determination model, the current anti-interference algorithm strategy can be determined quickly and accurately without human intervention, and the current radar echo signal can be subjected to anti-interference processing according to the current anti-interference algorithm strategy, which is beneficial to improving the efficiency and accuracy of anti-interference processing.

[0107] It is worth noting that in the embodiment of the above-mentioned signal processing device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0108] Figure 5 Schematic diagram of the structure of an electronic device that implements the signal processing method of an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0109] like Figure 5 As shown, the electronic device 20 includes at least one processor 21, and a memory connected to the at least one processor 21, such as a read-only memory (ROM) 22, a random access memory (RAM) 23, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 21 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 22 or the computer program loaded from the storage unit 28 to the random access memory (RAM) 23. In RAM23, various programs and data required for the operation of the electronic device 20 can also be stored. The processor 21, ROM22 and RAM23 are connected to each other through a bus 24. An input / output (I / O) interface 25 is also connected to the bus 24.

[0110] A number of components in the electronic device 20 are connected to the I / O interface 25, including: an input unit 26, such as a keyboard, a mouse, etc.; an output unit 27, such as various types of displays, speakers, etc.; a storage unit 28, such as a disk, an optical disk, etc.; and a communication unit 29, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 29 allows the electronic device 20 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0111] The processor 21 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 21 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 21 performs the various methods and processes described above, such as a signal processing method.

[0112] In some embodiments, the signal processing method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 28. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 20 via the ROM 22 and / or the communication unit 29. When the computer program is loaded into the RAM 23 and executed by the processor 21, one or more steps of the signal processing method described above may be performed. Alternatively, in other embodiments, the processor 21 may be configured to perform the signal processing method in any other appropriate manner (e.g., by means of firmware).

[0113] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0114] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0115] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0116] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0117] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0118] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0119] This embodiment also provides a computer program product, including a computer program, which, when executed by a processor, implements the signal processing method provided in any embodiment of the present application.

[0120] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0121] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0122] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A signal processing method, characterized in that: include: Receiving a current radar echo signal, and identifying current interference feature information of a current interference signal in the current radar echo signal; Inputting the current interference feature information into a pre-trained strategy determination model, and acquiring a current anti-interference algorithm strategy for processing the current interference signal based on an output result of the strategy determination model, so as to perform anti-interference processing on the current radar echo signal according to the current anti-interference algorithm strategy; Wherein, the strategy determination model includes a deep reinforcement learning model.

2. The method according to claim 1, characterized in that The identifying current interference feature information of the current interference signal in the current radar echo signal includes: Based on a multiple signal classification algorithm, determining a multiple signal classification spectrogram corresponding to the current radar echo signal; Identify the current interference type and current characteristic parameters of the current interference signal in the current radar echo signal; wherein the current interference type includes at least one of a main-side lobe interference type, a suppression-relay interference type, and a pulse continuous wave interference type; and the current characteristic parameters include frequency and / or amplitude; The multiple signal classification spectrogram, the current interference type and the current characteristic parameter are determined as the current interference characteristic information.

3. The method according to claim 1, characterized in that Before inputting the current interference feature information into the pre-trained strategy determination model, the method further includes: The sample interference feature information corresponding to the sample radar echo signal is input into the deep reinforcement learning model to be trained to make action decisions; Performing anti-interference processing on the sample radar echo signal based on the training anti-interference algorithm strategy output by the deep reinforcement learning model to be trained to obtain a processed echo signal; Determining a target reward value corresponding to the training anti-interference algorithm strategy based on the processed echo signal and a preset target reward function; The model parameters in the deep reinforcement learning model to be trained are adjusted based on the target reward value until the training is completed when a preset training condition is met, and the deep reinforcement learning model after the training is determined as the strategy determination model.

4. The method according to claim 3, characterized in that The adjusting the model parameters in the deep reinforcement learning model to be trained based on the target reward value until the training ends when a preset training condition is met includes: Adjusting current radar transmission parameters based on the training radar scheduling parameters output by the deep reinforcement learning model to be trained, and transmitting a sample transmission signal according to the adjusted current radar transmission parameters; The interference characteristic information of the received radar echo signal corresponding to the sample transmission signal is updated to the sample interference characteristic information, and the updated sample interference characteristic information is input into the deep reinforcement learning model after adjusting the model parameters for action decision-making, until the training is completed when the preset training conditions are met.

5. The method according to claim 3, characterized in that: The determining, based on the processed echo signal, the sample radar echo signal and a preset target reward function, a target reward value corresponding to the training anti-interference algorithm strategy comprises: Determining detection target point information and signal receiving power corresponding to the processed echo signal; Based on the detected target point information, the signal reception power, the predetermined actual target point information of the sample radar echo signal, the interference signal power, the noise signal power and the target reward function, the target reward value corresponding to the training anti-interference algorithm strategy is determined.

6. The method according to claim 1, characterized in that Before inputting the current interference feature information into the pre-trained strategy determination model, the method further includes: Acquire current status information of the radar corresponding to the current radar echo signal; The step of inputting the current interference feature information into a pre-trained strategy determination model comprises: The current state information and the current interference characteristic information are input into a pre-trained strategy determination model to obtain a next radar transmission parameter based on an output result of the strategy determination model, and in response to a transmission trigger operation for the next radar transmission signal, the next radar transmission signal is transmitted according to the next radar transmission parameter.

7. The method according to claim 6, characterized in that The current status information includes at least one of a working mode, a transmission carrier frequency, a transmission bandwidth, a radar antenna elevation angle, and a sector range.

8. A signal processing device, characterized in that: include: A signal receiving module, used to receive a current radar echo signal and identify current interference feature information of a current interference signal in the current radar echo signal; An information input module is used to input the current interference feature information into a pre-trained strategy determination model, and based on the output result of the strategy determination model, obtain the current anti-interference algorithm strategy for processing the current interference signal, so as to perform anti-interference processing on the current radar echo signal according to the current anti-interference algorithm strategy; wherein the strategy determination model includes a deep reinforcement learning model.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the signal processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the signal processing method according to any one of claims 1 to 7 when executed.

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