A radar interference waveform adaptive generation method, device and equipment

Abstract

The present invention provides a radar jammer waveform adaptive generation method, device and equipment, the method comprising: setting a radar signal generation function based on the configuration parameters of the radar in the opponent's radar; constructing a jammer model for reinforcement learning; in response to the received radar signal of the opponent, controlling the generation of a matching radar environment based on the generation function, and using the jammer model to control the jammer to output a jammer wave corresponding to a jamming style; evaluating the jamming effect of the jammer wave, and calculating and determining the reward value corresponding to the jamming effect; adjusting the parameters of the jammer model based on the jamming effect and the corresponding reward value, so that the adjusted jammer model can generate a jamming wave corresponding to a new jamming style. The method of the present invention can adaptively generate an optimized jamming waveform according to the change of the opponent's radar environment, thereby improving the effect of radar jamming.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar jamming and electronic countermeasures, and more specifically, relates to a radar jamming waveform adaptive generation method, device and equipment. Background Art

[0002] In recent years, the vigorous development of artificial intelligence technology has greatly improved the situational awareness, autonomous learning, autonomous reasoning and decision-making capabilities of radar and interference, making the electromagnetic confrontation between the two highly intelligent.

[0003] In a complex and ever-changing electromagnetic environment, whether the radar can function normally is very important to the outcome of the simulation exercise. Therefore, radar confrontation began to develop rapidly, using special electronic equipment to detect and interfere with enemy radars in exercise scenarios, including radar reconnaissance and radar interference. Radar reconnaissance is to obtain intelligence on enemy radars, and radar interference is to hinder the normal operation of enemy radars. In simulated exercise scenarios, radar detection plays an important role in threatening long-range targets. In order to deal with threats from enemy radars, radar interference aims to destroy the enemy's radar detection and positioning capabilities, protect one's own targets from being detected and identified by the enemy, and protect them from enemy attacks.

[0004] Traditional radar jamming methods, such as suppression jamming and deception jamming, often have fixed jamming patterns. In the face of complex and changeable radar environments, a single fixed jamming pattern is difficult to play a jamming role, and sometimes even has no jamming effect. Current radar jamming methods still have certain limitations: (1) The existing reinforcement learning jamming waveform optimization method does not consider the waveform selection among multiple jamming patterns, and then optimizes the waveform for the selected specific jamming pattern; (2) The evaluation indicators considered in the current jamming effect evaluation method are not diversified enough; (3) The existing reinforcement learning reward feedback function is relatively limited, and does not consider the radar state environment and the feedback after the jamming effect evaluation at the same time. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a radar interference waveform adaptive generation method, device and equipment which can adaptively generate optimized interference waveform according to the change of the opponent's radar environment and improve the effect of radar interference.

[0006] The present invention provides a radar jammer waveform adaptive generation method, comprising:

[0007] Setting a radar signal generation function based on radar configuration parameters in the other party's radar, wherein the generation function constructs different radar environments by generating different radar signals;

[0008] Constructing a jammer model for reinforcement learning, wherein the jammer model is applied to a jammer so that the jammer outputs an interference wave;

[0009] In response to the received radar signal of the adversary, a matching radar environment is generated based on the generation function, and the jammer model is used to control the jammer to output a jamming wave corresponding to a jamming pattern;

[0010] Evaluating the interference effect of the interference wave, and calculating and determining a reward value corresponding to the interference effect;

[0011] The parameters of the jammer model are adjusted based on the jamming effect and the corresponding reward value, so that the adjusted jammer model can generate jamming waves corresponding to the new jamming style, and the jamming effect of the jamming waves corresponding to the new jamming style is better than the jamming effect of the jamming waves corresponding to the previous jamming style.

[0012] In one embodiment, the method further comprises:

[0013] The jammer model is controlled to perform reinforcement learning for different radar environments until the jammer model can adaptively generate jamming waves that match the radar environment to the greatest extent under different radar environments.

[0014] In one embodiment, the step of setting a radar signal generation function based on configuration parameters of radars in two radar confrontation parties includes:

[0015] The radar signal generation function is set according to the radar parameters and radar signal form of the opponent's radar. The radar signal form includes linear frequency modulation signal, frequency coded signal, phase coded signal and complex modulation signal.

[0016] In one embodiment, the method further comprises:

[0017] The received radar signal of the other party's radar is subjected to real-time signal feature extraction to obtain the radar parameters and radar signal form. The radar parameters include frequency, amplitude, and pulse width.

[0018] In one embodiment, the evaluating the interference effect of the interference wave includes:

[0019] Constructing an interference effect evaluation system based on different evaluation indicators, wherein the different evaluation indicators are used to evaluate the interference effect of the interference wave according to the states of the other party's radar after receiving the interference wave, responding to the interference wave, and being interfered by the interference wave;

[0020] The interference effect is evaluated based on the interference effect evaluation system to obtain an evaluation result of the interference effect.

[0021] In one embodiment, the construction of an interference effect evaluation system based on different evaluation indicators includes:

[0022] Constructing a CFAR detection threshold ratio evaluation index, wherein the CFAR detection threshold ratio is the ratio of the detection threshold value after interference is implemented to the detection threshold value when there is no interference;

[0023] Constructing a false target number evaluation index, wherein the false target number is the number of false targets generated by the jammer when implementing the current jamming;

[0024] Constructing an interference-to-signal amplitude ratio evaluation index, wherein the interference-to-signal amplitude ratio is a ratio between the overall average amplitude of the received opponent's radar signal after interference and the overall average amplitude of the originally received radar signal;

[0025] Constructing an interference-to-signal ratio evaluation index, wherein the interference-to-signal ratio describes the relationship between the interference wave and the target echo signal from the perspective of power, and is defined as the ratio of the power of the interference wave to the power of the original radar signal;

[0026] Determine an interference evaluation factor based on the original radar signal power, the interference wave power and the radar improvement factor, wherein the radar improvement factor is the ratio of the pulse pressure signal-to-noise ratio of the corresponding received opponent radar signal to the original signal-to-noise ratio;

[0027] The interference effect evaluation system is formed based on the CFAR detection threshold ratio evaluation index, the false target number evaluation index, the interference-to-signal amplitude ratio evaluation index, the interference-to-signal ratio evaluation index and the interference evaluation factor.

[0028] In one embodiment, the interference pattern includes intermittent sampling forwarding interference and noise convolution interference, wherein the parameters to be optimized in the intermittent sampling forwarding interference are the number of forwarding times and the forwarding signal amplitude, and the parameter to be optimized in the noise convolution interference is the noise time width.

[0029] In one embodiment, adjusting the parameters of the jammer model based on the jamming effect and the corresponding reward value includes:

[0030] Based on the interference effect and the corresponding reward value, the jammer model is trained by combining the reinforcement learning algorithm. Q The value is updated based on the Q The value implements the parameter adjustment of the model:

[0031] To solve the Q The value function of the value is:

[0032]

[0033] Said is the learning rate; is the discount factor; For the currentt The reward value at the moment; For the current t The value function at time, S t For the current t The status of the enemy's radar at all times, A t For the current t Interference actions performed at all times; To execute the current t The value function of the interference action at the moment to the next state; for t The value function at time +1.

[0034] Another embodiment of the present invention also provides a radar jammer waveform adaptive generation device, including:

[0035] A setting module, used for setting a generation function of a radar signal according to configuration parameters of the radar in the other party's radar, wherein the generation function constructs different radar environments by generating different radar signals;

[0036] A first construction module is used to construct a jammer model for reinforcement learning, wherein the jammer model is applied to a jammer so that the jammer outputs an interference wave;

[0037] A generating module, configured to respond to a received radar signal of an adversary, control and generate a matching radar environment based on the generating function, and control the jammer to output an interference wave corresponding to an interference pattern using the jammer model;

[0038] An evaluation module, used for evaluating the interference effect of the interference wave, and calculating and determining a reward value corresponding to the interference effect;

[0039] An adjustment module is used to adjust the parameters of the jammer model according to the interference effect and the corresponding reward value, so that the adjusted jammer model can generate interference waves corresponding to the new interference style, and the interference effect of the interference wave corresponding to the new interference style is better than the interference effect of the interference wave corresponding to the previous interference style.

[0040] Another embodiment of the present invention further provides an electronic device, including:

[0041] one or more processors;

[0042] a memory configured to store one or more programs;

[0043] When the one or more programs are executed by the one or more processors, the one or more processors implement the radar interference waveform adaptive generation method as described in the above embodiment.

[0044] The beneficial effects of the present invention include using a reinforcement learning algorithm to learn the action value function of the Q function to find the optimal adjustment strategy. The Q function can assign a reward score to each state and action pair (s, a), which represents the expected reward that can be obtained according to the current strategy after executing action a from state s. The algorithm parameters are adjusted by a real-time feedback mechanism so that the jammer can adapt to the changing radar environment of the other party and match the characteristics of the other party's radar signal to the greatest extent, so as to achieve the effect of adaptive radar signal of the jamming signal. The method of the present application can dynamically adjust the style and corresponding parameters of the jamming signal (interference wave) according to the characteristics of the other party's radar system and the current radar environment to minimize the performance of the other party's radar, so that it cannot effectively detect the target, thereby achieving a better jamming effect. In general, the method provided by the present application has both suppression and deception effects, and can optimize the interference parameters of the jammer according to the other party's radar signal to adaptively generate interference waveforms. In addition, by constructing a multivariate evaluation criterion to evaluate the interference effect, the evaluation result of the interference effect can be fed back in real time, and the jammer parameter adjustment efficiency and accuracy can be assisted to improve, thereby improving the interference effect of the output interference wave.

[0045] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings.

[0046] The technical solution of the present application is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 The figure is a flow chart of a radar jammer waveform adaptive generation method in an embodiment of the present invention.

[0049] Figure 2 Schematic diagram of the application process of the radar interference waveform adaptive generation method in an embodiment of the present invention (the figure shows three parts in the process, namely part a for radar signal processing, part b for intelligent body detection, and part c for interference waveform determination, and the relationship between the three parts is from a to b, and from b to c).

[0050] Figure 3 The figure is a flow chart of a radar jammer waveform adaptive generation method in another embodiment of the present invention.

[0051] Figure 4 It is a structural block diagram of a radar jammer waveform adaptive generation device in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings, but are not intended to limit the present invention.

[0053] It should be understood that various modifications may be made to the embodiments disclosed herein. Therefore, the following description should not be considered as limiting, but merely as an example of an embodiment. Other modifications within the scope of the invention will occur to those skilled in the art.

[0054] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the general description of the invention given above and the detailed description of the embodiments given below, serve to explain the principles of the invention.

[0055] These and other characteristics of the invention will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the accompanying drawings.

[0056] The above and other aspects, features and advantages of the present invention will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0057] Specific embodiments of the present invention are described hereinafter with reference to the accompanying drawings; however, it should be understood that the disclosed embodiments are merely examples of the present invention, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that obscure the present invention. Therefore, the specific structural and functional details disclosed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present invention in a variety of ways with substantially any suitable detailed structure.

[0058] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," which may all refer to one or more of the same or different embodiments according to the present invention.

[0059] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0060] like Figure 1 As shown, the present invention includes a radar jammer waveform adaptive generation method, comprising:

[0061] S1: setting a radar signal generation function based on the configuration parameters of the other party's radar, wherein the generation function constructs different radar environments by generating different radar signals;

[0062] S2: constructing a jammer model for reinforcement learning, wherein the jammer model is applied to the jammer so that the jammer outputs an interference wave;

[0063] S3: in response to the received radar signal of the adversary, controlling and generating a matching radar environment based on the generating function, and using the jammer model to control the jammer to output an interference wave corresponding to an interference pattern;

[0064] S4: evaluating the interference effect of the interference wave, and calculating and determining a reward value corresponding to the interference effect;

[0065] S5: Adjust the parameters of the jammer model based on the jamming effect and the corresponding reward value, so that the adjusted jammer model can generate an interference wave corresponding to a new interference pattern, and the interference effect of the interference wave corresponding to the new interference pattern is better than the interference effect of the interference wave corresponding to the previous interference pattern.

[0066] The radar jammer waveform adaptive generation method based on reinforcement learning provided in this embodiment can be summarized as optimizing the jammer model's jamming pattern, such as but not limited to the jamming parameters of intermittent sampling forwarding jamming and noise convolution jamming, through the obtained opponent's radar signal, to adaptively generate an optimized jamming waveform adapted to the current opponent's radar environment according to the change of the opponent's radar environment, so as to negatively affect the opponent's radar performance through the output jamming wave, reduce the opponent's radar performance, that is, improve the effect of radar jamming, and at the same time, use multivariate evaluation criteria to evaluate and feedback the jamming effect, which can assist in real-time optimization of the jamming model parameters in our jammer, improve the performance of the jamming wave output next time and the matching degree with the opponent's radar environment, and ensure that the adjustment direction and adjustment amount of the parameters are correct and the adjustment efficiency is high.

[0067] In one embodiment, the method further comprises:

[0068] S6: Controlling the jammer model to perform reinforcement learning for different radar environments until the jammer model can adaptively generate jamming waves that best match the radar environment in different radar environments.

[0069] That is, the jammer model in this embodiment can perform reinforcement learning corresponding to different radar environments for multiple times until the jammer model can adaptively generate jamming waves that match the radar environment to the greatest extent under different radar environments.

[0070] Combination Figure 2 As shown, the method also includes:

[0071] S7: extracting signal features of the received radar signal of the other party's radar in real time to obtain the radar parameters and radar signal form, wherein the radar parameters include frequency, amplitude, and pulse width.

[0072] Based on step S7, the generation function of the radar signal is set based on the configuration parameters of the radars in the radar confrontation parties, including:

[0073] S8: setting a radar signal generation function according to radar parameters and radar signal forms of the opponent's radar. The radar signal forms include linear frequency modulation signals, frequency coded signals, phase coded signals, and complex modulation signals.

[0074] Exemplarily, after receiving the opponent's radar signal in this embodiment, feature extraction will be performed on it to determine the radar parameters and radar signal form. Based on this, the system will set the radar signal generation function so that the generation function can generate a radar signal that matches the opponent's radar and build a corresponding radar environment. At the same time, the system will also determine the target flight trajectory of our flying target, including the flight process from the starting point to the destination, and the task is to successfully fly to the destination. After that, a jammer model for reinforcement learning is constructed. The jammer model is integrated in the jammer to form an intelligent jammer so that the jammer can automatically generate interference waves for effectively weakening the opponent's radar performance according to the opponent's radar signal. The jammer in this embodiment can use deceptive interference and smart interference. The jammer's interference style includes intermittent sampling forwarding interference and noise convolution interference. In this embodiment, the parameters to be optimized in the intermittent sampling forwarding interference are the number of forwarding times and the forwarding signal amplitude, and the parameters to be optimized in the noise convolution interference are the noise time width.

[0075] The radar environment includes radar signals with different parameter settings and signal forms, that is, different radar signals generate different radar environments, and radar signals directly determine the radar environment. In order to determine the opponent's radar environment, our system will perform real-time signal feature extraction on the received opponent's radar signal, including feature extraction of frequency, amplitude, pulse width, etc. The extracted object, that is, radar signal, includes frequency modulation signal, frequency coded signal, phase coded signal and complex modulation signal, recorded as The interference pattern of the jammer in this embodiment is intermittent sampling forwarding interference and noise convolution interference, which is recorded as The parameters to be optimized in intermittent sampling forwarding interference are the number of forwarding times and the forwarding signal amplitude, and the parameter to be optimized in noise convolution interference is the noise time width.

[0076] The interference adaptive algorithm proposed in this embodiment is a typical algorithm Q-learning algorithm in reinforcement learning. Since Q-learning is a value iteration algorithm, it finds the optimal strategy by learning the action value function of the Q function. The Q function can assign a reward score to each state and action pair (s, a). This score represents the expected reward that can be obtained according to the current strategy after executing action a starting from state s. It aims to dynamically adjust the style and corresponding parameters of the interference signal according to the characteristics of the opponent's radar system, so as to minimize the performance of the opponent's radar and make it unable to effectively detect the target, thereby achieving a better interference effect.

[0077] Specifically, continue to combine Figure 2 As shown, in this embodiment, a radar environment class (corresponding to the generation function) and an agent class (corresponding to the jammer model) are created respectively. In the radar environment class, the parameters of the opponent's radar are mainly set, including frequency, pulse width, transmission power, etc.; the agent class includes receiving the opponent's radar parameters, and the agent selects and optimizes the interference parameters through the Q table and feedback of the reward function; the main function initializes the opponent's radar environment and the agent, and the agent optimizes the interference parameters by changing the radar parameters and reward feedback, and then generates an interference waveform to implement interference.

[0078] The core idea of ​​the Q-Learning algorithm is to first S and action set A To create a Q Value table (the Q The ordinate of the value table represents the state, and the abscissa represents the action corresponding to a certain state), and then according to the reward R and Q Update the estimated value Q In the table Q ( s,a ), which is the state-action value function. Q When the value table is updated, select the value in this state. Q The value is the largest, that is, the action corresponding to the maximum value of the state-action value function is executed, so that the intelligent agent can obtain the maximum cumulative reward from the external environment and thus find the optimal strategy.

[0079] The specific algorithm steps are as follows:

[0080] (1) Q Value table Data is initialized. Generally, all Q The value is initialized to 0.

[0081] (2) Determine the current state at time t S t .

[0082] (3) Based on Q Value table combined with certain action strategies π Choose your action .

[0083] (4) Execute action Then the state is transferred, and at the next moment t +1 moment to migrate to a new state and receive corresponding rewards .

[0084] (5) Update and correct by iterative recursive formula ,

[0085] in, a represents the learning rate, satisfying , and if a The larger the value, the more the previous iteration is retained. Q The smaller the value part, The value converges faster, but convergence too fast can lead to oscillation. represents the discount factor, satisfying , represents the impact and importance of subsequent rewards on the current moment, A value of 0 means that only the short-term rewards at the current moment are taken into consideration. A value of 1 means more emphasis is placed on future rewards. is the value function at the moment before the iteration, refers to the value function after iteration, Represents that at the next moment t +1 The corresponding state at the moment Maximum value.

[0086] (6) When the agent reaches the target state, that is, the end point or the restricted area, it stops iterating and returns to the starting state to start a new cycle; otherwise, it returns to step (2) to continue iterating and updating. .

[0087] (7) If the iterative update formula is satisfied, then Value iteration converges, The value table is updated. Then the action corresponding to the largest value is selected according to the greedy strategy, and the system can obtain the maximum Accumulate rewards to find the optimal strategy and the optimal path.

[0088] The Q-learning method of reinforcement learning is model-free and exploratory. The intelligent agent can continuously "trial and error" during the learning process to try new actions to discover reward signals. It is flexible, and the off-track strategy enables Q-learning to flexibly handle various problems and environments. In contrast, traditional methods rely more on prior knowledge or fixed rules and are prone to limitations. Moreover, because the Q-Learning algorithm is adaptive, it can continuously adjust the strategy according to environmental changes and feedback, thereby achieving adaptive learning. Traditional methods may require more manual adjustments or redesigns when facing environmental changes. Due to the changeable and complex radar waveforms, the traditional method of relying on a large amount of prior data for statistical analysis to formulate interference strategies has encountered great challenges. However, the interference decision-making method based on reinforcement learning has shown a unique "cognitive" advantage. Even in the absence of prior data, it can optimize the decision-making model through continuous "trial and error" learning, thereby obtaining the most effective interference strategy.

[0089] Furthermore, the jammer / jammer model in this embodiment optimizes the jamming parameters under the selected jamming style according to the results of the reward and the jamming effect evaluation feedback, and finally obtains the optimized jamming waveform.

[0090] like Figure 3 As shown, the evaluating the interference effect of the interference wave includes:

[0091] S9: constructing an interference effect evaluation system based on different evaluation indicators, wherein the different evaluation indicators are used to evaluate the interference effect of the interference wave according to the states of the other party's radar receiving the interference wave, responding to the interference wave, and being interfered by the interference wave;

[0092] S10: Evaluate the interference effect based on the interference effect evaluation system to obtain an evaluation result of the interference effect.

[0093] The interference effect evaluation system is constructed based on different evaluation indicators, including:

[0094] S11: constructing a CFAR detection threshold ratio evaluation index, wherein the CFAR detection threshold ratio is the ratio of the detection threshold value after interference is implemented to the detection threshold value when there is no interference;

[0095] S12: constructing a false target number evaluation index, where the false target number is the number of false targets generated by the jammer when performing the current jamming;

[0096] S13: constructing an interference-signal amplitude ratio evaluation index, where the interference-signal amplitude ratio is the ratio of the overall average amplitude of the received opponent's radar signal after interference to the overall average amplitude of the originally received radar signal;

[0097] S14: constructing an interference-to-signal ratio evaluation index, where the interference-to-signal ratio describes the relationship between the interference wave and the target echo signal from the perspective of power, and is defined as the ratio of the power of the interference wave to the power of the original radar signal;

[0098] S15: determining an interference evaluation factor based on the original radar signal power, the interference wave power and the radar improvement factor, wherein the radar improvement factor is a ratio of a pulse pressure signal-to-noise ratio of the received opponent radar signal to the original signal-to-noise ratio;

[0099] S16: The interference effect evaluation system is constructed based on the CFAR detection threshold ratio evaluation index, the false target number evaluation index, the interference-to-signal amplitude ratio evaluation index, the interference-to-signal ratio evaluation index and the interference evaluation factor.

[0100] Specifically, CFAR detection threshold ratio: Generally, the higher the detection threshold, the lower the probability of the radar detecting the target, but the false alarm probability will also decrease. When the detection threshold after interference is greater than the detection threshold without interference, it means that the interference effectively increases the background noise level, so that the radar needs a higher signal strength to detect the target, thereby reducing the detection performance of the radar. The formula is ;

[0101] In the formula This is the detection threshold ratio defined in this paper. is the detection threshold after interference is implemented, is the detection threshold value without interference. When the detection threshold after interference is greater than the detection threshold without interference, If it is greater than 1, it means that the interference effectively increases the background noise level, so that the radar needs a higher signal strength to detect the target, thereby reducing the radar's detection performance. It means that the interference is effective and weakens the radar's detection capability to a certain extent.

[0102] Number of false targets: Compare the parameters of a certain point in the track with the parameters of the real target with the same recording time, and identify the false targets similar to the real targets within a certain range.

[0103] During the test, let the jammer release the false targets as much as possible. And record the radar tracking data. If the number of false targets tracked by the radar in a certain test is n, the results after multiple tests are: ,in, is the number of trials.

[0104] Interference-to-signal amplitude ratio: the ratio of the overall average amplitude of the jammed signal to the overall average amplitude of the original signal. The strength of the jamming signal at the radar receiver exceeds the target echo signal. This also means that the jamming signal has a very good coverage effect on the radar receiver and can effectively interfere with the target echo signal, thereby increasing the probability of the radar detecting false targets, thereby detecting multiple false targets.

[0105] This ratio is used to analyze the strength of the interference signal relative to the radar echo signal, reflecting the influence of the interference signal on the radar receiving end. The expression is as follows: .

[0106] In the formula This is the interference-signal amplitude ratio defined in this paper, is the overall average amplitude of the signal after interference, is the overall average amplitude of the original signal without interference. When it is greater than 1, the strength of the jamming signal at the radar receiving end exceeds the target echo signal. This also means that the jamming signal has a very good coverage effect on the radar receiver and can effectively interfere with the target echo signal, thereby increasing the probability of the radar detecting false targets and detecting multiple false targets. In this case, the radar may find it difficult to distinguish the target signal, and the jamming effect is obvious.

[0107] Interference-to-signal ratio: refers to the ratio of the interference signal power to the original signal power. The interference-to-signal ratio directly affects the radar's target detection performance. When the interference-to-signal ratio is high, the signal received by the radar is mainly composed of interference signals, and the target signal is difficult to extract from it. At the same time, the radar's false alarm rate increases, the probability of target detection decreases, and it may even completely lose the ability to detect the target.

[0108] This ratio is used to evaluate the impact of interference signals on the radar system and is expressed as: ;

[0109] In the formula, is the signal-to-dry ratio, is the interference signal power, is the original signal power. The higher the ratio, the greater the strength of the jamming signal relative to the original signal, and the more significant the jamming effect on the radar. In electronic countermeasures, the interference-to-signal ratio is an important indicator for evaluating the effectiveness of jamming. The effectiveness of jamming is directly related to the interference-to-signal ratio. For example, when the interference-to-signal ratio is very high, it means that the jamming signal has a strong inhibitory effect on the target signal and the jamming effectiveness is good; when the interference-to-signal ratio is low, the jamming signal is difficult to have a significant impact on the target signal and the jamming effectiveness is poor.

[0110] Interference evaluation factor: It comprehensively considers the original signal power, interference signal power and radar improvement factor. The radar improvement factor is a ratio calculated based on the pulse compression signal-to-noise ratio and the original signal-to-noise ratio. It is usually used to quantify the radar's ability to resist interference. The expression of the interference evaluation factor is as follows: ;

[0111] In the formula, is the interference evaluation factor, is the interference signal power, is the original signal power, is the radar improvement factor. The smaller the ratio is, the better the interference effect is, that is, the more significant the radar is affected by the interference. The smaller the ratio is less than 1, the more significant the impact of the interference on the radar is, and the better the interference effect is.

[0112] After the evaluation is completed, the system needs to adjust the model parameters based on the evaluation results. The adjustment of the parameters of the jammer model based on the jamming effect and the corresponding reward value includes:

[0113] S17: Based on the interference effect and the corresponding reward value, the interference machine model is strengthened by the reinforcement learning algorithm. Q The value is updated based on the Q The value implements the parameter adjustment of the model:

[0114] To solve the Q The value function of the value is:

[0115]

[0116] Said is the learning rate; is the discount factor; For the current t The reward value at the moment; For the current t The value function at time, S t For the current t The status of the enemy's radar at all times, A t For the current t Interference actions performed at all times; To execute the current t The value function of the interference action at the moment to the next state; for t The value function at time +1.

[0117] In the learning process, after the model is updated, it will continue to generate new interference waves based on the new parameters, and repeat the above process to continuously perform reinforcement learning until the target flight mission is completed, that is, the completion of this mission and the statistics of parameter changes and interference effect evaluation index changes during the process are completed. The flight test process can be performed multiple times until the set number of simulation tasks is completed, and the final multiple mission completions and the statistical average of parameter changes and interference effect evaluation index changes during the process are obtained. That is, all learning results are statistically averaged, and the optimal parameters of the model are finally determined. After obtaining the final parameters, the model can achieve the effect of the jammer adaptively generating matching interference waves based on different radar scenarios.

[0118] like Figure 4 As shown, another embodiment of the present invention also provides a radar jammer waveform adaptive generation device 100, comprising:

[0119] A setting module, used for setting a generation function of a radar signal according to configuration parameters of the radar in the other party's radar, wherein the generation function constructs different radar environments by generating different radar signals;

[0120] A first construction module is used to construct a jammer model for reinforcement learning, wherein the jammer model is applied to a jammer so that the jammer outputs an interference wave;

[0121] A generating module, configured to respond to a received radar signal of an adversary, control and generate a matching radar environment based on the generating function, and control the jammer to output a jamming wave corresponding to a jamming pattern using the jammer model;

[0122] An evaluation module, used for evaluating the interference effect of the interference wave, and calculating and determining a reward value corresponding to the interference effect;

[0123] An adjustment module is used to adjust the parameters of the jammer model according to the interference effect and the corresponding reward value, so that the adjusted jammer model can generate interference waves corresponding to the new interference style, and the interference effect of the interference wave corresponding to the new interference style is better than the interference effect of the interference wave corresponding to the previous interference style.

[0124] In one embodiment, the device further comprises:

[0125] The control module is used to control the jammer model to perform reinforcement learning for different radar environments until the jammer model can adaptively generate jamming waves that match the radar environment to the greatest extent under different radar environments.

[0126] In one embodiment, the step of setting a radar signal generation function based on configuration parameters of radars in two radar confrontation parties includes:

[0127] The radar signal generation function is set according to the radar parameters and radar signal form of the opponent's radar. The radar signal form includes linear frequency modulation signal, frequency coded signal, phase coded signal and complex modulation signal.

[0128] In one embodiment, the device further comprises:

[0129] The extraction module is used to extract the signal features of the received radar signal of the other party's radar in real time to obtain the radar parameters and radar signal form. The radar parameters include frequency, amplitude, and pulse width.

[0130] In one embodiment, the evaluating the interference effect of the interference wave includes:

[0131] Constructing an interference effect evaluation system based on different evaluation indicators, wherein the different evaluation indicators are used to evaluate the interference effect of the interference wave according to the states of the other party's radar after receiving the interference wave, responding to the interference wave, and being interfered by the interference wave;

[0132] The interference effect is evaluated based on the interference effect evaluation system to obtain an evaluation result of the interference effect.

[0133] In one embodiment, the construction of an interference effect evaluation system based on different evaluation indicators includes:

[0134] Constructing a CFAR detection threshold ratio evaluation index, wherein the CFAR detection threshold ratio is the ratio of the detection threshold value after interference is implemented to the detection threshold value when there is no interference;

[0135] Constructing a false target number evaluation index, wherein the false target number is the number of false targets generated by the jammer when implementing the current jamming;

[0136] Constructing an interference-to-signal amplitude ratio evaluation index, wherein the interference-to-signal amplitude ratio is a ratio between the overall average amplitude of the received opponent's radar signal after interference and the overall average amplitude of the originally received radar signal;

[0137] Constructing an interference-to-signal ratio evaluation index, wherein the interference-to-signal ratio describes the relationship between the interference wave and the target echo signal from the perspective of power, and is defined as the ratio of the power of the interference wave to the power of the original radar signal;

[0138] Determine an interference evaluation factor based on the original radar signal power, the interference wave power and the radar improvement factor, wherein the radar improvement factor is the ratio of the pulse pressure signal-to-noise ratio of the corresponding received opponent radar signal to the original signal-to-noise ratio;

[0139] The interference effect evaluation system is formed based on the CFAR detection threshold ratio evaluation index, the false target number evaluation index, the interference-to-signal amplitude ratio evaluation index, the interference-to-signal ratio evaluation index and the interference evaluation factor.

[0140] In one embodiment, the interference pattern includes intermittent sampling forwarding interference and noise convolution interference, wherein the parameters to be optimized in the intermittent sampling forwarding interference are the number of forwarding times and the forwarding signal amplitude, and the parameter to be optimized in the noise convolution interference is the noise time width.

[0141] In one embodiment, adjusting the parameters of the jammer model based on the jamming effect and the corresponding reward value includes:

[0142] Based on the interference effect and the corresponding reward value, the jammer model is trained by combining the reinforcement learning algorithm. Q The value is updated based on the Q The value implements the parameter adjustment of the model:

[0143] To solve the Q The value function of the value is:

[0144]

[0145] Said is the learning rate; is the discount factor; For the current t The reward value at the moment; For the current t The value function at time, S t For the current t The status of the enemy's radar at all times, A t For the current t Interference actions performed at all times; To execute the current t The value function of the interference action at the moment to the next state; for t The value function at time +1.

[0146] Another embodiment of the present invention further provides an electronic device, including:

[0147] one or more processors;

[0148] a memory configured to store one or more programs;

[0149] When the one or more programs are executed by the one or more processors, the one or more processors implement the radar interference waveform adaptive generation method as described in the above embodiment.

[0150] Furthermore, an embodiment of the present invention also provides a storage medium on which a computer program is stored, and when the program is executed by a processor, the radar interference waveform adaptive generation method as described above is implemented. It should be understood that each solution in this embodiment has the corresponding technical effect in the above method embodiment, which will not be repeated here.

[0151] Furthermore, an embodiment of the present invention also provides a computer program product, which is tangibly stored on a computer-readable medium and includes computer-readable instructions, which, when executed, enable at least one processor to perform a radar interference waveform adaptive generation method such as that in the embodiment described above.

[0152] It should be noted that the computer storage medium of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access storage medium (RAM), a read-only storage medium (ROM), an erasable programmable read-only storage medium (EPROM or flash memory), an optical fiber, a portable compact disk read-only storage medium (CD-ROM), an optical storage medium, a magnetic storage medium, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can send, propagate or transmit a program configured to be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, antenna, optical cable, RF, etc., or any suitable combination of the above.

[0153] In addition, it should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program codes.

[0154] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0155] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

Claims

1. A radar jammer waveform adaptive generation method, characterized in that: include: Setting a radar signal generation function based on radar configuration parameters in the opponent's radar, wherein the generation function constructs different radar environments by generating different radar signals; Constructing a jammer model for reinforcement learning, wherein the jammer model is applied to a jammer so that the jammer outputs an interference wave; In response to the received radar signal of the adversary, a matching radar environment is generated based on the generation function, and the jammer model is used to control the jammer to output a jamming wave corresponding to a jamming pattern; Evaluating the interference effect of the interference wave, and calculating and determining a reward value corresponding to the interference effect; Adjusting the parameters of the jammer model based on the jamming effect and the corresponding reward value, so that the adjusted jammer model can generate jamming waves corresponding to the new jamming pattern, and the jamming effect of the jamming waves corresponding to the new jamming pattern is better than the jamming effect of the jamming waves corresponding to the previous jamming pattern; The evaluating the interference effect of the interference wave comprises: Constructing an interference effect evaluation system based on different evaluation indicators, wherein the different evaluation indicators are used to evaluate the interference effect of the interference wave according to the states of the other party's radar after receiving the interference wave, responding to the interference wave, and being interfered by the interference wave; Evaluating the interference effect based on the interference effect evaluation system to obtain an evaluation result of the interference effect; The interference effect evaluation system is constructed based on different evaluation indicators, including: Constructing a CFAR detection threshold ratio evaluation index, wherein the CFAR detection threshold ratio is the ratio of the detection threshold value after interference is implemented to the detection threshold value when there is no interference; Constructing a false target number evaluation index, wherein the false target number is the number of false targets generated by the jammer when implementing the current jamming; Constructing an interference-to-signal amplitude ratio evaluation index, wherein the interference-to-signal amplitude ratio is a ratio between the overall average amplitude of the received opponent's radar signal after interference and the overall average amplitude of the originally received radar signal; Constructing an interference-to-signal ratio evaluation index, wherein the interference-to-signal ratio describes the relationship between the interference wave and the target echo signal from the perspective of power, and is defined as the ratio of the power of the interference wave to the power of the original radar signal; Determine an interference evaluation factor based on the original radar signal power, the interference wave power and the radar improvement factor, wherein the radar improvement factor is the ratio of the pulse pressure signal-to-noise ratio of the received opponent radar signal to the original signal-to-noise ratio; The interference effect evaluation system is formed based on the CFAR detection threshold ratio evaluation index, the false target number evaluation index, the interference-to-signal amplitude ratio evaluation index, the interference-to-signal ratio evaluation index and the interference evaluation factor.

2. The radar jammer waveform adaptive generation method according to claim 1, characterized in that: The method further comprises: The jammer model is controlled to perform reinforcement learning for different radar environments until the jammer model can adaptively generate jamming waves that match the radar environment to the greatest extent under different radar environments.

3. The radar jammer waveform adaptive generation method according to claim 1, characterized in that: The generating function of setting the radar signal based on the configuration parameters of the radar in the opponent's radar includes: The radar signal generation function is set according to the radar parameters and radar signal form of the opponent's radar. The radar signal form includes linear frequency modulation signal, frequency coded signal, phase coded signal and complex modulation signal.

4. The radar jammer waveform adaptive generation method according to claim 3, characterized in that: The method further comprises: The received radar signal of the other party's radar is subjected to real-time signal feature extraction to obtain the radar parameters and radar signal form. The radar parameters include frequency, amplitude, and pulse width.

5. The radar jammer waveform adaptive generation method according to claim 1, characterized in that: The interference patterns include intermittent sampling forwarding interference and noise convolution interference, wherein the parameters to be optimized in the intermittent sampling forwarding interference are the number of forwarding times and the forwarding signal amplitude, and the parameter to be optimized in the noise convolution interference is the noise time width.

6. The radar jammer waveform adaptive generation method according to claim 5, characterized in that: The adjusting the parameters of the jammer model based on the jamming effect and the corresponding reward value includes: Based on the interference effect and the corresponding reward value, the jammer model is trained by combining the reinforcement learning algorithm. Q The value is updated, and then based on the Q The value implements the parameter adjustment of the model: To solve the Q The value function of the value is: Said is the learning rate; is the discount factor; For the current t The reward value at the moment; For the current t The value function at time, S t For the current t The status of the enemy's radar at all times, A t For the current t Interference actions performed at all times; To execute the current t The value function of the interference action at the moment to the next state; for t The value function at time +1.

7. A radar jammer waveform adaptive generation device, characterized in that: include: A setting module, used for setting a generation function of a radar signal according to configuration parameters of the radar in the other party's radar, wherein the generation function constructs different radar environments by generating different radar signals; A first construction module is used to construct a jammer model for reinforcement learning, wherein the jammer model is applied to a jammer so that the jammer outputs an interference wave; A generating module, configured to respond to a received radar signal of an adversary, control and generate a matching radar environment based on the generating function, and control the jammer to output an interference wave corresponding to an interference pattern using the jammer model; An evaluation module, used for evaluating the interference effect of the interference wave, and calculating and determining a reward value corresponding to the interference effect; An adjustment module, used for adjusting the parameters of the jammer model according to the jamming effect and the corresponding reward value, so that the adjusted jammer model can generate an interference wave corresponding to a new interference pattern, and the interference effect of the interference wave corresponding to the new interference pattern is better than the interference effect of the interference wave corresponding to the previous interference pattern; The evaluating the interference effect of the interference wave comprises: Constructing an interference effect evaluation system based on different evaluation indicators, wherein the different evaluation indicators are used to evaluate the interference effect of the interference wave according to the states of the other party's radar after receiving the interference wave, responding to the interference wave, and being interfered by the interference wave; Evaluating the interference effect based on the interference effect evaluation system to obtain an evaluation result of the interference effect; The interference effect evaluation system is constructed based on different evaluation indicators, including: Constructing a CFAR detection threshold ratio evaluation index, wherein the CFAR detection threshold ratio is the ratio of the detection threshold value after interference is implemented to the detection threshold value when there is no interference; Constructing a false target number evaluation index, wherein the false target number is the number of false targets generated by the jammer when implementing the current jamming; Constructing an interference-to-signal amplitude ratio evaluation index, wherein the interference-to-signal amplitude ratio is a ratio between the overall average amplitude of the received opponent's radar signal after interference and the overall average amplitude of the originally received radar signal; Constructing an interference-to-signal ratio evaluation index, wherein the interference-to-signal ratio describes the relationship between the interference wave and the target echo signal from the perspective of power, and is defined as the ratio of the power of the interference wave to the power of the original radar signal; Determine an interference evaluation factor based on the original radar signal power, the interference wave power and the radar improvement factor, wherein the radar improvement factor is the ratio of the pulse pressure signal-to-noise ratio of the received opponent radar signal to the original signal-to-noise ratio; The interference effect evaluation system is formed based on the CFAR detection threshold ratio evaluation index, the false target number evaluation index, the interference-to-signal amplitude ratio evaluation index, the interference-to-signal ratio evaluation index and the interference evaluation factor.

8. An electronic device, characterized in that: include: one or more processors; a memory configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the radar interference waveform adaptive generation method according to any one of claims 1 to 6.

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