Cognition-based radar synthesis anti-mainlobe interference method

Abstract

This invention proposes a cognitive-based comprehensive radar anti-main lobe interference method, comprising: detecting interference signals using the antenna front-end based on the actual hardware of the radar equipment, and measuring and predicting interference parameters and behaviors; implementing complex diversity waveforms and coherent accumulation in waveform selection and signal processing; adding anti-interference decision-making to the current radar signal processing to generate anti-interference strategies; using partial aperture resources for high-resolution direction finding of interference sources; performing data interference removal based on signal processing results and target prior information to achieve stable target tracking; and verifying and evaluating the anti-interference effect through current experiments. This invention comprehensively considers multiple dimensions (time, frequency, space, and data) and multiple aspects (antenna system, receiving system, signal processing, and data processing system) of the radar, combining intelligent cognitive concepts to construct a relatively complete main lobe interference countermeasure system. This architecture can be applied to various radar systems.

Description

Technical Field

[0001] This application relates to the field of radar detection technology, and in particular to a cognitive-based radar integrated anti-main lobe interference method. Background Technology

[0002] Faced with main lobe interference such as targeting frequency jamming and relay jamming, radar tracking accuracy is drastically reduced, and it may even be difficult to intercept targets. Therefore, how to effectively deal with main lobe interference is a difficult problem in the field of radar detection.

[0003] Currently, radar anti-mainlobe interference typically employs transmit waveform parameter agility, which involves frequency and time agility and encoding of the transmitted signal to counter jammer detection and forwarding. Secondly, methods such as blocking matrices and subspace projection are often used in signal preprocessing and signal processing to achieve spatial filtering of interference. However, these anti-jamming methods are relatively simplistic and lack a comprehensive systemic approach. Therefore, to better and more efficiently cope with complex and ever-changing interference patterns, constructing a cognitive-decision closed-loop anti-jamming system is a highly effective comprehensive radar anti-jamming measure. Summary of the Invention

[0004] In view of this, the present invention provides a cognitive-based radar synthesis method for resisting main lobe interference.

[0005] The present invention proposes a cognitive-based radar integrated anti-main lobe interference method, comprising: A cognitive-based radar synthesis method for resisting main lobe interference, characterized by comprising: Step 1: Based on the actual hardware of the radar equipment, use the antenna front end to detect interference signals, measure interference parameters, and predict behavior. Step 2: Implement complex diversity waveforms and coherent accumulation in waveform selection and signal processing; Step 3: Add anti-jamming decision-making to the current radar signal processing to generate anti-jamming strategies; Step 4: Utilize partial aperture resources for high-resolution direction finding of interference sources; Step 5: Based on the signal processing results and prior target information, interference is eliminated from the data to achieve stable tracking of the target; Step 6: Verify the current experiment and evaluate its anti-interference effect.

[0006] In one embodiment, the step of using the antenna front end to detect interference signals, measure interference parameters, and predict behavior, based on the actual hardware of the radar equipment, includes: channelized reception of interference signals, measurement of interference signal parameters, identification of interference sources, and prediction of interference behavior.

[0007] In one implementation, the process of achieving complex diversity waveforms and coherent accumulation in waveform selection and signal processing... Complex diversity waveforms take the form of inter-pulse frequency agility, inter-pulse coding agility, inter-pulse time agility, and intra-pulse multi-frequency modulation.

[0008] In one implementation, adding anti-jamming decision-making to the current radar signal processing and generating an anti-jamming strategy includes: By combining the electromagnetic spectrum distribution of the interference signal, the prediction results of the jammer's behavior, and the radar operating parameters, a closed-loop cognitive anti-main lobe interference decision network of radar and environment is constructed using deep learning algorithms. The target tracking results obtained through radar interaction with the environment are used to evaluate the effectiveness of the current anti-jamming strategy based on a deep neural network. At the same time, the network parameters are iteratively updated as feedback to optimize the strategy.

[0009] In one embodiment, the high-resolution direction finding method for interference sources is performed by utilizing a portion of the aperture resources. When the radar broadband detection equipment detects the jammer parameter information, DOA estimation is performed using a portion of the radar array resources.

[0010] In one implementation, the verification and anti-interference effect evaluation of the current experiment includes: A broadband receiving channel was added to the front end of the reused radar antenna, and various types of jamming were released simultaneously to target the UAV-borne jammer and conduct field tests. In the experiment, a test scenario was constructed based on the radar beam direction and detection power. The flight altitude and flight path of the UAV were set, and the target tracking results before and after the UAV released interference were compared.

[0011] Another aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the cognitive-based radar integrated anti-main lobe interference method as described in any of the preceding claims.

[0012] Another aspect of the present invention provides a computer storage medium storing a computer program that is executed to implement the cognitive-based radar integrated anti-main lobe interference method as described in any of the preceding claims.

[0013] By adopting the above technical solution, the present invention has at least the following advantages: The method provided by this invention comprehensively considers multiple dimensions—time, frequency, space, and data—and multiple aspects of the radar's antenna system, receiving system, signal processing, and data processing system, combining intelligent cognitive concepts to construct a relatively complete main lobe jamming countermeasure system. This architecture can be applied to various radar types, has good applicability, and fully considers engineering feasibility, providing important technical support for radar main lobe anti-jamming. Attached Figure Description

[0014] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a schematic diagram of a cognitive-based radar integrated anti-mainlobe interference method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the logic of the cognitive-based radar integrated anti-main lobe interference method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0015] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments.

[0016] While exemplary embodiments of the invention are shown in the accompanying drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey its scope to those skilled in the art. The invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0017] This invention provides a cognitive-based radar synthesis method for resisting main lobe interference, combined with... Figure 1 This includes the following steps: Step 1: Based on the actual hardware of the radar equipment, use the antenna front end to detect interference signals, measure interference parameters, and predict behavior. Step 2: Implement complex diversity waveforms and coherent accumulation in waveform selection and signal processing; Step 3: Add anti-jamming decision-making to the current radar signal processing to generate anti-jamming strategies; Step 4: Utilize partial aperture resources for high-resolution direction finding of interference sources; Step 5: Based on the signal processing results and prior target information, interference is eliminated from the data to achieve stable tracking of the target; Step 6: Verify the current experiment and evaluate its anti-interference effect.

[0018] The following will combine Figure 2 The method provided in this embodiment will be described in detail.

[0019] The following steps are taken to construct a cognitive-based radar integrated anti-main lobe interference system architecture: Step 1: Based on the actual hardware of the radar equipment, use the antenna front end to detect interference signals, measure interference parameters and predict behavior. This mainly includes channelized reception of interference signals, measurement of interference signal parameters, identification of interference sources and prediction of interference behavior. 1) Reconnaissance and reception strategy based on digital channelization The basic idea of ​​digital channelized reception is to divide the frequency band to be monitored into several equal-width sub-bands, and then shift these sub-bands to the baseband for subsequent digital signal processing. The process of digital channelized reception is as follows: first, the radio frequency signal is down-converted to an intermediate frequency (IF); then, a high-speed ADC is used to sample the IF signal to obtain a digital signal; the digital signal passes through a channelization structure to obtain a low-data-rate baseband signal; and subsequent algorithms, such as signal detection, are then applied to the baseband signal.

[0020] 2) Joint measurement strategy for interference signal characteristic parameters This study extracts characteristic parameters of active radar interference in the time, frequency, and other domains to provide a feature parameter library for identifying the types of active radar interference. The extracted feature parameters must be discriminative; that is, for a given feature value, one type of interference must be significantly different from other types of interference. This is necessary to distinguish different types of interference signals and thus classify the interference signals. Simultaneously, the extracted feature parameters must be weakly sensitive to noise, meaning they should still be extractable even under conditions of low noise levels. The main interference signal characteristic parameters include time / frequency / energy domain parameters such as signal energy, signal frequency, and signal duration.

[0021] 3) Interference source identification strategy based on decision tree model Based on the parameter measurement results, the characteristic information of each interference source is summarized, and parameters such as frequency, pulse width, and pulse repetition interval are extracted as features for interference source identification. Based on prior information in the interference identification database (such as frequency band, pulse repetition frequency, pulse width, etc.), an interference classifier is used to classify and identify the signals. The interference feature information obtained after comprehensive analysis and statistical processing will be used to further update the interference identification database.

[0022] Decision trees can be used for classification, making a binary choice at each node. The classification process involves two main steps: First, a decision tree model is built using training sample data. This involves finding and learning classification rules from known samples to determine the threshold values ​​for each feature parameter. Then, the established decision tree model is used to classify unknown input samples, effectively classifying interference types. The decision tree classification process starts from the root node and proceeds downwards, comparing threshold values ​​at internal nodes and determining whether to execute the left or right branch based on the result. This process continues until a leaf node is reached, at which point the classification is complete.

[0023] 4) Interference behavior prediction based on deep learning networks By utilizing refined signal parameter measurements and combining interference cognition and interference source identification results, a query network based on a multilayer perceptron is constructed to achieve end-to-end mapping between environmental perception results and jammer behavior prediction. The network adopts a learning method and can respond to test data in real time. By minimizing the error between the predicted results and the actual results, the model parameters are trained offline, and the jammer strategy library is updated by storing and analyzing new interference signals detected.

[0024] Step 2: Based on the actual hardware of the radar equipment, implement complex diversity waveforms and coherent accumulation in waveform selection and signal processing; Complex diversity waveforms are mainly characterized by inter-pulse frequency agility, inter-pulse coding agility, inter-pulse time agility, and intra-pulse multi-frequency modulation. By rapidly changing waveform parameters, the interception probability of the jammer is reduced, and the forwarding lag is delayed, which is beneficial for separating the jamming signal and the echo signal, thereby achieving anti-jamming.

[0025] However, given the nonlinear phase changes in the echo of agile waveforms, traditional methods are insufficient for coherent processing, as non-coherent accumulation results in significant losses. Therefore, a spectral search approach is needed to compensate for the phase changes caused by parameter agility, thereby solving the coherent accumulation problem and ensuring stable radar performance while mitigating interference.

[0026] The third step is to add an anti-jamming decision-making component to the original radar signal processing to achieve intelligent and autonomous generation of anti-jamming strategies, and to interconnect and exchange information with signal processing, data processing, jamming signal detection, and radar transmission selection. By combining information such as the electromagnetic spectrum distribution of interference signals, prediction results of jammer behavior, and radar operating parameters, a radar-environment closed-loop cognitive anti-mainlobe interference decision network is constructed using deep learning algorithms. Target tracking results obtained through radar-environment interaction are used to evaluate the effectiveness of the current anti-jamming strategy based on a deep neural network, and this evaluation serves as feedback to iteratively update the network parameters, thereby optimizing the strategy. The algorithm employs an offline learning method, enabling rapid responses to complex and changing environments, fully utilizing radar resources, and effectively improving the radar's ability to resist mainlobe interference.

[0027] Step 4: For phased array bulk radar, some aperture resources can be used for high-resolution direction finding of interference sources, which can be used as an auxiliary means to eliminate interference during beamforming; When the radar broadband detection equipment detects jammer parameter information, it uses part of the radar array resources to estimate DOA. Through array resource optimization, it maximizes the use of aperture resources for jammer direction finding without affecting the radar detection function, and performs jamming processing and suppression in the airspace dimension.

[0028] Step 5: In the final data processing stage, interference is removed from the data based on the signal processing results and prior information about the target. During data processing, interference is separated and eliminated by combining signal processing results with prior information such as parameter agility, and using trajectory association and clustering algorithms.

[0029] Step 6: Experimental verification and evaluation of anti-interference effect.

[0030] Based on the above steps, adaptive modifications are made to the radar equipment that needs improvement, and a cognitive-based radar integrated anti-jamming system architecture is constructed. Its anti-jamming capability is then verified through experiments.

[0031] By reusing the radar antenna front end and adding a broadband receiving channel, various types of jamming were simultaneously released to target a UAV-borne jammer in a field test. During the test, a test scenario needed to be constructed based on radar beam pointing and detection power, and the UAV's flight altitude and flight path were set. The target tracking results before and after the UAV released jamming were compared, including parameters such as signal-to-noise ratio and track continuity.

[0032] The effectiveness of the cognitive-based radar integrated anti-mainlobe jamming system architecture was verified through field jamming countermeasure experiments, and the measured data were statistically analyzed. The data analysis mainly included two aspects: First, the measurement and identification results of jamming parameters. After the reconnaissance module detects different types of jamming, it measures the parameters of the received jamming signals in real time, including the jamming start frequency, termination frequency, and peak power. Based on the measurement information, the type of jamming is determined, and anti-jamming strategies are generated in terms of waveform selection and parameter selection. Second, the target tracking performance under jamming conditions is analyzed, including range tracking curve error, velocity tracking curve error, and azimuth and elevation angle tracking curve error. Statistical analysis of systematic and random errors is used to determine tracking stability.

[0033] Compared to existing technologies, this embodiment can be used for: A closed-loop radar anti-main lobe interference system architecture based on interference environment cognition is constructed. Through interference cognition and behavior prediction, autonomous anti-interference strategy generation, radar parameter agility, high-resolution direction finding of interference sources, interference data separation and hierarchical elimination, and adaptive track association and detection, bidirectional feedback iteration and closed-loop operation are carried out from multiple dimensions of time, frequency, space and data. Ultimately, comprehensive interference suppression is achieved, enabling target tracking with accuracy under main lobe interference and ensuring the effectiveness of radar in complex electromagnetic environments.

[0034] When the radar is operating normally, it first transmits signals and detects targets in normal operating mode. Simultaneously, using a shared radar antenna and a broadband reception channel, it determines the interference status within the radar's operating frequency band, senses the signal spectrum distribution, classifies and identifies interference types, and recognizes and predicts jammer behavior. Based on the interference parameter measurement results and radar search and tracking results, a comprehensive judgment and decision is made to determine the radar's operating state and optimal parameter selection for the next cycle. The specific workflow is as follows: First, the broadband reconnaissance receiver completes signal acquisition and parameter measurement, obtains the signal distribution within the radar's operating bandwidth, and simultaneously classifies, identifies, and predicts the interference detection time window; II. Secondly, the anti-jamming decision system makes a comprehensive decision based on the reconnaissance results and the radar search and tracking processing results, choosing whether to enter the anti-jamming working mode and what type of jamming countermeasure strategy to select. On the one hand, it uses the jamming detection time window obtained from reconnaissance to control the generation of fixed-frequency decoy signals to induce and drag the jammer, while guiding the signal processing system to process the agile waveform of such parameters to achieve target detection; on the other hand, it sends the anti-jamming decision control signal to the radar front end to plan the signal frequency and waveform parameters to be transmitted by the system in the next frame. III. Simultaneously, guided and instructed by anti-interference decisions, the signal processing system completes signal processing algorithms under anti-interference working mode, including array adaptive interference suppression, coherent accumulation, clutter suppression, target detection, and multipath error suppression. IV. Then, based on the signal processing results and the interference type and feature information given by the anti-interference decision, the back-end data processing system completes the separation and elimination of interference at the data level, as well as the stable tracking of the target, and feeds back the tracking results to the anti-interference decision. Finally, from antenna reception to cognitive decision-making and then to signal data processing, a closed-loop system is formed. This task cycle is continuously performed during radar operation to achieve comprehensive radar anti-interference.

[0035] Another embodiment of the present invention provides an electronic device, such as... Figure 3 As shown, it includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the cognitive-based radar integrated anti-main lobe interference method as described in the first embodiment.

[0036] In another embodiment of the present invention, a computer storage medium stores a computer program that is executed to implement the cognitive-based radar integrated anti-main lobe interference method as described in the first embodiment.

[0037] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims. All of these forms are within the protection scope of this application.

Claims

1. A cognitive-based radar method for synthesizing anti-mainlobe interference, comprising: The method comprises the following steps: Step 1: According to the actual hardware condition of the radar equipment, interference signal detection is carried out by using the antenna front end, interference parameter measurement and behavior prediction are carried out; Step 2: Complex diversity waveform and coherent accumulation are realized in waveform selection and signal processing; Step 3: Anti-interference decision is added in current radar signal processing, and anti-interference strategy is generated; Step 4: High-resolution direction finding of the interference source is carried out by using part of the aperture resource; Step 5: According to the signal processing result and the prior information of the target, interference elimination on the data is carried out, and stable tracking of the target is realized; Step 6: The current test is verified and the anti-interference effect is evaluated.

2. The cognitive-based radar synthetic anti-jamming method of claim 1, wherein, According to the actual hardware condition of the radar equipment, interference signal detection is carried out by using the antenna front end, interference parameter measurement and behavior prediction are carried out, which comprises interference signal channelization reception, interference signal parameter measurement, interference source identification and interference behavior prediction.

3. The cognitive-based radar synthetic anti-jamming method of claim 1, wherein, In the realization of complex diversity waveform and coherent accumulation in waveform selection and signal processing, The complex diversity waveform form is pulse-to-pulse frequency agility, pulse-to-pulse coding agility, pulse-to-pulse time agility and pulse-in multi-frequency modulation.

4. The cognitive-based radar synthetic anti-jamming method of claim 1, wherein, The anti-interference decision is added in current radar signal processing, and anti-interference strategy is generated, which comprises: Combining the electromagnetic spectrum distribution of the interference signal, the interference behavior prediction result and the radar working parameter, a radar and environment closed-loop cognitive anti-main lobe interference decision network is constructed by using a deep learning algorithm; The target tracking result obtained through the interaction between the radar and the environment is used to evaluate the effect of the current anti-interference strategy based on a deep neural network, and is used as feedback to update the network parameters, so that the strategy optimization is realized.

5. The cognitive-based radar synthetic anti-jamming method of claim 1, wherein, When the radar wideband detection equipment detects the interference machine parameter information, the DOA estimation is carried out by using part of the array resource of the radar.

6. The cognitive-based radar synthetic anti-jamming method of claim 1, wherein, The current test is verified and the anti-interference effect is evaluated, which comprises: In the multiplexing radar antenna front end, a wideband receiving channel is added, and various types of interference are released simultaneously to unmanned aerial interference machines as targets for field test; In the test, the test scene is constructed according to the radar beam pointing and detection power, the flight height and flight route of the unmanned aerial vehicle are set, and the target tracking results before and after the unmanned aerial vehicle releases interference are compared.

7. An electronic device, comprising: The method comprises a memory and a processor, the memory stores a computer program, and the processor is used to execute the computer program to realize the cognitive-based radar comprehensive anti-main lobe interference method according to any one of claims 1 to 6.

8. A computer storage medium, characterized in that The medium stores a computer program, and the computer program is executed to realize the cognitive-based radar comprehensive anti-main lobe interference method according to any one of claims 1 to 6.

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