Satellite-borne cognitive sar ground radio frequency interference suppression method and system

Abstract

This invention provides a method and system for suppressing ground radio frequency interference in spaceborne cognitive SAR, comprising: step S1: interference feature extraction and dynamic knowledge base construction; step S2: interference suppression strategy and beam control parameter pre-planning; step S3: on-orbit real-time suppression execution and parameter adaptive adjustment; step S4: quantitative evaluation of suppression effect and iterative learning of strategy. Step S1 specifically includes: initializing and continuously updating the dynamic interference source knowledge base, wherein the initialization data sources of the knowledge base include archived data acquired before system initialization and prior interference information acquired from other sources; if no initialization data is available, this step is executed after the first observation mission acquires echo data to complete the initial construction, and the knowledge base is continuously updated by processing data accumulated from previous observation missions during system operation; the dynamic knowledge base contains the geographic coordinates, frequency characteristics, power levels, radiation characteristics, and temporal evolution data of the interference sources.

Description

Technical Field

[0001] This invention relates to the field of spaceborne radar signal processing technology, and more specifically, to a method and system for suppressing ground radio frequency interference in spaceborne cognitive SAR. Background Technology

[0002] Spaceborne synthetic aperture radar (SAR), as an important technology for Earth observation, achieves high-precision imaging by transmitting microwave signals and receiving echoes from ground objects. However, increasingly complex ground-based radio frequency interference (RFI) has become a key factor restricting the improvement of image quality and posing a serious threat to imaging results. Existing technologies typically use static filtering methods for RFI suppression, but the filtering parameters are difficult to adapt to the dynamic changes of interference sources, resulting in insufficient interference suppression or signal distortion. In addition, existing technologies lack the ability to recognize and learn from the interference environment, and cannot perform forward-looking strategy planning and online adaptive adjustment for specific scenarios, thus limiting the interference suppression effect. To address the above problems, this invention proposes a spaceborne cognitive SAR ground-based radio frequency interference suppression method and system. By constructing a dynamic interference knowledge base, pre-planning suppression strategies, implementing real-time adaptive execution in orbit, and providing effect evaluation feedback in a closed-loop cognitive process, intelligent perception and precise suppression of dynamic radio frequency interference are achieved, significantly improving the imaging quality and system adaptability of spaceborne SAR in complex electromagnetic environments.

[0003] The patent "A Frequency-Agile Cognitive SAR Anti-jamming Strategy Generation Method Based on Action Transfer-Demonstration Learning" (CN120706496A) describes a method for generating frequency-agile cognitive SAR anti-jamming strategies based on action transfer-demonstration learning. This method utilizes Markov decision process modeling and a two-stage reward setting, leveraging transfer learning to accelerate strategy generation and improve SAR anti-jamming strategy performance. However, this method focuses on the strategy learning algorithm, neglects adaptive adjustment of real-time beam control parameters, relies on preset interference modes, and fails to fully utilize hardware adaptive mechanisms such as antenna pattern notch formation, resulting in low robustness in dynamic and complex interference environments.

[0004] The patent "A Radar Anti-jamming Method and System Based on Self-Awareness" (CN119575319A) constructs a deep neural network to perform matched filtering and pulse normalization processing on radar echo signals. It utilizes a one-dimensional convolutional neural network and a long short-term memory network to extract signal features, achieving effective identification and suppression of interference signals. However, the parameters of this method depend on the pre-trained neural network model, lacking a dynamic knowledge base and real-time adaptive mechanism. This results in the inability to adjust online according to the spatiotemporal changes of the interference source in complex electromagnetic environments. Furthermore, it focuses on pulse-level processing and does not involve antenna pattern optimization, leading to insufficient adaptability in spaceborne scenarios due to fixed parameters.

[0005] The patent "A Method and System for Active Anti-jamming Waveform Generation of Cognitive Radar Based on DQN and MAB" (CN119959889A) uses a deep Q-network and a multi-armed slot machine algorithm to dynamically generate anti-jamming waveforms. It optimizes waveform parameters through time-frequency analysis and CFAR detection, demonstrating the strong active anti-jamming capability of cognitive radar. However, this method focuses on radar waveform design rather than the overall framework for spaceborne SAR interference suppression. The waveform strategy library lacks a dynamic knowledge base or antenna beam control parameter planning, making it impossible to implement antenna pattern suppression strategies. Furthermore, parameter optimization relies on offline training and lacks real-time iterative learning, limiting its effectiveness in complex space-ground geometries due to the fixed waveform.

[0006] The patent "Cognitive Radar Waveform Design Method for Resisting Instantaneous Transponder Slice Reconstruction Interference" (CN106443595A) proposes a waveform design method based on estimating interference parameters from echo signals and using phase coding of a cost function that minimizes the weighted integral sidelobe level to suppress slice reconstruction interference and improve radar target detection capabilities. However, the suppression parameters of this method depend on a single, fixed estimation, making it difficult to cope with the time-varying characteristics of interference sources in complex electromagnetic environments. Furthermore, the pure waveform design approach does not utilize antenna pattern optimization, lacks spatial domain suppression capabilities, and does not involve continuous evaluation and closed-loop optimization of the suppression strategy, limiting its robustness in long-term spaceborne missions.

[0007] The paper "Waveform Design for Cognitive Radar Interference and Sidelobe Equalization Suppression" (Wu Yue et al., Radar Science and Technology, 2018, 16(01):61-67.) proposes a waveform optimization method based on the MMSE criterion and the Lagrange multiplier method. This method achieves equal suppression of interference and sidelobes by iteratively optimizing the transmitted waveform and the received filter, thereby improving the signal-to-interference ratio (SIR) of the output signal. However, this method focuses on offline waveform design and does not employ a dynamic knowledge base or real-time parameter adaptive mechanism. It relies on prior interference information and cannot adapt to the dynamic evolution of interference sources in a spaceborne environment. Furthermore, this method only suppresses interference through filters and does not utilize antenna pattern optimization, thus reducing its suppression effectiveness in complex radio frequency interference environments.

[0008] The paper "Avoidance of Time-Varying Radio Frequency Interference With Software-Defined Cognitive Radar" (BH Kirk et al., IEEE Transactions on Aerospace and Electronic Systems, vol.55, no.3) proposes using software-defined cognitive radar to achieve a perception-action loop. It suppresses time-varying radio frequency interference through fast spectrum sensing (FSS) and frequency adaptation, improving real-time responsiveness. It can adjust the radar's operating frequency band on a millisecond timescale to maximize bandwidth and signal-to-interference ratio. However, this method mainly relies on frequency avoidance strategies and does not employ antenna pattern optimization techniques, thus limiting its ability to suppress sidelobe interference. Furthermore, based on reactive adaptation, it lacks dynamic knowledge base support and pre-planning mechanisms, making it unable to effectively cope with the complex and ever-changing interference sources in the spaceborne environment.

[0009] The paper "Adaptive waveform design for interference mitigation in SAR" (Claire Tierney, Bernard Mulgrew, Signal Processing, 2021, vol.178) proposes an adaptive waveform design scheme for SAR systems. This scheme suppresses radio frequency interference through frequency domain system identification and waveform optimization. It utilizes generalized least squares estimation and stationary phase approximation to synthesize a nonlinear frequency-modulated waveform to minimize range profile estimation error, achieving waveform adaptation at the pulse level with high computational efficiency. However, this method lacks real-time environmental awareness and dynamic knowledge base updates; interference suppression mainly relies on waveform spectrum adjustment and does not utilize spatial domain suppression techniques such as antenna pattern design, making it difficult to effectively handle the dynamic changes of multi-source interference in spaceborne SAR.

[0010] In summary, existing radio frequency interference suppression methods employ static filtering or predefined waveform design, making it difficult to adapt parameter design to the dynamic changes of interference sources and lacking online awareness and learning capabilities regarding the interference environment. Therefore, there is an urgent need to develop SAR interference suppression capabilities that integrate dynamic interference sensing, strategy planning, and adaptive optimization to achieve dynamic and precise suppression of ground-based SAR radio frequency interference. Summary of the Invention

[0011] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method and system for suppressing ground radio frequency interference in spaceborne cognitive SAR.

[0012] According to one aspect of the present invention, a method for suppressing ground radio frequency interference of spaceborne cognitive SAR includes:

[0013] Step S1: Interference feature extraction and dynamic knowledge base construction; Step S2: Interference suppression strategy and pre-planning of beam control parameters; Step S3: On-orbit real-time suppression execution and parameter adaptive adjustment; Step S4: Quantitative evaluation of the inhibition effect and iterative learning of the strategy.

[0014] Preferably, step S1 specifically includes: The system initializes and continuously updates a dynamic interference source knowledge base. The initialization data sources for the knowledge base include archived data acquired before system initialization and prior interference information acquired from other sources. If no initialization data is available, this step is performed after the first observation mission acquires echo data to complete the initial construction. During system operation, the system continuously processes data accumulated from previous observation missions to update the knowledge base. The dynamic knowledge base contains the geographic coordinates, frequency characteristics, power levels, radiation characteristics, and temporal evolution data of the interference sources.

[0015] Preferably, step S1, interference feature extraction and dynamic knowledge base construction, includes: Step S1.1: Frequency domain identification of interference modes: The power spectral density estimation method is used to perform spectral analysis on the data used to build the knowledge base, extract the frequency characteristics and power level of the interference signal, and determine the interference modulation characteristics to identify the interference mode, which is used to guide the selection of interference suppression strategies. Step S1.2: Image domain acquisition of interference source location and radiation characteristics: Automatic classification and segmentation of SAR images are performed using machine learning algorithms to extract image features to identify and locate interference source areas, and the radiation characteristics of interference sources in the interference areas are analyzed. The radiation characteristics of interference sources include the average apparent intensity, imaging features, and the trend of their radiation characteristics with the radar incident angle as shown on the SAR image. Step S1.3: Establish time evolution data and update the dynamic database: Store the frequency characteristics, power levels, and radiation characteristics data of the same interference source obtained at different transit times in a time series, establish time evolution data, form corresponding interference source files, and enable the initial construction and continuous updating of the dynamic interference source knowledge base.

[0016] Preferably, step S2 specifically includes: Before the satellite performs its observation mission, based on the predicted satellite orbit parameters and antenna beam pointing, and combined with the interference source knowledge base, the suppression strategy and antenna pattern for the entire observation arc are planned in advance.

[0017] Preferably, step S2 includes: Step S2.1: Divide the observation spatiotemporal grid and calculate the relative geometric relationship between the satellite and the interference source: Based on the predicted satellite orbit, the observation arc is discretized into a series of continuous spatiotemporal points; for each spatiotemporal point, the relative geometric relationship between the satellite and each interference source in the knowledge base is calculated to obtain the azimuth and elevation angles of the interference source relative to the satellite. Step S2.2: Pre-determine the interference source region: Based on the calculation results of each spatiotemporal point in step S2.1, compare the azimuth and elevation angles of each interference source with the main lobe width and side lobe range of the antenna pattern. Based on a preset threshold, pre-determine whether each interference source is located in the main lobe region or the side lobe region of the antenna pattern at that spatiotemporal point. Step S2.3: Generate suppression strategy sequence: Based on the judgment result, generate corresponding suppression strategies and parameters for each spatiotemporal point; store the control parameters of all spatiotemporal points in time sequence for use by the satellite during on-orbit execution.

[0018] Preferably, in step S2.3, generating a corresponding suppression strategy and parameters for each spatiotemporal point specifically includes: If the interference source is located in the sidelobe region, the plan is to use antenna pattern optimization to suppress it. The phase and amplitude weights of the phased array antenna are dynamically adjusted using the antenna pattern optimization design method. The phased array wave control code that makes the antenna pattern form a notch in the direction of interference is pre-calculated and generated, so that the antenna pattern forms a notch in the direction of interference to reduce the gain. If the interference source is located in the main lobe region, matched filtering suppression is planned. The echo signal is convolved based on the interference parameters in the knowledge base. Matched filter coefficients for filtering out the interference are pre-designed to suppress the interference components while retaining the target signal.

[0019] Preferably, step S3 specifically includes: When the satellite is performing an observation mission, based on the real-time satellite position and velocity information, the suppression strategy instructions and key parameters for the corresponding spatiotemporal point, which are pre-calculated and stored in the interference suppression strategy and beam control parameter pre-planning in step S2, are invoked to drive the phased array antenna and signal processor to perform real-time interference suppression and adaptively adjust the strategy. If the real-time interference suppression strategy indicates sidelobe suppression, then the pre-stored beam control code is loaded into the beam control system of the phased array antenna, so that the antenna pattern forms a notch in the direction of interference. If the real-time interference suppression strategy indicates main lobe suppression, then the pre-stored filter coefficients are loaded into the matched filter of the signal processor to filter the echo signal. The adaptive adjustment strategy for real-time interference suppression includes: real-time monitoring and analysis of new observed echo data after loading suppression parameters; if the suppression effect is not as expected or unexpected interference characteristics are detected, the adaptive adjustment mechanism is triggered to fine-tune the initial suppression parameters in real time based on a preset optimization algorithm to adapt to changes in interference sources and environmental conditions.

[0020] Preferably, step S4 specifically includes: After observation, echo data is collected to evaluate the suppression effect and update the interference source knowledge base to optimize subsequent suppression strategies and achieve continuous learning. Preferably, step S4 includes: Step S4.1: Imaging and Quality Assessment: Apply SAR imaging algorithms to the suppressed echo data to generate an image with suppressed interference, and calculate image quality indicators, including local signal-to-interference ratio and integral sidelobe ratio. Step S4.2: Knowledge base closed-loop update: Compare the actual image quality indicators of imaging and quality assessment in step S4.1 with the expected suppression effect of interference suppression strategy and beam control parameters pre-planned in step S2. Combine the adaptive adjustment record of on-orbit real-time suppression execution and parameter adaptive adjustment in step S3 to perform closed-loop correction of the parameters in the dynamic interference source knowledge base. The parameter correction includes interference source power level and center frequency. Step S4.3: Strategy Iteration Optimization: Using reinforcement learning algorithms to maximize the signal-to-interference ratio while minimizing the negative impact on imaging quality, optimize the interference suppression strategy and pre-planning strategy of waveguide parameters in step S2, and the adaptive adjustment algorithm of on-orbit real-time suppression execution and parameter adaptive adjustment in step S3, so as to improve the subsequent suppression efficiency and adaptive capability.

[0021] According to another aspect of the present invention, a spaceborne cognitive SAR ground radio frequency interference suppression system includes: Module M1: Interference Feature Extraction and Dynamic Knowledge Base Construction; Module M2: Interference suppression strategy and pre-planning of beam control parameters; Module M3: On-orbit real-time suppression execution and parameter adaptive adjustment; Module M4: Quantitative evaluation of suppression effect and iterative learning of strategy.

[0022] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention solves the technical problem of static interference information and difficulty in adapting to spatiotemporal dynamic changes in existing methods by constructing a dynamic interference source knowledge base and integrating frequency domain and image domain feature extraction techniques, thereby improving the perception capability of spaceborne SAR in complex electromagnetic environments and the pertinence of interference suppression.

[0023] 2. Based on satellite orbit prediction and dynamic knowledge base, this invention pre-plans suppression strategies and wave control parameters within the observation arc. Through multi-domain joint optimization of space, time and frequency, it realizes an adaptive suppression mechanism for antenna pattern notch formation and matched filtering, thereby enhancing the anti-interference efficiency, signal fidelity and real-time response capability of the spaceborne SAR imaging system.

[0024] 3. This invention utilizes a closed-loop cognitive process of quantitative evaluation of suppression effect and iterative learning of strategy, and uses reinforcement learning algorithm to provide real-time feedback and adjustment of suppression strategy. Through online learning and continuous optimization, it achieves autonomous improvement of system performance, enhances the robustness of spaceborne SAR in the face of radio frequency interference and the stability of imaging quality in long-term missions, and makes up for the shortcomings of traditional methods in adaptability to complex electromagnetic environments. Attached Figure Description

[0025] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart of a method for suppressing ground radio frequency interference in spaceborne cognitive SAR.

[0026] Figure 2 This is a structural diagram of a spaceborne cognitive SAR ground radio frequency interference suppression system. Detailed Implementation

[0027] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0028] This embodiment provides a method for suppressing ground radio frequency interference in spaceborne cognitive SAR, including: Step S1: Interference feature extraction and dynamic knowledge base construction; Step S2: Interference suppression strategy and pre-planning of beam control parameters; Step S3: On-orbit real-time suppression execution and parameter adaptive adjustment; Step S4: Quantitative evaluation of the inhibition effect and iterative learning of the strategy.

[0029] Specifically, step S1, interference feature extraction and dynamic knowledge base construction, involves initializing and continuously updating a dynamic interference source knowledge base. The initialization data sources for the knowledge base include archived data acquired before system initialization and prior interference information obtained from other sources.

[0030] If no initialization data is available, this step is performed after the first observation mission acquires echo data to complete the initial construction. During system operation, data accumulated from previous observation missions is continuously processed to update the knowledge base. The dynamic knowledge base includes the geographic coordinates, frequency characteristics, power levels, temporal evolution data, and radiation characteristics of the interference source.

[0031] Specifically, step S1, interference feature extraction and dynamic knowledge base construction, includes: Step S1.1: Frequency domain identification of interference patterns; Step S1.2: Obtain the location and radiation characteristics of the interference source in the image domain; Step S1.3: Establish time evolution data and update the dynamic database.

[0032] Step S1.1 employs a power spectral density estimation method (such as the Burg method or Welch method) to perform spectral analysis on the data used to build the knowledge base, extracts the frequency characteristics and power level of the interference signal, and determines the interference modulation characteristics to identify the interference mode, which is used to guide the selection of interference suppression strategies. Step S1.2 involves SAR imaging processing and obtaining the location and radiation characteristics of interference sources. Machine learning algorithms (such as convolutional neural networks or support vector machines) are used to automatically classify and segment SAR images, extract image features to identify and locate interference source regions, and analyze the radiation characteristics of the interference sources. These characteristics include the apparent intensity mean, imaging features, and the trend of radiation characteristics with the radar incident angle as shown in the SAR image.

[0033] Step S1.3 involves storing the frequency characteristics, power levels, radiation characteristics, and other data of the same interference source obtained at different transit times in a time series, establishing time evolution data, forming corresponding interference source files, and realizing the initial construction and continuous updating of the dynamic interference source knowledge base.

[0034] Specifically, step S2, the interference suppression strategy and beam control parameters are pre-planned: Before the satellite performs its observation mission, based on the predicted satellite orbit parameters and antenna beam pointing, and combined with the interference source knowledge base, the suppression strategy and antenna pattern for the entire observation arc are pre-planned, including: Step S2.1: Divide the observation spatiotemporal grid and calculate the relative geometric relationship between the satellite and the interference source; Step S2.2: Pre-determine the interference source area; Step S2.3: Generate the suppression strategy sequence.

[0035] Step S2.1 calculates the relative geometric relationship between the satellite and the interference source, and based on the predicted satellite orbit, discretizes the observation arc segment into... A continuous discrete spatiotemporal point ,in From start time and fixed interval Sure.

[0036] Assume the satellite is at point in time and space. The position vector based on the geocentric coordinate system is: Interference source The position vector based on the geocentric coordinate system is: This information was obtained from the interference source knowledge base. Among them, The X-axis coordinate components of the satellite position vector in the geocentric Earth-fixed coordinate system. The Y-axis coordinate components of the satellite position vector in the geocentric Earth-fixed coordinate system. The Z-axis coordinate components of the satellite position vector in the geocentric Earth-fixed coordinate system. The X-axis coordinate components of the interference source location vector in the geocentric-fixed coordinate system. The Y-axis coordinate components of the interference source location vector in the geocentric-ground-fixed coordinate system. The Z-axis coordinate component of the location vector of the interference source in the geocentric-geostatic coordinate system.

[0037] The relative position vector of the interference source with respect to the satellite is:

[0038] in, For spatiotemporal point index, For the interference source index, As a source of interference The position vector, For satellites at points in time and space The position vector.

[0039] The satellite's coordinate system uses the East-North-Sky direction as its axis, and the relative position vector... Transform to the satellite's body coordinate system and calculate the satellite's position in spacetime. relative interference source slant distance :

[0040]

[0041] in, For spatiotemporal point index, For the interference source index, As a source of interference The relative position component in the direction of the satellite's east. As a source of interference The relative position component in the north direction of the satellite, As a source of interference The relative position component in the satellite's celestial direction. For satellites at points in time and space and interference sources The relative position vector in the satellite's body coordinate system.

[0042] For each spatiotemporal point The relative geometric relationship between the satellite's position vector and the position vector of each interference source in the knowledge base is calculated to obtain the azimuth angle of the interference source relative to the satellite. and pitch angle .

[0043]

[0044]

[0045] in, For spatiotemporal point index, For the interference source index, As a source of interference The relative position component in the direction of the satellite's east. As a source of interference The relative position component in the north direction of the satellite, As a source of interference The relative position component in the satellite's celestial direction. It is the slant distance. It is the arctangent function in the fourth quadrant. For satellites at points in time and space relative interference source slant distance Step S2.2 compares the azimuth and elevation angles of each interference source with the main lobe width and side lobe range of the antenna pattern based on the calculation results of each spatiotemporal point in step S2.1. Based on a preset threshold, it pre-determines whether each interference source is located in the main lobe region or the side lobe region of the antenna pattern at that spatiotemporal point.

[0046] For each spatiotemporal point and interference sources Compare its azimuth and elevation deviations with the corresponding main lobe width of the antenna pattern. Preset threshold. and Used for tolerance adjustment (default can be set to 0).

[0047] The logic for determining the antenna pattern region is as follows: If and If the interference source is located in the main lobe region, then it is marked as the side lobe region; otherwise, it is marked as the side lobe region.

[0048] in, For spatiotemporal point index, For the interference source index, As a source of interference Relative to the satellite at a point in time and space azimuth angle, As a source of interference Relative to the satellite at a point in time and space pitch angle, For antenna beam pointing, and Main lobe width, and Tolerance adjustment threshold, Indicates the azimuth angle of the antenna beam center. This indicates the elevation angle of the antenna beam center.

[0049] In step S2.3, based on the judgment result, a corresponding suppression strategy and parameters are generated for each spatiotemporal point; the control parameters of all spatiotemporal points are stored in time sequence for use by the satellite during on-orbit execution.

[0050] Specifically, step S2.3 is based on the interference source. Location is each spacetime point Generate the corresponding suppression strategy.

[0051] If the interference source is located in the sidelobe region, then antenna pattern optimization suppression is planned. This involves dynamically adjusting the phase and amplitude weights of the phased array antenna using an antenna pattern optimization design method. A phased array wave control code is pre-calculated to create a notch in the antenna pattern along the interference direction, thus reducing gain. This problem can be formulated as an optimization problem, with constraints based on the corresponding spatiotemporal point, the interference source, and a knowledge base of the interference source. The optimal weight vector is then solved. :

[0052] in, For spatiotemporal point index, For the interference source index, Let be the weight vector of the phased array antenna. The interference plus noise covariance matrix is ​​based on spatiotemporal points. and interference sources The geometric relationships and power level calculations in the knowledge base; The constraint matrix is ​​based on the azimuth angle. and pitch angle Build and ensure zero-trap orientation; For spatiotemporal point and interference sources The calculated constraint vector defines the main lobe gain and null depth, ensuring gain in the desired signal direction while creating a null in the interference direction. Let represent a constrained minimization optimization problem, where the weight vector is a vector vector. To optimize the problem variables, the superscript H indicates the conjugate transpose of a matrix or vector.

[0053] If the interference source is located in the main lobe region, matched filtering suppression is planned. The echo signal is convolved based on interference parameters (such as frequency, bandwidth, and modulation characteristics) stored in the knowledge base. Matched filter coefficients for filtering out the interference are pre-designed to suppress the interference components while preserving the target signal. The interference source signal template obtains signal characteristics from the interference source knowledge base to generate the matched filter.

[0054] impulse response of matched filter Designed as an interference signal template Conjugate inversion:

[0055] in, For spatiotemporal point index, For the interference source index, To save time, The normalization coefficient is... As a source of interference At the point of spacetime Signal template, superscript This indicates the complex conjugate operation.

[0056] The output of the matched filter is:

[0057] in, For spatiotemporal point index, For the interference source index, In order to target the source of interference At the point of spacetime The impulse response of the designed matched filter, For satellites at points in time and space Considering interference sources The received echo signal.

[0058] Specifically, in step S3, on-orbit real-time suppression execution and parameter adaptive adjustment, when the satellite is performing an observation mission, based on the real-time satellite position and velocity information, the suppression strategy instructions and key parameters for the corresponding spatiotemporal point, which are pre-calculated and stored in the interference suppression strategy and beam control parameter pre-planning in step S2, are invoked to drive the phased array antenna and signal processor to execute real-time interference suppression and adaptive adjustment strategies.

[0059] If the strategy is indicated as sidelobe suppression, the pre-stored beam control code is loaded into the beam control system of the phased array antenna to form a notch in the direction of interference in the antenna pattern; if the strategy is indicated as main lobe suppression, the pre-stored filter coefficients are loaded into the matched filter of the signal processor to filter the echo signal.

[0060] The adaptive interference suppression process monitors and analyzes new observed echo data in real time after the suppression parameters are applied. If the suppression effect is found to be less than expected or unexpected interference characteristics appear, the adaptive adjustment mechanism is triggered. Based on the preset optimization algorithm, the initial suppression parameters are fine-tuned in real time to adapt to changes in the interference source and environmental conditions.

[0061] Specifically, step S4, quantitative evaluation of suppression effect and iterative learning of strategy, involves collecting echo data after observation, evaluating the suppression effect, and updating the interference source knowledge base to optimize subsequent suppression strategies and achieve continuous learning. This includes: Step S4.1: Imaging and quality assessment; Step S4.2: Closed-loop update of the knowledge base; Step S4.3: Iterative optimization of the strategy.

[0062] Step S4.1 applies a SAR imaging algorithm to the suppressed echo data to generate an image with suppressed interference, and calculates image quality indicators, including local signal-to-interference ratio and integral sidelobe ratio.

[0063] Step S4.2 compares the actual image quality index of the imaging and quality assessment in step S4.1 with the expected suppression effect of the interference suppression strategy and beam control parameters pre-planned in step S2. Combined with the adaptive adjustment record of the on-orbit real-time suppression execution and parameter adaptive adjustment in step S3, the parameters in the dynamic interference source knowledge base are corrected in a closed loop. The parameter correction includes the interference source power level and center frequency, etc.

[0064] Step S4.3 uses reinforcement learning algorithms (such as deep Q-networks) to maximize the signal-to-interference ratio while minimizing the negative impact on imaging quality. It optimizes the interference suppression strategy and pre-planning strategy of waveguide parameters in step S2, and the adaptive adjustment algorithm of on-orbit real-time suppression execution and parameter adaptive adjustment in step S3, thereby improving subsequent suppression efficiency and adaptive capability.

[0065] The present invention also provides a spaceborne cognitive SAR ground radio frequency interference suppression system, which can be implemented by executing the process steps of the spaceborne cognitive SAR ground radio frequency interference suppression method. That is, those skilled in the art can understand the spaceborne cognitive SAR ground radio frequency interference suppression method as a preferred embodiment of the spaceborne cognitive SAR ground radio frequency interference suppression system.

[0066] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0067] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for suppressing ground radio frequency interference in spaceborne cognitive SAR, characterized in that, include: Step S1: Interference feature extraction and dynamic knowledge base construction; Step S2: Interference suppression strategy and pre-planning of beam control parameters; Step S3: On-orbit real-time suppression execution and parameter adaptive adjustment; Step S4: Quantitative evaluation of the inhibition effect and iterative learning of the strategy.

2. The method according to claim 1, characterized in that, Step S1 specifically includes: The system initializes and continuously updates a dynamic interference source knowledge base. The initialization data sources for the knowledge base include archived data acquired before system initialization and prior interference information acquired from other sources. If no initialization data is available, this step is performed after the first observation mission acquires echo data to complete the initial construction. During system operation, the system continuously processes data accumulated from previous observation missions to update the knowledge base. The dynamic knowledge base contains the geographic coordinates, frequency characteristics, power levels, radiation characteristics, and temporal evolution data of the interference sources.

3. The method according to claim 2, characterized in that, The steps S1, interference feature extraction and dynamic knowledge base construction, include: Step S1.1: Frequency domain identification of interference modes: Using the power spectral density estimation method, perform spectrum analysis on the data used to build the knowledge base, extract the frequency characteristics and power level of the interference signal, and determine the interference modulation characteristics to identify the interference mode; Step S1.2: Image domain acquisition of interference source location and radiation characteristics: Automatic classification and segmentation of SAR images are performed using machine learning algorithms to extract image features to identify and locate interference source areas, and the radiation characteristics of interference sources in the interference areas are analyzed. The radiation characteristics of interference sources include the average apparent intensity, imaging features, and the trend of their radiation characteristics with the radar incident angle as shown on the SAR image. Step S1.3: Establish time evolution data and update the dynamic database: Store the frequency characteristics, power levels, and radiation characteristics data of the same interference source obtained at different transit times in a time series, establish time evolution data, form corresponding interference source files, and enable the initial construction and continuous updating of the dynamic interference source knowledge base.

4. The method according to claim 1, characterized in that, Step S2 specifically includes: Before the satellite performs its observation mission, based on the predicted satellite orbit parameters and antenna beam pointing, and combined with the interference source knowledge base, the suppression strategy and antenna pattern for the entire observation arc are planned in advance.

5. The method according to claim 4, characterized in that, Step S2 includes: Step S2.1: Divide the observation spatiotemporal grid and calculate the relative geometric relationship between the satellite and the interference source: Based on the predicted satellite orbit, the observation arc is discretized into a series of continuous spatiotemporal points; for each spatiotemporal point, the relative geometric relationship between the satellite and each interference source in the knowledge base is calculated to obtain the azimuth and elevation angles of the interference source relative to the satellite. Step S2.2: Pre-determine the interference source region: Based on the calculation results of each spatiotemporal point in step S2.1, compare the azimuth and elevation angles of each interference source with the main lobe width and side lobe range of the antenna pattern. Based on a preset threshold, pre-determine whether each interference source is located in the main lobe region or the side lobe region of the antenna pattern at that spatiotemporal point. Step S2.3: Generate suppression strategy sequence: Based on the judgment result, generate corresponding suppression strategies and parameters for each spatiotemporal point; store the control parameters of all spatiotemporal points in time sequence for use by the satellite during on-orbit execution.

6. The method according to claim 5, characterized in that, In step S2.3, a corresponding suppression strategy and parameters are generated for each spatiotemporal point, specifically including: If the interference source is located in the sidelobe region, the plan is to use antenna pattern optimization to suppress it. The phase and amplitude weights of the phased array antenna are dynamically adjusted using the antenna pattern optimization design method. The phased array wave control code that makes the antenna pattern form a notch in the direction of interference is pre-calculated and generated, so that the antenna pattern forms a notch in the direction of interference to reduce the gain. If the interference source is located in the main lobe region, matched filtering suppression is planned. The echo signal is convolved based on the interference parameters in the knowledge base. Matched filter coefficients for filtering out the interference are pre-designed to suppress the interference components while retaining the target signal.

7. The method according to claim 1, characterized in that, Step S3 specifically includes: When the satellite is performing an observation mission, based on the real-time satellite position and velocity information, the suppression strategy instructions and key parameters for the corresponding spatiotemporal point, which are pre-calculated and stored in the interference suppression strategy and beam control parameter pre-planning in step S2, are invoked to drive the phased array antenna and signal processor to perform real-time interference suppression and adaptively adjust the strategy. If the real-time interference suppression strategy indicates sidelobe suppression, then the pre-stored beam control code is loaded into the beam control system of the phased array antenna, so that the antenna pattern forms a notch in the direction of interference. If the real-time interference suppression strategy indicates main lobe suppression, then the pre-stored filter coefficients are loaded into the matched filter of the signal processor to filter the echo signal. The adaptive adjustment strategy for real-time interference suppression includes: real-time monitoring and analysis of new observed echo data after loading suppression parameters; if the suppression effect is not as expected or unexpected interference characteristics are detected, the adaptive adjustment mechanism is triggered to fine-tune the initial suppression parameters in real time based on a preset optimization algorithm to adapt to changes in interference sources and environmental conditions.

8. The method according to claim 1, characterized in that, Step S4 specifically includes: After observation, echo data is collected, the suppression effect is evaluated, and the interference source knowledge base is updated to optimize subsequent suppression strategies and achieve continuous learning.

9. The method according to claim 10, characterized in that, Step S4 includes: Step S4.1: Imaging and Quality Assessment: Apply SAR imaging algorithms to the suppressed echo data to generate an image with suppressed interference, and calculate image quality indicators, including local signal-to-interference ratio and integral sidelobe ratio. Step S4.2: Knowledge base closed-loop update: Compare the actual image quality indicators of imaging and quality assessment in step S4.1 with the expected suppression effect of interference suppression strategy and beam control parameters pre-planned in step S2. Combine the adaptive adjustment record of on-orbit real-time suppression execution and parameter adaptive adjustment in step S3 to perform closed-loop correction of the parameters in the dynamic interference source knowledge base. The parameter correction includes interference source power level and center frequency. Step S4.3: Strategy Iteration Optimization: Using reinforcement learning algorithms to maximize the signal-to-interference ratio while minimizing the negative impact on imaging quality, optimize the interference suppression strategy and pre-planning strategy of waveguide parameters in step S2, and the adaptive adjustment algorithm of on-orbit real-time suppression execution and parameter adaptive adjustment in step S3, so as to improve the subsequent suppression efficiency and adaptive capability.

10. A spaceborne cognitive SAR ground radio frequency interference suppression system, characterized in that, include: Module M1: Interference Feature Extraction and Dynamic Knowledge Base Construction; Module M2: Interference suppression strategy and pre-planning of beam control parameters; Module M3: On-orbit real-time suppression execution and parameter adaptive adjustment; Module M4: Quantitative evaluation of suppression effect and iterative learning of strategy.

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