Satellite-borne cognitive SAR azimuth blurring suppression method and system

Abstract

This invention provides a method and system for suppressing azimuth ambiguity in spaceborne cognitive SAR. The core process is as follows: Database Construction: Integrating historical multi-angle observation data to establish a globally geocoded backscattering coefficient database. Risk Prediction: Combining satellite orbit, velocity, and PRF (Pulse Repetition Frequency), the database is used to predict the azimuth ambiguity signal ratio in the observation area. Identification and Early Warning: Comparing with preset thresholds, potential areas with excessive azimuth ambiguity are accurately identified. Joint Optimization: For risk areas, the azimuth digital beamforming (DBF) weights and PRF parameters are optimized, creating antenna gain notches or adjusting frequencies in the direction of strong ambiguity sources to avoid aliasing. Adaptive Execution: The optimized antenna pattern and PRF sequence are applied for real-time transmission and reception to suppress ambiguity echoes. Closed-Loop Update: The knowledge base is continuously updated to achieve iterative performance upgrades.

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 azimuth ambiguity in spaceborne cognitive SAR. Background Technology

[0002] Spaceborne synthetic aperture radar (SAR), as a high-resolution Earth observation technology, possesses all-weather, all-day imaging capabilities and has significant application value in surveying, monitoring, and disaster management. However, traditional spaceborne SAR systems have significant limitations in azimuth ambiguity suppression: due to the lack of prior knowledge of the backscattering characteristics of the observation scene, it is difficult to accurately predict azimuth ambiguity signals; simultaneously, the fixed antenna pattern and pulse repetition frequency (PRF) parameters lack adaptive adjustment capabilities, failing to effectively suppress strong azimuth ambiguity sources, resulting in a significant deterioration in image quality under high-contrast scenes. To address these issues, this invention introduces a closed-loop optimization mechanism from cognitive radar to construct an azimuth ambiguity suppression method based on a priori scattering characteristic database. This method achieves intelligent prediction, joint optimization, and real-time suppression of azimuth ambiguity, significantly improving imaging quality and the system's adaptability in complex observation scenarios.

[0003] The patent "SAR Image Azimuth Blur Suppression Device and Method Based on Time-Frequency Domain Joint Search and Suppression" (CN118425966A) proposes to reduce the impact of azimuth blur on imaging by using spectrum truncation, joint time-frequency domain search for residual azimuth blur energy, time-frequency domain suppression, and spectral extrapolation to recover the image. This method utilizes the distribution characteristics of blur energy in the time-frequency domain for fine suppression, avoiding the resolution loss of traditional frequency domain filtering. However, as a post-processing technique, its parameters, such as truncation size and suppression threshold, need to be preset and cannot be adaptively adjusted according to real-time observation conditions. Furthermore, this method does not involve antenna pattern optimization or dynamic PRF scheduling, leading to unstable blur suppression effects in complex geographical scenarios.

[0004] The patent "Azimuth Ambiguity Suppression Method for Spaceborne SAR with Non-Uniform Sampling" (CN118731864A) proposes to suppress azimuth ambiguity by compensating for the phase difference between the transmitting and receiving stations, calculating the equivalent azimuth sampling positions, sorting the sampling points, and reconstructing the echo using the Adaptive Weight-Conjugate Gradient-Toppertitz Matrix (ACT) algorithm. This method is applicable to situations where there is relative motion between channels in a distributed SAR platform, but it relies on fixed algorithm parameters and offline calculations, does not involve azimuth antenna pattern optimization, and lacks adaptability and robustness in complex geographical environments.

[0005] The patent "A Method for Azimuth Blur Detection and Suppression in Spaceborne SAR Scene Matching Curve Imaging" (CN119335533A) proposes to achieve blur refocusing through azimuth blur modeling, range migration correction, and azimuth phase compensation. It combines image entropy difference and CFAR detection to identify blurry regions and utilizes Wiener filtering and sub-block replacement to suppress low-resolution loss. This method balances blur suppression and resolution preservation using entropy difference detection and local replacement; however, it is based on fixed thresholds and filtering parameters and lacks a real-time feedback mechanism. Furthermore, it cannot form an adaptive antenna pattern notch in the direction of arrival, making it difficult to adapt to complex and changing observation environments, and its suppression effect is limited in strongly blurred scenes.

[0006] The patent "A Method for Azimuth Blur Suppression of Strip-Mode Spaceborne SAR Images" (CN120539726A) is based on range-Doppler domain migration line detection and refocusing transformation. It identifies blur signals through Hough transform and performs inverse operations to suppress them, thus achieving blur suppression in strip-mode SAR images. This method uses fixed-threshold binarization and predefined convolution kernel matching, but does not incorporate the real-time evaluation and closed-loop optimization mechanisms found in cognitive SAR. Compared to document 1, its parameters (such as the range migration model and interpolation factor) are preset before imaging and cannot be dynamically adjusted according to the scene's backscattering characteristics, potentially leading to blur residues in highly heterogeneous regions. Furthermore, this method does not utilize antenna pattern notch technology to actively suppress blur sources but passively extracts already generated blur signals, limiting its robustness in environments with strong scattering variations.

[0007] The paper "Cross Ambiguity Function Shaping of Cognitive MIMO Radar: ASynergistic Approach to Antenna Placement and Waveform Design" (Z. Xie et al., IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-13, 2024, Art no. 5107313) proposes to shape the ambiguity function of MIMO radar by jointly optimizing antenna position, waveform, and filters to improve target indication capability, using the main lobe integral sidelobe ratio as a metric. However, this method is designed for MIMO radar architecture and is not specifically designed for SAR, and focuses on antenna placement and waveform diversity rather than SAR-specific azimuth ambiguity suppression. Traditional MIMO radar parameters are fixed and difficult to adapt to the complex geometric environment and dynamic clutter of SAR. Although this paper introduces cognitive optimization, it does not utilize antenna pattern notch technology to directly suppress azimuth ambiguity. Therefore, it lacks adaptability in the azimuth ambiguity suppression scenario of spaceborne SAR, especially lacking integration of SAR orbital parameters and backscatter databases.

[0008] The paper "An Advanced Azimuth Ambiguity Suppression Scheme for Azimuth Multichannel SAR System" (J. Li et.al, IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1-5, 2024, Art no. 4009005) proposes an azimuth ambiguity suppression scheme based on multichannel cancellation (MCC) and ambiguity refocusing. First, it locates the ambiguity region by comparing multiple view images. Then, it extracts the ambiguity components using linear channel combination. Finally, it suppresses ambiguity through refocusing and subtraction. However, this method does not employ cognitive SAR principles; parameters such as the pulse repetition frequency (PRF) are fixed and cannot adaptively adjust to environmental changes, potentially leading to poor performance in complex scenarios. Furthermore, it does not utilize antenna pattern notch technology but relies on post-processing algorithms for ambiguity extraction and suppression. Designed specifically for multichannel SAR systems, it is not suitable for widespread radar applications and lacks responsiveness to real-time environmental changes.

[0009] In summary, existing methods for suppressing azimuth ambiguity in spaceborne SAR rely on static parameter settings or post-processing algorithms. These methods lack adaptive adjustment capabilities, making it difficult to cope with dynamic changes in high-contrast or complex geographical environments, resulting in insufficient azimuth ambiguity suppression. Therefore, there is an urgent need to develop integrated prior databases to achieve joint optimization of antenna pattern and PRF for spaceborne cognitive SAR azimuth ambiguity suppression, thereby improving the environmental adaptability and dynamic azimuth ambiguity suppression accuracy of SAR imaging systems. Summary of the Invention

[0010] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method and system for suppressing azimuth ambiguity in spaceborne cognitive SAR.

[0011] According to one aspect of the present invention, a method for suppressing azimuth ambiguity in spaceborne cognitive SAR includes:

[0012] Step S1: This includes processing historical spaceborne SAR multi-angle observation data, extracting radar backscattering coefficient features under different geographic coordinates, and constructing a backscattering coefficient database based on global geocoding. Step S2: For the upcoming observation task, based on the satellite orbital parameters, velocity vector, and set pulse repetition frequency, and in conjunction with the backscattering coefficient database, estimate the azimuth ambiguity signal ratio at each time point within the observation area. Step S3: Compare the estimated azimuth blur ratio with a preset threshold to identify potential azimuth blur regions where the azimuth blur ratio exceeds the standard; Step S4: For the identified potential azimuth ambiguity areas, jointly optimize the azimuth digital beamforming weights and pulse repetition frequency parameters, generate an adaptive azimuth antenna pattern, and form a notch in the direction of arrival of the source of strong azimuth ambiguity, and / or adjust the PRF to avoid spectral aliasing. Step S5: When performing the observation task, the adaptive azimuth antenna pattern and optimized PRF sequence generated by the joint optimization of the azimuth antenna pattern and PRF in step S4 are used for signal transmission and reception to suppress azimuth ambiguity echoes and improve imaging quality. Step S6: Knowledge base update and closed-loop optimization.

[0013] Preferably, step S2 specifically includes: Step S2.1: Calculate the Doppler center frequency and azimuth spectrum of the satellite at the current azimuth time; Step S2.2: Based on the azimuth spectrum folding effect, calculate the Doppler frequencies corresponding to each azimuth ambiguity component; Step S2.3: Using the three-dimensional position vectors corresponding to the main target and the azimuth ambiguity components, extract the corresponding radar backscattering coefficients from the database; Step S2.4: Calculate the radar antenna gain corresponding to the main target and the ambiguity components in each direction; Step S2.5: Based on the output of steps S2.1-S2.4, calculate the azimuth ambiguity signal ratio for the current azimuth time.

[0014] Preferably, in sub-step S2.5, the fuzzy signal ratio is calculated as follows:

[0015] in, For location and time, Index of azimuth ambiguity components ( ), For Doppler frequency, For azimuth Doppler bandwidth, The pulse repetition frequency, For echo amplitude gain, For location vectors in the database The corresponding backscattering coefficient, For location and time No. The position vector of each fuzzy component For location and time The position vector of the main target, Indicates the first The azimuth ambiguity of the source wave direction angle Indicates the azimuth angle of the main beam. Indicates the first The slant distance corresponding to each azimuth ambiguity source.

[0016] Preferably, step S3 includes: Step S3.1: Based on the azimuth ambiguity signal ratio calculated in step S2, generate AASR distribution maps for each azimuth time within the observation area; Step S3.2: Compare each value in the AASR distribution chart with a preset threshold; Step S3.3: Mark all azimuth time regions where the AASR value exceeds the threshold as potential azimuth ambiguity regions, and record the azimuth time and the corresponding ambiguity signal intensity of these regions.

[0017] Preferably, step S4 includes: Step S4.1: Calculate the azimuth direction of arrival (ADI) of the source of strong azimuth ambiguity; Step S4.2: Construct a joint optimization problem of azimuth antenna pattern and PRF; Step S4.3: Solve the optimization problem to obtain the optimal azimuth beamforming weights and / or the optimal PRF sequence; Step S4.4: Generate adaptive azimuth antenna pattern and PRF scheduling instructions.

[0018] Preferably, step S4.1 specifically includes: Based on the identification results of potential azimuth ambiguity regions corresponding to the identification of strong azimuth ambiguity regions in step S3, the direction of arrival (DOA) of the source of strong azimuth ambiguity is calculated. The DOA DOA is determined by the relative geometric relationship between the satellite velocity vector and the ambiguity region.

[0019] Preferably, the joint optimization problem constructed in step S4.2 has an objective function of minimizing the global azimuth blur ratio of the entire observation area while satisfying the main lobe gain constraint. Take all azimuth times under specific beamforming weights and pulse repetition frequencies. On The maximum or average value; decision variables include the azimuth beamforming weight vector. and pulse repetition frequency .

[0020] Preferably, step S4 specifically includes: the expression for the joint optimization problem is as follows:

[0021] in, Weighting for beamforming and pulse repetition frequency The optimization objective function represents minimizing the global azimuth ambiguity signal ratio. To form a weight vector for the azimuth beam, This represents the conjugate transpose of the azimuth beamforming weight vector. The pulse repetition frequency, Azimuth The corresponding azimuth array response vector, The main beam pointing to the azimuth angle. This is the minimum constraint on the main lobe gain. For the first The azimuth ambiguity of the source wave direction angle To constrain the notch depth, For the index of the source of orientation ambiguity, This represents the total number of sources with ambiguity. This is the minimum allowable value for the pulse repetition frequency. This is the maximum permissible value for the pulse repetition frequency. To use pulse repetition frequency The global distance blur ratio of the entire observation scene at that time. This is the distance ambiguity ratio threshold, used to constrain distance ambiguity performance.

[0022] Preferably, step S6 includes: Step S6.1: Based on the echo data and imaging results obtained from this observation mission, evaluate the actual suppression effect of azimuth ambiguity; Step S6.2: Correct the backscattering coefficient database based on the evaluation results; Step S6.3: Use the updated database and optimized model for the pre-evaluation of the next observation task to achieve closed-loop learning and continuous optimization of the system.

[0023] According to another aspect of the present invention, a spaceborne cognitive SAR azimuth ambiguity suppression system is characterized by comprising: Module M1: Includes processing historical spaceborne SAR multi-angle observation data, extracting radar backscattering coefficient features under different geographic coordinates, and constructing a backscattering coefficient database based on global geocoding; Module M2: For the upcoming observation mission, based on satellite orbit parameters, velocity vector, and the set pulse repetition frequency, and in conjunction with the backscattering coefficient database, it estimates the azimuth ambiguity signal ratio at each time point within the observation area. Module M3: Compares the estimated azimuth blur ratio with a preset threshold to identify potential azimuth blur regions where the azimuth blur ratio exceeds the standard; Module M4: For the identified potential azimuth ambiguity areas, jointly optimize the azimuth digital beamforming weights and pulse repetition frequency parameters, generate an adaptive azimuth antenna pattern, and form a notch in the direction of arrival of the source of strong azimuth ambiguity, and / or adjust the PRF to avoid spectral aliasing. Module M5: When performing the observation task, the adaptive azimuth antenna pattern and optimized PRF sequence jointly generated by the azimuth antenna pattern of module M4 and PRF are used for signal transmission and reception to suppress azimuth ambiguity echoes and improve imaging quality. Module M6: Knowledge base update and closed-loop optimization.

[0024] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention constructs a prior knowledge base by processing historical spaceborne SAR data and performs azimuth ambiguity pre-evaluation by combining satellite orbital parameters. This solves the problem of traditional methods lacking prior knowledge of scene backscattering and significantly improves the effectiveness and scene adaptability of SAR azimuth ambiguity suppression.

[0025] 2. This invention adopts a joint optimization strategy of azimuth antenna pattern and PRF. By constructing an optimization problem to solve the optimal beam weight and PRF sequence, a notch is formed in the direction of the ambiguity source or spectral aliasing is avoided. This solves the problem that fixed parameters cannot be adaptively suppressed and dynamically responds to changes in the SAR imaging environment.

[0026] 3. This invention introduces a knowledge base update and closed-loop optimization mechanism, evaluates the azimuth ambiguity suppression effect based on echo data and dynamically corrects the database, solving the problem of lack of real-time feedback in azimuth ambiguity suppression of spaceborne SAR. By iteratively optimizing and updating the knowledge base, a cognitive SAR loop is formed, improving the long-term adaptability of the SAR system. Attached Figure Description

[0027] 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 the method for suppressing azimuth ambiguity in spaceborne cognitive SAR.

[0028] Figure 2 This is a structural diagram of a spaceborne cognitive SAR azimuth ambiguity suppression system. Detailed Implementation

[0029] 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.

[0030] A method for suppressing azimuth ambiguity in spaceborne cognitive SAR provided by the present invention includes: Step S1: Construction of the prior knowledge base; Step S2: Pre-assessment of azimuth ambiguity; Step S3: Identification of regions with strong azimuth ambiguity; Step S4: Joint optimization of azimuth antenna pattern and PRF; Step S5: Adaptive observation execution; Step S6: Knowledge base update and closed-loop optimization.

[0031] Specifically, step S1, the construction of the prior knowledge base, includes processing historical spaceborne SAR multi-angle observation data, extracting radar backscattering coefficient features under different geographic coordinates, and constructing a backscattering coefficient database based on global geocoding. Can be defined as all locations The corresponding set of backscattering coefficients:

[0032] in, It is a three-dimensional position vector. The extent of the observation area, For position The backscattering coefficient at that location.

[0033] Specifically, step S2, the azimuth ambiguity pre-evaluation, is for the upcoming observation task. Based on satellite orbital parameters, velocity vectors, and a set pulse repetition frequency, and in conjunction with the backscattering coefficient database, it estimates the azimuth ambiguity signal ratio at each time point within the observation area, including: Step S2.1: Calculate the Doppler center frequency and azimuth spectrum of the satellite at the current azimuth time; Step S2.2: Based on the azimuth spectrum folding effect, calculate the Doppler frequency corresponding to each azimuth ambiguity component.

[0034] Step S2.3: Using the three-dimensional position vectors corresponding to the main target and the azimuth ambiguity components, extract the corresponding radar backscattering coefficients from the database; Step S2.4: Calculate the radar antenna gain corresponding to the main target and the ambiguity components in each direction; Step S2.5: Calculate the azimuth ambiguity ratio of the current azimuth time.

[0035] Step S2.5 calculates the current azimuth time. The specific formula for calculating the azimuth ambiguity signal ratio is as follows:

[0036] in, For location and time, Index of azimuth ambiguity components ( ), For Doppler frequency, For azimuth Doppler bandwidth, The pulse repetition frequency, For echo amplitude gain, For location vectors in the database The corresponding backscattering coefficient, For location and time No. The position vector of each fuzzy component For location and time The position vector of the main target, Indicates the first The azimuth ambiguity of the source wave direction angle Indicates the azimuth angle of the main beam. Indicates the first The slant distance corresponding to each azimuth ambiguity source.

[0037] Specifically, step S3, strong azimuth blur region identification, compares the estimated azimuth blur signal ratio with a preset threshold to identify potential azimuth blur regions where the azimuth blur signal ratio exceeds the threshold, including: Step S3.1: Based on the azimuth ambiguity signal ratio calculated in step S2, generate AASR distribution maps for each azimuth time within the observation area; Step S3.2: Compare each value in the AASR distribution chart with a preset threshold; Step S3.3: Mark all azimuth time regions where the AASR value exceeds the threshold as potential azimuth ambiguity regions, and record the azimuth time and the corresponding ambiguity signal intensity of these regions.

[0038] Specifically, step S3.3 involves the location-time set of the potential location-ambiguous region. It can be defined as satisfying:

[0039] in, For location and time, Location and time The azimuth ambiguity signal ratio, Preset Threshold.

[0040] Specifically, step S4, the joint optimization of the azimuth antenna pattern and PRF, targets the identified potential azimuth ambiguity regions. It jointly optimizes the azimuth digital beamforming weights and pulse repetition frequency parameters to generate an adaptive azimuth antenna pattern and forms a notch in the direction of arrival of strong azimuth ambiguity sources. It also adjusts the PRF to avoid spectral aliasing, including: Step S4.1: Calculate the azimuth direction of arrival (ADI) of the source of strong azimuth ambiguity; Step S4.2: Construct a joint optimization problem of azimuth antenna pattern and PRF; Step S4.3: Solve the optimization problem to obtain the optimal azimuth beamforming weights and / or the optimal PRF sequence; Step S4.4: Generate adaptive azimuth antenna pattern and PRF scheduling instructions.

[0041] Step S4.1, based on the identification results of the potential azimuth ambiguity region corresponding to the strong azimuth ambiguity region identification in step S3, calculates the direction of arrival (DOA) of the source of the strong azimuth ambiguity. The DOA DOA is determined by the relative geometric relationship between the satellite velocity vector and the ambiguity region. The method for calculating the direction of arrival (DOA) of a source with ambiguity in one azimuth is as follows:

[0042] in, For the satellite velocity vector, For the index of the source of orientation ambiguity, This represents the total number of sources with ambiguity. For the first The position vector of each fuzzy component This is the satellite position vector.

[0043] The joint optimization problem constructed in step S4.2 has an objective function of minimizing the global azimuth blur ratio of the entire observation area while satisfying the main lobe gain constraint. Take all azimuth times under specific beamforming weights and pulse repetition frequencies. On The maximum or average value. Decision variables include the azimuth beamforming weight vector. and pulse repetition frequency .

[0044] The optimization problem is described as follows:

[0045] in, Weighting for beamforming and pulse repetition frequency The optimization objective function represents minimizing the global azimuth ambiguity signal ratio. To form a weight vector for the azimuth beam, This represents the conjugate transpose of the azimuth beamforming weight vector. The pulse repetition frequency, Azimuth The corresponding azimuth array response vector, The main beam pointing to the azimuth angle. This is the minimum constraint on the main lobe gain. For the first The azimuth ambiguity of the source wave direction angle To constrain the notch depth, For the index of the source of orientation ambiguity, This represents the total number of sources with ambiguity. This is the minimum allowable value for the pulse repetition frequency. This is the maximum permissible value for the pulse repetition frequency. To use pulse repetition frequency The global distance blur ratio of the entire observation scene at that time. This is the distance ambiguity ratio threshold, used to constrain distance ambiguity performance.

[0046] Specifically, in step S5, adaptive observation execution, during the execution of the observation task, applies the adaptive azimuth antenna pattern and optimized PRF sequence jointly optimized in step S4 to transmit and receive signals, thereby suppressing azimuth ambiguity echoes and improving imaging quality.

[0047] in, For azimuth beamforming, a weight vector is formed, and the weights are... Through joint optimization, the antenna pattern was improved in the main beam direction. Maintaining high gain and strong azimuth ambiguity source direction A notch is formed on the top to suppress orientation ambiguity. It is the azimuth angle. Azimuth The corresponding azimuth array response vector, This is the original transmitted signal.

[0048] Specifically, step S6, knowledge base update and closed-loop optimization, includes: Step S6.1: Based on the echo data and imaging results obtained from this observation mission, evaluate the actual suppression effect of azimuth ambiguity; Step S6.2: Correct the backscattering coefficient database based on the evaluation results; Step S6.3: Use the updated database and optimized model for the pre-evaluation of the next observation task to achieve closed-loop learning and continuous optimization of the system.

[0049] Step S6.2 corrects the backscattering coefficient database based on the evaluation results. The recursive update formula is:

[0050] in, Learning rate ( ), control the weight of historical data, The historical backscattering coefficient, This is the measured value of the backscattering coefficient obtained in this observation.

[0051] The present invention also provides a spaceborne SAR azimuth ambiguity suppression system, which can be implemented by executing the process steps of the spaceborne SAR azimuth ambiguity suppression method. That is, those skilled in the art can understand the spaceborne SAR azimuth ambiguity suppression method as a preferred embodiment of the spaceborne SAR azimuth ambiguity suppression system.

[0052] 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.

[0053] 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 azimuth ambiguity in spaceborne cognitive SAR, characterized in that, include: Step S1: This includes processing historical spaceborne SAR multi-angle observation data, extracting radar backscattering coefficient features under different geographic coordinates, and constructing a backscattering coefficient database based on global geocoding. Step S2: For the upcoming observation task, based on the satellite orbital parameters, velocity vector, and set pulse repetition frequency, and in conjunction with the backscattering coefficient database, estimate the azimuth ambiguity signal ratio at each time point within the observation area. Step S3: Compare the estimated azimuth blur ratio with a preset threshold to identify potential azimuth blur regions where the azimuth blur ratio exceeds the standard; Step S4: For the identified potential azimuth ambiguity areas, jointly optimize the azimuth digital beamforming weights and pulse repetition frequency parameters, generate an adaptive azimuth antenna pattern, and form a notch in the direction of arrival of the source of strong azimuth ambiguity, and / or adjust the PRF to avoid spectral aliasing. Step S5: When performing the observation task, the adaptive azimuth antenna pattern and optimized PRF sequence generated by the joint optimization of the azimuth antenna pattern and PRF in step S4 are used for signal transmission and reception to suppress azimuth ambiguity echoes and improve imaging quality. Step S6: Knowledge base update and closed-loop optimization.

2. The method according to claim 1, characterized in that, Step S2 specifically includes: Step S2.1: Calculate the Doppler center frequency and azimuth spectrum of the satellite at the current azimuth time; Step S2.2: Based on the azimuth spectrum folding effect, calculate the Doppler frequencies corresponding to each azimuth ambiguity component; Step S2.3: Using the three-dimensional position vectors corresponding to the main target and the azimuth ambiguity components, extract the corresponding radar backscattering coefficients from the database; Step S2.4: Calculate the radar antenna gain corresponding to the main target and the ambiguity components in each direction; Step S2.5: Based on the output of steps S2.1-S2.4, calculate the azimuth ambiguity signal ratio for the current azimuth time.

3. The method according to claim 2, characterized in that, In sub-step S2.5, the fuzzy signal ratio is calculated as follows: in, For location and time, Index of azimuth ambiguity components , For Doppler frequency, For azimuth Doppler bandwidth, The pulse repetition frequency, For echo amplitude gain, For location vectors in the database The corresponding backscattering coefficient, For location and time No. The position vector of each fuzzy component For location and time The position vector of the main target, Indicates the first The azimuth ambiguity of the source wave direction angle Indicates the azimuth angle of the main beam. Indicates the first The slant distance corresponding to each azimuth ambiguity source.

4. The method according to claim 1, characterized in that, Step S3 includes: Step S3.1: Based on the azimuth ambiguity signal ratio calculated in step S2, generate AASR distribution maps for each azimuth time within the observation area; Step S3.2: Compare each value in the AASR distribution chart with a preset threshold; Step S3.3: Mark all azimuth time regions where the AASR value exceeds the threshold as potential azimuth ambiguity regions, and record the azimuth time and the corresponding ambiguity signal intensity of these regions.

5. The method according to claim 1, characterized in that, Step S4 includes: Step S4.1: Calculate the azimuth direction of arrival (ADI) of the source of strong azimuth ambiguity; Step S4.2: Construct a joint optimization problem of azimuth antenna pattern and PRF; Step S4.3: Solve the optimization problem to obtain the optimal azimuth beamforming weights and / or the optimal PRF sequence; Step S4.4: Generate adaptive azimuth antenna pattern and PRF scheduling instructions.

6. The method according to claim 5, characterized in that, Step S4.1 specifically includes: Based on the identification results of potential azimuth ambiguity regions corresponding to the identification of strong azimuth ambiguity regions in step S3, the direction of arrival (DOA) of the source of strong azimuth ambiguity is calculated. The DOA DOA is determined by the relative geometric relationship between the satellite velocity vector and the ambiguity region.

7. The method according to claim 5, characterized in that, The joint optimization problem constructed in step S4.2 has an objective function of minimizing the global azimuth blur ratio of the entire observation area while satisfying the main lobe gain constraint. Take all azimuth times under specific beamforming weights and pulse repetition frequencies. On The maximum or average value; decision variables include the azimuth beamforming weight vector. and pulse repetition frequency .

8. The method according to claim 1, characterized in that, Step S4 specifically includes the following: The expression for the joint optimization problem is as follows: in, Weighting for beamforming and pulse repetition frequency The optimization objective function represents minimizing the global azimuth ambiguity signal ratio. To form a weight vector for the azimuth beam, This represents the conjugate transpose of the azimuth beamforming weight vector. The pulse repetition frequency, Azimuth The corresponding azimuth array response vector, The main beam pointing to the azimuth angle. This is the minimum constraint on the main lobe gain. For the first The azimuth ambiguity of the source wave direction angle To constrain the notch depth, This serves as an index for the source of the ambiguity. This represents the total number of sources with ambiguity. This is the minimum allowable value for the pulse repetition frequency. This is the maximum permissible value for the pulse repetition frequency. To use pulse repetition frequency The global distance blur ratio of the entire observation scene at that time. This is the distance ambiguity ratio threshold, used to constrain distance ambiguity performance.

9. The method according to claim 1, characterized in that, Step S6 includes: Step S6.1: Based on the echo data and imaging results obtained from this observation mission, evaluate the actual suppression effect of azimuth ambiguity; Step S6.2: Correct the backscattering coefficient database based on the evaluation results; Step S6.3: Use the updated database and optimized model for the pre-evaluation of the next observation task to achieve closed-loop learning and continuous optimization of the system.

10. A spaceborne cognitive SAR azimuth ambiguity suppression system, characterized in that, include: Module M1: Includes processing historical spaceborne SAR multi-angle observation data, extracting radar backscattering coefficient features under different geographic coordinates, and constructing a backscattering coefficient database based on global geocoding; Module M2: For the upcoming observation mission, based on satellite orbit parameters, velocity vector, and the set pulse repetition frequency, and in conjunction with the backscattering coefficient database, it estimates the azimuth ambiguity signal ratio at each time point within the observation area. Module M3: Compares the estimated azimuth blur ratio with a preset threshold to identify potential azimuth blur regions where the azimuth blur ratio exceeds the standard; Module M4: For the identified potential azimuth ambiguity areas, jointly optimize the azimuth digital beamforming weights and pulse repetition frequency parameters, generate an adaptive azimuth antenna pattern, and form a notch in the direction of arrival of the source of strong azimuth ambiguity, and / or adjust the PRF to avoid spectral aliasing. Module M5: When performing the observation task, the adaptive azimuth antenna pattern and optimized PRF sequence jointly generated by the azimuth antenna pattern of module M4 and PRF are used for signal transmission and reception to suppress azimuth ambiguity echoes and improve imaging quality. Module M6: Knowledge base update and closed-loop optimization.

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