Modular miniature high-performance airborne synthetic aperture radar system

Abstract

The invention relates to the technical field of airborne synthetic aperture radar systems, and discloses a modular miniature high-performance airborne synthetic aperture radar system. According to the system, under the assistance of Beidou navigation, an original echo signal is acquired and preprocessed through a radar data acquisition unit; the navigation data interface unit receives Beidou positioning information and time sequence signals and converts the Beidou positioning information and the time sequence signals into available reference of radar; the signal processing unit generates a preliminary radar image; the fusion evaluation unit aligns the image with a coordinate reference, calculates a reference scattering characteristic through a radar model, and compares the reference scattering characteristic with a measured value to obtain a deviation index; and the control execution unit dynamically adjusts the radar transmitting power, the scanning mode and the resource allocation according to the index. According to the system, real-time perception of imaging quality and automatic optimization of front-end parameters are realized, the problem of uneven imaging quality caused by fixed parameters of a traditional radar system is solved, and the adaptive imaging capability in a complex scene is improved.

Description

Technical Field

[0001] This invention relates to the field of airborne synthetic aperture radar system technology, specifically a modular, miniature, high-performance airborne synthetic aperture radar system. Background Technology

[0002] Existing airborne synthetic aperture radar (SAR) systems typically employ relatively fixed operating modes for data acquisition. Radar parameters such as transmit power and beam scanning mode are mostly preset before the mission, lacking the ability to dynamically adjust them based on the actual imaging scenario and results during flight. The data processing flow exhibits a sequential, open-loop characteristic, meaning that signal acquisition, imaging processing, and image output are relatively independent stages. This approach becomes rigid when facing complex terrain, variable targets, or unexpected missions, making it difficult to guarantee optimal imaging quality.

[0003] Existing technical solutions have shortcomings. Fixed-parameter modes cannot adapt to differences in scattering characteristics within the scanning area, potentially leading to signal saturation in some regions or insufficient resolution in others due to weak echo signals, affecting the overall uniformity and information content of the image. Sequential processing results in a lag in the system's assessment of image quality; any parameter adjustments rely on post-flight analysis or operator intervention, leading to slow response times and failing to meet the demands of applications requiring high real-time performance. Furthermore, the system has low resource utilization efficiency and lacks an intelligent dynamic scheduling mechanism.

[0004] While providing platform position and attitude compensation based solely on BeiDou navigation is a standard practice, it doesn't fully utilize its high-precision spatiotemporal information. Imaging quality assessment often relies on the statistical characteristics of the image itself or pixel-level comparisons with a baseline image, lacking quantitative evaluation methods that address the radar's physical scattering mechanisms and are tied to precise geographic coordinates. This prevents the system from accurately perceiving the physical differences between the current imaging state and the ideal state, thus hindering the provision of accurate and direct basis for adjusting front-end parameters. Summary of the Invention

[0005] The purpose of this invention is to provide a modular, miniature, high-performance airborne synthetic aperture radar system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a modular miniature high-performance airborne synthetic aperture radar system, the system comprising: With the assistance of BeiDou navigation, coordinated operation is achieved through radar data acquisition unit, navigation data interface unit, signal processing unit, fusion evaluation unit and control execution unit; The radar data acquisition unit is configured to acquire the raw echo signal of the airborne synthetic aperture radar and preprocess the raw echo signal to extract signal amplitude and phase features. The navigation data interface unit is configured to receive real-time positioning information and timing signals from the BeiDou navigation system, and convert the real-time positioning information and timing signals into coordinates and clock references usable by the radar. The signal processing unit is configured to identify the target region based on the signal amplitude and phase characteristics, calculate the scattering characteristics of the target region, and generate a preliminary radar image; The fusion evaluation unit is configured to align the preliminary radar image with the coordinate and clock reference, calculate the reference scattering characteristics using a preset radar model, and compare the scattering characteristics of the target area with the reference scattering characteristics to determine a deviation index. The control execution unit is configured to adjust the radar transmit power and scanning mode according to the deviation index, dynamically update the scanning timing based on the real-time positioning information, and allocate radar resources according to the updated scanning timing.

[0007] Preferably, the method by which the radar data acquisition unit acquires the raw echo signal of the airborne synthetic aperture radar includes: Deploy multi-band radar sensors to collect multi-angle echo data, which includes range information, azimuth information, and band characteristics; The multi-angle echo data is calibrated to remove noise and interference components, generating a clean echo signal. The pure echo signal is subjected to frame segmentation processing, which divides the continuous signal into multiple time segments, and a window function is applied to each time segment to enhance the signal characteristics. Extract the signal amplitude and phase features of each time segment, and combine the features of all time segments into a feature sequence.

[0008] Preferably, the method by which the signal processing unit identifies the target region based on the signal amplitude and phase characteristics includes: The feature sequence is input into the target detection network, which uses a convolutional structure to analyze the feature sequence layer by layer and outputs the position and size of the potential target. Cluster analysis is performed on the location and size of the potential targets to merge adjacent targets into continuous regions, and the average scattering intensity of each continuous region is calculated. Based on the comparison between the average scattering intensity and a preset threshold, the continuous region is divided into a high scattering region and a low scattering region, and the high scattering region is marked as the target region.

[0009] Preferably, the method by which the signal processing unit calculates the scattering characteristics of the target region includes: Multi-band features, including backscattering coefficients and polarization responses, are extracted for each target region. The scattering matrix is ​​calculated based on the multi-band characteristics, and the scattering matrix is ​​decomposed into eigenvalues ​​to obtain the main scattering component and the secondary scattering component. The scattering entropy is determined based on the ratio of the primary scattering component to the secondary scattering component, and a comprehensive scattering index is generated by combining the scattering power. A preliminary radar image is then generated based on the comprehensive scattering index.

[0010] Preferably, the method by which the fusion evaluation unit aligns the preliminary radar image with the coordinate and clock reference includes: Establish the transformation relationship between the radar coordinate system and the BeiDou navigation coordinate system, and map the pixels of the preliminary radar image to the global coordinate system through rigid body transformation; The radar timestamps were synchronized using the BeiDou time synchronization signal, and the time dimension of the preliminary radar image was aligned. The initial radar image is resampled in the global coordinate system to ensure that the image resolution is consistent with the spatial scale of the navigation data.

[0011] Preferably, the method by which the fusion evaluation unit calculates the reference scattering characteristics using a preset radar model includes: The preset radar model is trained based on historical radar data and BeiDou navigation data, and includes the mapping relationship between terrain elevation and scattering characteristics. Input the elevation information from the current BeiDou navigation data into the preset radar model, and output the predicted scattering value; The predicted scattering values ​​are smoothed to generate a continuous reference scattering field.

[0012] Preferably, the method by which the fusion evaluation unit compares the scattering characteristics of the target region with the reference scattering characteristics to determine the deviation index includes: Calculate the difference between the comprehensive scattering index of the target region and the continuous reference scattering field at the corresponding location; The difference is normalized to obtain the standardized deviation; The mean and variance of the standardized deviations of all target regions are calculated, and the mean and variance are combined to generate an overall deviation index.

[0013] Preferably, the method by which the control execution unit adjusts the radar transmit power and scanning mode according to the deviation index includes: The overall deviation index is input into the power control function, and the power control function outputs the base transmit power level; Based on the basic transmit power level, query the preset scanning mode table and select the corresponding beamwidth and pulse repetition frequency; By combining the platform speed information provided by BeiDou navigation, the scanning direction is dynamically adjusted to compensate for motion effects.

[0014] Preferably, the method by which the control execution unit dynamically updates the scanning timing based on the real-time positioning information includes: Monitor the rate of change of BeiDou navigation data. When the rate of change exceeds a threshold, trigger a scan timing update. Calculate the new scan start time and period to ensure that the radar coverage area matches the platform trajectory; The radar beam transmission timings are reassigned based on the new scanning sequence.

[0015] Preferably, the method by which the control execution unit allocates radar resources according to the updated scanning timing includes: The scanning period is divided into multiple time slots, and each time slot corresponds to a beam transmission opportunity. Time slots are allocated based on the priority of the target area, with higher priority areas receiving more time slots. Monitor radar energy consumption in real time and adjust time slot length to balance energy consumption and performance.

[0016] Compared with the prior art, the beneficial effects of the present invention are: By comparing preliminary radar images with reference scattering characteristics based on the BeiDou spatiotemporal reference to generate deviation indicators, and adjusting the radar front-end transmission power and scanning mode in real time accordingly, a closed-loop adaptive control mechanism is established. This system changes the traditional open-loop processing mode, enabling automatic optimization of data acquisition strategies based on real-time imaging results. The radar can intelligently allocate energy and scanning attention according to changes in target characteristics, avoiding local oversaturation or undersaturation of images and improving the consistency of image quality in complex scenes. The imaging process has shifted from static preset to dynamic response, enhancing adaptability to changes in the mission environment and reducing reliance on manual intervention.

[0017] By utilizing the precise coordinates and clock reference provided by BeiDou navigation, the measured scattering characteristics are physically aligned and compared with the theoretical reference scattering characteristics calculated by a pre-set radar model, enabling imaging quality assessment based on a physical model. This method goes beyond simple geometric correction and image post-processing, deeply integrating navigation information into the physical process of radar imaging. It allows the system to quantify the essential difference between the actual and expected performance of the payload under current imaging conditions, providing a direct and precise physical basis for adjusting front-end parameters, thus improving the accuracy of system perception and the scientific nature of decision-making. This deep integration provides core support for the intelligent and autonomous operation of radar systems. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating the working principle of the modular miniature high-performance airborne synthetic aperture radar system described in this invention. Figure 2 A flowchart for acquiring the original echo signal; Figure 3 A flowchart for identifying the target region; Figure 4 A graph showing the relationship between the ratio of primary to secondary scattering components and scattering entropy. Figure 5 This is a scatter plot showing the transformation relationship between the radar coordinate system and the global coordinate system X coordinate. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 This invention provides a modular, miniature, high-performance airborne synthetic aperture radar (SAR) system. The system includes a radar data acquisition unit, a navigation data interface unit, a signal processing unit, a fusion evaluation unit, and a control execution unit. The radar data acquisition unit is configured to acquire the raw echo signal from the airborne SAR and preprocess the raw echo signal to extract signal amplitude and phase characteristics. The navigation data interface unit is configured to receive real-time positioning information and timing signals from the BeiDou Navigation Satellite System and convert the real-time positioning information and timing signals into coordinate and clock references usable by the radar. The signal processing unit is configured to identify the target area based on the signal amplitude and phase characteristics, calculate the scattering characteristics of the target area, and generate a preliminary radar image. The fusion evaluation unit is configured to align the preliminary radar image with the coordinate and clock references, calculate the reference scattering characteristics using a preset radar model, and compare the scattering characteristics of the target area with the reference scattering characteristics to determine a deviation index. The control execution unit is configured to adjust the radar transmit power and scanning mode according to the deviation index, dynamically update the scanning timing based on real-time positioning information, and allocate radar resources according to the updated scanning timing.

[0021] Example 1: See Figure 2In practical implementation, the radar data acquisition unit of the modular micro high-performance airborne synthetic aperture radar system deploys multi-band radar sensors to collect multi-angle echo data. This multi-angle echo data includes range information, azimuth information, and band characteristics. During operation, the multi-band radar sensors simultaneously receive electromagnetic wave reflection signals from different frequency bands. After the multi-angle echo data acquisition is completed, the data is calibrated to remove noise and interference components, generating a clean echo signal. The calibration process employs a digital filtering algorithm to suppress random noise and system errors. In some embodiments, the clean echo signal undergoes frame processing, dividing the continuous signal into multiple time segments. A window function is applied to each time segment to enhance signal characteristics. The window function is designed based on the signal stationarity assumption, and its expression is: in: This represents the value of the window function at time t. and For preset constant coefficients, Indicates the length of a time segment. The time variable is used. It can be understood that framing processing reduces spectral leakage through overlapping addition, and the smoothness of signal edges is improved after the application of the window function. In specific implementation, the application of the window function is a key step in framing processing. The window function adopts a cosine form with preset constant coefficients and is designed based on the signal stationarity assumption to enhance the signal characteristics of each time segment. The window function is applied to the signal segments in a weighted manner, and an overlapping addition method is used during framing, that is, a partial overlap region is set between adjacent time segments to reduce spectral leakage. After the window function is applied, the transition region of the signal edge is smoothed, improving the distortion caused by signal truncation, thereby improving the accuracy of subsequent feature extraction. This implementation method ensures the coherence and integrity of the echo signal representation in the time-frequency domain, providing a reliable foundation for the preprocessing of the radar data acquisition unit. Optionally, the signal amplitude and phase characteristics of each time segment are extracted. The signal amplitude is calculated through envelope detection, and the phase characteristics are obtained through orthogonal demodulation. The features of all time segments are combined into a feature sequence, which is stored in matrix form for subsequent analysis. In some embodiments, the deployment of multi-band radar sensors considers azimuth and range sampling rate matching to ensure the integrity and consistency of multi-angle echo data. It is understood that the generation of feature sequences involves normalization and stitching operations on time-segment features to form a data output with uniform dimensions.

[0022] Example 2: See Figure 3In specific implementations, the method for the signal processing unit to identify target regions based on signal amplitude and phase characteristics includes inputting a feature sequence into a target detection network. The target detection network uses a convolutional structure to analyze the feature sequence layer by layer, outputting the position and size of potential targets. The convolutional structure contains multiple convolutional layers and pooling layers to extract spatial features. Cluster analysis is performed on the position and size of potential targets, merging adjacent targets into continuous regions, and calculating the average scattering intensity of each continuous region. The cluster analysis uses a density-based algorithm to identify spatial clusters. Based on a comparison of the average scattering intensity with a preset threshold, the continuous regions are divided into high-scattering and low-scattering regions, and the high-scattering region is marked as the target region. The preset threshold is set based on historical radar data statistics. In some embodiments, the method for the signal processing unit to calculate the scattering characteristics of the target region includes extracting multi-band features for each target region. The multi-band features include backscattering coefficients and polarization responses. The backscattering coefficients are obtained through radar equation inversion. A scattering matrix is ​​calculated based on the multi-band features, and eigenvalue decomposition is performed on the scattering matrix to obtain the main scattering component and the secondary scattering component. Eigenvalue decomposition generates eigenvectors and eigenvalues. The scattering entropy is determined by the ratio of the primary scattering component to the secondary scattering component. The scattering entropy reflects the randomness of target scattering. Combined with the scattering power, a comprehensive scattering index is generated. The scattering power is calculated from the echo signal energy. The comprehensive scattering index characterizes the electromagnetic properties of the target area, and a preliminary radar image is generated based on the comprehensive scattering index. The formula for calculating the scattering entropy can be understood as: in: Represents scattering entropy, This represents the eigenvalue corresponding to the principal scattering component. This represents the eigenvalue corresponding to the subscattering component. Optionally, the target detection network is trained using a labeled radar dataset to optimize network parameters and improve detection accuracy. In some embodiments, clustering analysis employs Euclidean distance to measure the similarity between potential targets, merging spatially neighboring regions. It can be understood that the calculation of the scattering matrix is ​​based on a linear combination of multi-band features, forming a symmetric matrix form. Optionally, the initial radar image is generated by mapping the integrated scattering indices to a grayscale space, forming visualized radar data.

[0023] See Figure 4This figure is a core feature analysis diagram of the signal processing unit's calculation of the target region's scattering characteristics. The horizontal axis represents the ratio of the primary to secondary scattering components, corresponding to the ratio of the eigenvalues ​​of the primary scattering component to the eigenvalues ​​of the secondary scattering components after the signal processing unit performs eigenvalue decomposition on the target region's scattering matrix. The vertical axis represents the scattering entropy, an index calculated based on this ratio, reflecting the degree of randomness in target scattering. This figure visually presents the core quantitative relationship of scattering characteristics and is a key visualization result for the signal processing unit's analysis of the target's electromagnetic scattering characteristics. Through this relationship, the eigenvalue information of the scattering matrix can be transformed into a scattering entropy index characterizing the target's characteristics, providing a quantitative basis for the subsequent generation of preliminary radar images. This embodies the technical logic of extracting scattering characteristics based on eigenvalue decomposition of the scattering matrix.

[0024] Example 3: In a specific implementation, the method by which the fusion evaluation unit aligns the preliminary radar image with coordinate and clock references includes establishing a transformation relationship between the radar coordinate system and the BeiDou navigation coordinate system. A rigid body transformation is used to map the pixels of the preliminary radar image to the global coordinate system, including the calculation of rotation matrices and translation vectors. The BeiDou timing signal is used to synchronize radar timestamp acquisition and align the time dimension of the preliminary radar image. Timestamp alignment is achieved through time interpolation. In the global coordinate system, the preliminary radar image is resampled to ensure that the image resolution is consistent with the spatial scale of the navigation data. The resampling uses a bilinear interpolation algorithm to process pixel mapping. In some embodiments, the method by which the fusion evaluation unit calculates reference scattering characteristics using a preset radar model includes: the preset radar model is trained based on historical radar data and BeiDou navigation data, containing a mapping relationship between terrain elevation and scattering characteristics. The preset radar model is established using regression analysis. Elevation information from the current BeiDou navigation data is input into the preset radar model, and predicted scattering values ​​are output. The predicted scattering values ​​are obtained through forward propagation calculation of the model. The predicted scattering values ​​are smoothed to generate a continuous reference scattering field. The smoothing process uses a Gaussian filtering method to eliminate data abrupt changes. It can be understood that the mathematical expression for rigid body transformation is: in: and Represents coordinates in the global coordinate system. and Represents the coordinates in the radar coordinate system. Indicates the rotation angle between coordinate systems. and This represents the translation vector. Optionally, time dimension alignment is achieved through establishing a correspondence table between radar acquisition times and BeiDou timing signals to achieve precise synchronization. In some embodiments, the resampling process considers the proportional relationship between the pixel spacing of the radar image and the grid spacing of the navigation data, and performs scale normalization processing. It can be understood that the training of the preset radar model uses a large amount of historical datasets, and the model parameters are optimized by minimizing the prediction error.

[0025] See Figure 5 This figure corresponds to the initial radar image and coordinate reference alignment stage of the fusion evaluation unit, visually representing the transformation effect between the radar coordinate system and the BeiDou global coordinate system: the horizontal axis represents the X-coordinate of the radar coordinate system, and the vertical axis represents the X-coordinate of the global coordinate system. The trend in the figure shows a significant positive correlation between the scattered points, indicating good linear consistency in the transformation between the radar local coordinates and the BeiDou global coordinates, demonstrating the effectiveness of the rigid body transformation + resampling coordinate alignment method. A small amount of discrete fluctuation is a reasonable error in the data calibration process, consistent with the actual processing characteristics of bilinear interpolation resampling. The core function of this figure is to verify the accuracy of the coordinate alignment. Only when the radar coordinates and the BeiDou global coordinates achieve a stable mapping can the subsequent fusion evaluation unit compare the initial radar image and the reference scattering field under the same spatiotemporal reference. This is a key verification result of the spatiotemporal reference fusion technology logic.

[0026] Example 4: In specific implementation, the method by which the fusion evaluation unit compares the scattering characteristics of the target area with those of the reference scattering characteristics to determine the deviation index includes calculating the difference between the comprehensive scattering index of the target area and the continuous reference scattering field at corresponding positions. The difference is calculated through pixel-by-pixel arithmetic subtraction. The difference is normalized to obtain the standardized deviation. The normalization process uses a linear scaling method to transform the values ​​to a uniform range. The mean and variance of the standardized deviations of all target areas are statistically analyzed, and the mean and variance are fused to generate the overall deviation index. The fusion process uses a linear weighted combination method. In some embodiments, the method by which the control execution unit adjusts the radar transmit power and scanning mode according to the deviation index includes inputting the overall deviation index into the power control function. The power control function outputs the basic transmit power level and uses a piecewise linear mapping relationship. The preset scanning mode table is queried according to the basic transmit power level to select the corresponding beamwidth and pulse repetition frequency. The preset scanning mode table stores the mapping relationship between power level and parameters. Combined with the platform speed information provided by Beidou navigation, the scanning direction is dynamically adjusted to compensate for motion effects. The scanning direction adjustment is achieved through a beam control algorithm. It can be understood that the formula for calculating the overall deviation index is: in: Indicates the overall deviation index. The mean of the standardized deviation is represented. The variance representing the standardized deviation, and This represents the preset fusion coefficient. Optionally, the normalization process uses a maximum-minimum scaling method to transform the difference to between zero and one. In some embodiments, the input-output relationship of the power control function is implemented through a lookup table to ensure real-time response performance. Referring to Table 1, the preset scan mode table contains radar operating parameters corresponding to different power levels.

[0027] Table 1: Preset Scanning Modes It is understandable that when dynamically adjusting the scanning direction, the platform speed information is used to calculate the beam deflection angle. Optionally, the selection of beamwidth and pulse repetition frequency is matched based on the discrete levels of a preset scanning mode table.

[0028] Example 5: In a specific implementation, the method for the control execution unit to dynamically update the scanning timing based on real-time positioning information includes monitoring the position change rate of BeiDou navigation data. When the position change rate exceeds a preset threshold, a scanning timing update is triggered. The preset threshold is set according to the platform's motion characteristics. A new scanning start time and period are calculated to ensure that the radar coverage area matches the platform trajectory. The scanning start time is adjusted by a time offset, and the scanning period is dynamically scaled according to the platform speed. The radar beam transmission timing is reallocated according to the new scanning timing, and the beam transmission timing allocation is implemented based on a time slot mechanism. In some embodiments, the position change rate is monitored by calculating the instantaneous velocity change using differential BeiDou positioning data. When the instantaneous velocity change exceeds a threshold, the timing update process is immediately initiated. The scanning period adjustment is calculated using a formula: in: Indicates a new scan cycle. Indicates the basic scan cycle. Indicates the periodic adjustment coefficient. This represents the difference between the rate of position change and the reference value. It can be understood that the new scan start time is determined by adding the calculated time offset to the current time, ensuring that the radar beam coverage is synchronized with the platform's current position. Optionally, the reallocation of beam transmission opportunities is achieved by updating the radar system's timer settings, with timer parameters dynamically configured according to the new scan sequence. The method by which the control execution unit allocates radar resources according to the updated scan sequence includes dividing the scan cycle into multiple time slots, each time slot corresponding to one beam transmission opportunity. The time slot length is dynamically set according to the scan mode. Time slots are allocated based on the priority of the target area, with higher priority areas allocated more time slots. The priority is determined based on the scattering characteristics and geographical location of the target area. Real-time monitoring of radar energy consumption is conducted, and the time slot length is adjusted to balance energy consumption and performance. Energy consumption is collected in real-time by a power sensor. In some embodiments, the time slots are divided using equal intervals or variable intervals. Equal intervals simplify the scheduling logic, while variable intervals adapt to different area scanning needs. The time slot allocation algorithm is based on priority queue management, with higher priority target areas receiving transmission resources first. It can be understood that radar energy consumption monitoring includes transmission power and duty cycle measurements, and the time slot length is automatically reduced when energy consumption approaches a limit. Optionally, the time slot length can be adjusted using a linear decreasing strategy to gradually reduce the proportion of time slots in low-priority areas in order to maintain stable system operation.

[0029] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0030] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A modular, miniature, high performance airborne synthetic aperture radar system, characterized by, The system realizes cooperative operation through a radar data acquisition unit, a navigation data interface unit, a signal processing unit, a fusion evaluation unit and a control execution unit under the assistance of Beidou navigation; The radar data acquisition unit is configured to obtain original echo signals of an airborne synthetic aperture radar, and pre-process the original echo signals to extract signal amplitude and phase features; The navigation data interface unit is configured to receive real-time positioning information and timing signals of a Beidou navigation system, and convert the real-time positioning information and timing signals into coordinates and clock references available for radar; The signal processing unit is configured to identify a target area based on the signal amplitude and phase features, calculate scattering characteristics of the target area, and generate a preliminary radar image; The fusion evaluation unit is configured to align the preliminary radar image with the coordinates and clock references, calculate reference scattering characteristics through a preset radar model, and compare the scattering characteristics of the target area with the reference scattering characteristics to determine a deviation index; The control execution unit is configured to adjust radar transmission power and scanning mode according to the deviation index, dynamically update scanning timing based on the real-time positioning information, and allocate radar resources according to the updated scanning timing.

2. The modular miniature high performance airborne synthetic aperture radar system of claim 1, wherein, The method for the radar data acquisition unit to obtain original echo signals of an airborne synthetic aperture radar comprises: Deploying multi-band radar sensors to collect multi-angle echo data, the multi-angle echo data including range information, azimuth information and band characteristics; Calibrating the multi-angle echo data to remove noise and interference components and generate pure echo signals; Frame processing the pure echo signals, dividing continuous signals into multiple time segments, and applying a window function to each time segment to enhance signal features; Extracting signal amplitude and phase features of each time segment, and combining features of all time segments into a feature sequence.

3. The modular miniature high performance airborne synthetic aperture radar system of claim 2, wherein, The method for the signal processing unit to identify a target area based on the signal amplitude and phase features comprises: Inputting the feature sequence into a target detection network, the target detection network using a convolutional structure to analyze the feature sequence layer by layer and outputting positions and sizes of potential targets; Performing cluster analysis on the positions and sizes of the potential targets, merging adjacent targets into continuous regions, and calculating scattering intensity means of each continuous region; According to a comparison between the scattering intensity means and a preset threshold, dividing the continuous regions into high-scattering regions and low-scattering regions, and marking the high-scattering regions as target regions.

4. The modular miniature high performance airborne synthetic aperture radar system of claim 3, wherein, The method for the signal processing unit to calculate scattering characteristics of a target region comprises: Extracting multi-band features including backscattering coefficients and polarization responses for each target region; Calculating a scattering matrix based on the multi-band features, and performing eigenvalue decomposition on the scattering matrix to obtain main scattering components and secondary scattering components; Determining scattering entropy according to a ratio of the main scattering components to the secondary scattering components, generating a comprehensive scattering index in combination with scattering power, and generating a preliminary radar image based on the comprehensive scattering index.

5. The modular miniature high performance airborne synthetic aperture radar system of claim 4, wherein, The method for the fusion evaluation unit to align the preliminary radar image with the coordinates and clock references comprises: The conversion relationship between the radar coordinate system and the Beidou navigation coordinate system is established, and the pixel points of the preliminary radar image are mapped to the global coordinate system through rigid body transformation; The radar acquisition time stamp is synchronized using the Beidou timing signal, and the time dimension of the preliminary radar image is aligned; In the global coordinate system, the preliminary radar image is resampled to ensure that the image resolution is consistent with the spatial scale of the navigation data.

6. The modular miniature high performance airborne synthetic aperture radar system of claim 5, wherein, The method for calculating the reference scattering characteristic by the preset radar model includes: The preset radar model is trained based on historical radar data and Beidou navigation data and contains the mapping relationship between terrain elevation and scattering characteristic; The elevation information in the current Beidou navigation data is input into the preset radar model, and a predicted scattering value is output; The predicted scattering value is smoothed to generate a continuous reference scattering field.

7. The modular miniature high performance airborne synthetic aperture radar system of claim 6, wherein, The method for comparing the scattering characteristic of the target region with the reference scattering characteristic to determine the deviation index by the fusion evaluation unit includes: The difference between the comprehensive scattering index of the target region and the continuous reference scattering field at the corresponding position is calculated; The difference is normalized to obtain a standardized deviation; The mean and variance of the standardized deviations of all target regions are calculated, and the overall deviation index is generated by fusing the mean and variance.

8. The modular miniature high performance airborne synthetic aperture radar system of claim 7, wherein, The method for adjusting the radar transmission power and scanning mode according to the deviation index by the control execution unit includes: The overall deviation index is input into a power control function, and the power control function outputs a basic transmission power level; The corresponding beam width and pulse repetition frequency are selected by querying a preset scanning mode table according to the basic transmission power level; The scanning direction is dynamically adjusted to compensate for the motion effect in combination with the platform speed information provided by the Beidou navigation.

9. The modular miniature high performance airborne synthetic aperture radar system of claim 8, wherein, The method for dynamically updating the scanning timing based on the real-time positioning information by the control execution unit includes: The position change rate of the Beidou navigation data is monitored, and when the position change rate exceeds a threshold, the scanning timing update is triggered; The new scanning start time and period are calculated to ensure that the radar coverage area matches the platform trajectory; The radar beam transmission opportunity is redistributed according to the new scanning timing.

10. The modular miniature high performance airborne synthetic aperture radar system of claim 9, wherein, The method for allocating radar resources according to the updated scanning timing by the control execution unit includes: The scanning period is divided into multiple time slots, and each time slot corresponds to a beam transmission opportunity; The time slots are allocated according to the priority of the target region, and more time slots are allocated to high-priority regions; The radar energy consumption is monitored in real time, and the time slot length is adjusted to balance energy consumption and performance.

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