Sub-aperture self-focusing CSAR imaging method combined with topographic relief error compensation

Abstract

The invention discloses a subaperture self-focusing CSAR imaging method combined with topographic relief error compensation, which comprises the following steps of: reading digital elevation model data of an observation scene, supplementing height direction information for grid points of an imaging area, and eliminating height direction errors; an echo signal acquired by the CSAR is converted into a frequency domain, multiplied by a reference signal and converted into a time domain signal, and range pulse compression is completed; segmenting the full-circumference aperture into a plurality of non-overlapped sub-apertures, and imaging the sub-aperture data by using a backward projection algorithm to obtain a sub-aperture image; a self-focusing algorithm based on an image sharpest criterion is adopted to process each sub-aperture image to carry out motion error compensation, phase errors are estimated, and iteration estimation is carried out for multiple times; and performing accumulation processing on the sub-aperture images after the self-focusing processing to obtain a high-precision full-aperture CSAR image. Compared with the prior art, the precision and quality of CSAR imaging can be improved by combining topographic relief error compensation and sub-aperture self-focusing processing.

Description

Technical Field

[0001] The present invention relates to the field of radar technology, and more specifically, to a sub-aperture self-focusing CSAR imaging method combined with terrain fluctuation error compensation. Background Art

[0002] Synthetic Aperture Radar (SAR) obtains high resolution in the range direction by emitting wide bandwidth signals. At the same time, the radar platform observes the scene targets at a wide angle and can obtain high resolution in the azimuth direction. As one of the sensors with new quality productivity in the field of microwave remote sensing and advanced arrays, SAR technology has been rapidly developed and widely concerned. SAR imaging can obtain more electromagnetic scattering information of the observed target by reconstructing the target scattering function, which helps to analyze, classify and identify the target characteristics. SAR can provide all-weather and all-day reconnaissance capabilities, has significant advantages in remote sensing observation, and has a wide range of uses.

[0003] Circular SAR (CSAR) refers to a radar platform that performs 360-degree observation around a scene target, with its antenna transmitting beam always pointing to the target scene. Compared with traditional SAR that uses a straight trajectory, CSAR can perform 360-degree observation, thereby obtaining a more complete target profile and better background clutter suppression. Therefore, the omnidirectional scattering characteristics of scene targets obtained by CSAR can effectively improve the detection performance of targets.

[0004] CSAR high-resolution images can provide richer target feature information, which helps to identify, classify, locate and track different targets with high precision, and then obtain the position and motion trajectory of the target. However, due to terrain undulation errors and motion errors of the CSAR platform, CSAR imaging will be blurred, and high-resolution CSAR images cannot be effectively obtained. Related processing methods are needed to further obtain high-quality CSAR images.

[0005] CSAR imaging processing is usually based on the flat ground assumption, that is, the imaging scene is considered to be an ideal plane, and all targets are distributed on this plane. In fact, the observation scene inevitably has ups and downs in the terrain, so that the actual imaging area cannot fully meet the assumption of the ideal plane. CSAR usually uses slant range imaging, which records the slant range rather than the vertical distance. When imaging, the scattering intensity of the same target is projected onto the imaging plane at equal distances. Therefore, when the observation scene has ups and downs in the terrain, the target projection position in the imaging result will deviate from the actual imaging position, which will cause geometric displacement in the CSAR image, thereby reducing the CSAR's ability to resolve the target in the vertical slant range direction, making it impossible for CSAR to obtain high-precision images.

[0006] In addition, due to various factors such as air flow and actual flight control, there is an inevitable deviation between the actual flight trajectory of the airborne CSAR platform and the ideal circular trajectory. However, the positioning accuracy that commercial-grade inertial navigation systems (INS) and global positioning systems (GPS) can provide is generally only in the order of meters, which cannot meet the needs of high-resolution CSAR imaging. Due to the physical space, weight and cost limitations of the platform, airborne CSAR often cannot use ultra-high-precision measurement sensors, and usually uses INS or GPS to record the flight trajectory. CSAR imaging is more sensitive to flight trajectory data, and excessive measurement errors of INS and GPS will cause serious defocusing of CSAR images. Therefore, the application of autofocusing algorithms is particularly critical in high-resolution CSAR imaging processing. However, existing methods usually perform autofocusing on full-aperture CSAR echo data, but the imaging quality is not improved much, because the imaging data quality of some sub-aperture angles is poor, which will lead to poor quality of the full-aperture image obtained in coherent accumulation.

[0007] The prior art discloses a circular synthetic aperture radar imaging method and system, equipment, medium and terminal, which performs aperture segmentation on complete CSAR data, and uses a fast algorithm to image sub-aperture data separately in combination with the data to obtain sub-images; local images located at different elevation planes in the sub-aperture images are used as references for registration to obtain a set of CSAR images with different depths of field; and the obtained images with different depths of field are multi-focus fused to obtain a fully focused image. The drawback of this solution is that the imaging data quality of some sub-aperture angles is poor, which will lead to poor quality of the full-aperture image obtained in coherent accumulation.

[0008] To this end, in combination with the above requirements and the defects of the prior art, the present application proposes a sub-aperture self-focusing CSAR imaging method combined with terrain relief error compensation. Summary of the invention

[0009] The present invention provides a sub-aperture self-focusing CSAR imaging method combined with terrain undulation error compensation, which improves the accuracy and quality of CSAR imaging by combining terrain undulation error compensation and sub-aperture self-focusing processing.

[0010] The primary purpose of the present invention is to solve the above technical problems. The technical solution of the present invention is as follows: A first aspect of the present invention provides a sub-aperture self-focusing CSAR imaging method combined with terrain relief error compensation, the method comprising the following steps: S1. Read the digital elevation model data of the observation scene, add altitude information to the grid points in the imaging area, and eliminate altitude errors.

[0011] S2. The echo signal collected by CSAR is transformed into the frequency domain, multiplied by the reference signal and converted into a time domain signal to complete the range pulse compression.

[0012] S3, dividing the full circumferential aperture into a plurality of non-overlapping sub-apertures, imaging the sub-aperture data using a back-projection algorithm, and obtaining a sub-aperture image.

[0013] S4. Using the autofocus algorithm based on the sharpest image criterion to process each sub-aperture image, perform motion error compensation, estimate the phase error, and perform multiple iterative estimates.

[0014] S5. Accumulate the sub-aperture images after the self-focusing process to obtain a high-precision full-aperture CSAR image.

[0015] The second aspect of the present invention provides a sub-aperture self-focusing CSAR imaging system combined with terrain undulation error compensation. The system is used for the sub-aperture self-focusing CSAR imaging method combined with terrain undulation error compensation, and includes: a data processing module, a signal processing module, an error compensation module and an imaging module.

[0016] The data processing module reads the digital elevation model data of the observation scene, supplements the height information for the grid points in the imaging area, and eliminates the height error. The signal processing module transforms the echo signal collected by the CSAR into the frequency domain, multiplies it by the reference signal and converts it into a time domain signal to complete the range pulse compression. The error compensation module receives the data output by the data processing module and the signal processing module, divides the full circular aperture into a plurality of non-overlapping sub-apertures, uses the back-projection algorithm to image the sub-aperture data to obtain the sub-aperture image, and uses the self-focusing algorithm based on the sharpest image criterion to process each sub-aperture image for motion error compensation, estimates the phase error, and inputs it into the imaging module after multiple iterative estimations. The imaging module accumulates the sub-aperture images after the self-focusing processing to obtain a high-precision full-aperture CSAR image.

[0017] Compared with the prior art, the technical solution of the present invention has the following beneficial effects: The present invention provides a sub-aperture self-focusing CSAR imaging method combined with terrain undulation error compensation, which supplements the height information of the grid points in the imaging area, and performs motion error and phase error compensation after obtaining the sub-aperture image by using the back-projection algorithm, thereby solving the problems of terrain undulation of the observation scene and airborne CSAR motion error, thereby improving the quality and effect of CSAR imaging, thereby ensuring that CSAR achieves high-quality imaging in complex environments, and the obtained high-precision CSAR images can provide richer target feature information, which is helpful for high-precision identification, classification, positioning and tracking of different targets, and obtaining the position and motion trajectory of the target, thereby improving the image interpretation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a flow chart of a sub-aperture self-focusing CSAR imaging method combined with terrain relief error compensation.

[0019] Figure 2 Schematic diagram of range displacement caused by terrain undulation in CSAR imaging in one embodiment of the present invention.

[0020] Figure 3 In one embodiment of the present invention, the target Geometric diagram of the ideal CSAR motion trajectory.

[0021] Figure 4 FIG. 1 is a geometric diagram of onboard CSAR imaging when motion errors exist in one embodiment of the present invention.

[0022] Figure 5 This is an imaging result diagram of a point target with terrain undulation error in one embodiment of the present invention.

[0023] Figure 6 This is an imaging result diagram of a point target compensating for terrain undulation error in one embodiment of the present invention.

[0024] Figure 7 This is an azimuth profile diagram before and after terrain undulation error compensation when the point target height errors are 14m, 18m, 10m, and 6m respectively in an embodiment of the present invention.

[0025] Figure 8 This is a comparison chart of the imaging result of a vehicle with 1m and -1m terrain undulation errors and the imaging result compensated by DEM data.

[0026] Fig. 9 This is the imaging result of the Gotcha scene with terrain undulation error.

[0027] Fig.10 This is the imaging result diagram of the Gotcha scene with terrain relief error and compensation for the terrain relief error.

[0028] Fig.11 This is the airborne CSAR flight trajectory diagram of Gotcha data.

[0029] Fig.12 It is a schematic diagram of the experimental results of sub-aperture original BP algorithm imaging and sub-aperture self-focusing imaging in an embodiment of the present invention.

[0030] Fig.13 It is an enlarged view and azimuthal cross-sectional view of the results of sub-aperture original BP algorithm imaging and sub-aperture self-focusing imaging in one embodiment of the present invention.

[0031] Fig.14 The figure is a comparison diagram between the imaging result of the present invention and the imaging result of the traditional method in a Gotcha scenario in one embodiment.

[0032] Fig.15 The schematic diagram of the sub-aperture self-focusing CSAR imaging system combined with terrain relief error compensation of the present invention. DETAILED DESCRIPTION

[0033] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0034] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0035] Example 1 like Figure 1 As shown, the present invention provides a sub-aperture self-focusing CSAR imaging method combined with terrain undulation error compensation, and the method comprises the following steps: S1. Read the digital elevation model data of the observation scene, supplement the height information of the grid points in the imaging area, and eliminate the height error.

[0036] S2. The echo signal collected by CSAR is transformed into the frequency domain, multiplied by the reference signal and converted into a time domain signal to complete the range pulse compression.

[0037] S3, dividing the full circumferential aperture into a plurality of non-overlapping sub-apertures, imaging the sub-aperture data using a back-projection imaging algorithm, and obtaining a sub-aperture image.

[0038] S4. Using the autofocus algorithm based on the sharpest image criterion to process each sub-aperture image, perform motion error compensation, estimate the phase error, and perform multiple iterative estimates.

[0039] S5. Accumulate the sub-aperture images after the self-focusing process to obtain a high-precision full-aperture CSAR image.

[0040] According to the above technical features, the present invention uses the digital elevation model (DEM) data of the observation scene to set the CSAR imaging grid to eliminate the height error. Then, the full-circular aperture echo signal is subjected to range pulse compression processing, and sub-aperture segmentation is performed, and the sub-aperture data is imaged using the back projection (BP) algorithm to obtain a preliminarily focused sub-aperture image. Then, the sub-aperture image is subjected to multiple iterative self-focusing processes to obtain a well-focused sub-aperture image. Finally, the focused sub-aperture images are incoherently accumulated to obtain a high-precision full-aperture CSAR image. The present invention can simultaneously solve the terrain undulation error and the CSAR motion error to obtain a high-precision CSAR image. The influence of the terrain undulation error on CSAR imaging is analyzed, a signal model of the CSAR motion error is established, a self-focusing algorithm based on back projection is studied, and a method for solving the motion error estimate based on the sharpest image criterion is analyzed. Then, a sub-aperture self-focusing CSAR high-precision imaging method combined with terrain undulation error compensation is proposed. This method first uses the observation scene DEM data to compensate for the terrain undulation error of the observation scene, and then uses the self-focusing algorithm combined with BP to focus the sub-aperture echo signal for imaging. Finally, the focused sub-aperture image is accumulated to obtain a high-precision full-aperture CSAR image, and the effectiveness of this method is verified by experimental data. The present invention can ensure that CSAR can achieve high-quality imaging in complex environments. The obtained high-precision CSAR images can provide richer target feature information, which is helpful for high-precision identification, classification, positioning and tracking of different targets, and obtaining the position and motion trajectory of the target, thereby improving the image interpretation effect.

[0041] In this embodiment, Figure 2 As shown in the figure, it is a schematic diagram of calculating the distance displacement caused by terrain undulation. In the step S1, the height information is supplemented to the grid points in the imaging area. The calculation process of eliminating the height error is as follows: according to the position of the point target Height difference from the imaging area plane , and slow time Time point target To the radar antenna phase center Slope distance , calculate the error of the projection position of the point target on the imaging plane caused by the terrain undulation error, and then calculate the distance displacement caused by the terrain undulation according to the position relationship between the point target and the imaging area plane, so as to compensate for the terrain undulation error, where, is the height of CSAR motion platform, is the location of the point target.

[0042] The calculation formula of step S1 is: (1) (2) in, Indicates point target Image point, and point target With the same slope distance, i.e. point target The position in the CSAR image after imaging, Indicates the ground distance of CSAR in the imaging plane; if the point target There is an error between the height and the height of the imaging plane , then the point target The projected ground distance in the imaging plane can be calculated using the following geometric relationship: (3) The error of the target projection position on the imaging plane caused by the terrain undulation error is expressed as: (4) Among them, it can be seen from formula (4) that The numerical sign and If the target is located above the imaging plane, its reflected signal will be received by CSAR earlier than the point in the imaging plane with the same incident angle, and the imaging position of the target in the CSAR image will move toward the direction close to CSAR; if the target is located below the imaging plane, the reflected signal will arrive at CSAR later, causing the target imaging position to move away from CSAR; if the actual height of the target is consistent with the height of the imaging plane, there is no deviation in the target imaging position.

[0043] Since the CSAR system uses a 360-degree observation imaging mode, the target imaging displacement becomes more complicated. Figure 3 As shown, let any point in the imaging scene be , CSAR is located at point , which is The projection of the plane is a point In this embodiment, the motion error is ignored and it is assumed that the CSAR antenna is aligned with the center point during the imaging process. The incident angle remains unchanged and is Then, the distance displacement caused by terrain undulation is calculated based on the positional relationship between the point target and the imaging area plane as follows: First, the point target is calculated The beam incident angle , and correct the error of the target projection position on the imaging plane caused by the terrain undulation error, and jointly calculate the distance displacement caused by the terrain undulation , the formula is as follows: (5) Since the flight altitude of the airborne CSAR system is much larger than the error of terrain undulation, that is, , points caused by terrain undulation The incident angle The change of can be ignored, then formula (4) can be rewritten as: (6) Combining equations (4), (5) and (6), we can obtain the distance displacement caused by terrain undulation as: (7) in, Indicates the CSAR antenna's focus on the center point during imaging For an incident angle of 10°, the range displacement caused by terrain undulation is proportional to the terrain undulation error, and the closer the point target is to the projection point of the CSAR on the imaging plane, the greater the range displacement caused by the terrain undulation.

[0044] It can be concluded that the range displacement caused by terrain undulation is proportional to the amount of terrain undulation error, and the closer the point target is to the projection point of CSAR on the imaging plane, the greater the range displacement caused by terrain undulation. Therefore, in order to ensure that CSAR can still perform high-precision imaging of the target when there is terrain undulation error in the target observation scene, it is necessary to take measures to compensate for the terrain undulation error.

[0045] In step S2, the echo signal collected by CSAR is transformed into the frequency domain, multiplied by the reference signal and converted into a time domain signal. The specific process of completing the range pulse compression is as follows: Calculate the CSAR first The position vector of the antenna phase center at the azimuth sampling position Position vector to point target One-way slope distance According to the influence of motion error on the quality of CSAR two-dimensional imaging, the echo signal received by the antenna is orthogonally demodulated and range-direction pulse compressed.

[0046] Due to airflow interference and the inability to precisely control the flight trajectory, CSAR will inevitably have motion errors when observing targets, resulting in low imaging quality. The following specifically analyzes the impact of motion errors on the quality of CSAR two-dimensional imaging.

[0047] In a specific embodiment, Figure 4 The 3D geometry diagram of the airborne CSAR platform is shown in Figure 1. Axis, at height A radius of The 360° circular trajectory can be achieved. The azimuth of the CSAR platform with the axis as the relative starting point is , whose value is , the CSAR beam always points to the center of the scene , it is assumed that the CSAR beam observation viewing angle remains unchanged during the entire circular motion under far-field conditions. Figure 4 In the figure, the red curve is the actual observed motion curve of CSAR, and the black circular curve is the planned ideal circular observed motion curve. It can be seen that there is a deviation between the actual motion curve of CSAR and the ideal circular curve, and motion error compensation measures need to be taken.

[0048] The step S2 includes the following expression: Set CSAR The phase center position vector of the azimuth sampling position is , set point is any point target in the observation scene, and its position vector is recorded as The antenna phase center is Point target The one-way slope distance between the two is: (8) The echo signal received by the antenna can be expressed as follows after orthogonal demodulation and range pulse compression: (9) The step S3 specifically includes: dividing the full circular aperture into 120 non-overlapping sub-apertures, each sub-aperture having an azimuth accumulation angle of 3°, and imaging the sub-aperture data after CSAR range pulse compression data using a back-projection algorithm.

[0049] It should be noted that in the imaging plane, the vector at any point can be expressed as The CSAR system sends and receives signals from multiple azimuth positions, each of which provides a pulse echo signal, and the entire imaging process involves the echo signals received from these azimuth positions. The imaging result at is expressed as: (10) (11) Therefore, the image obtained by focused imaging is obtained by summing the projection vectors of the echo signals at all sampling positions on all grid points in the imaging scene. The mathematical expression is: (12) There is a measurement error between the CSAR motion trajectory measured by the sensor and the actual motion trajectory, which directly affects the accuracy and reliability of the imaging system. In order to specifically analyze the phase error caused by the position error, the following mathematical model can be used to represent it. The phase center coordinates of the CSAR are , then the phase error caused by the measurement error can be expressed as: (13) Since the change of the imaging plane grid is usually much smaller than the distance from the CSAR to the target, when establishing the phase error compensation model, for the sake of simplicity, it is usually assumed that the phase error Not affected by the imaging grid point position The impact of the change is expressed as When the above phase error exists, due to the influence of the phase error, the projection of the range-to-compression pulse is shifted in phase, which is multiplied by an error phase factor, and its expression is: (14) In the case of a large phase error, Summing will result in poor imaging focusing, so the echo signal needs to be processed in blocks and the phase error needs to be compensated.

[0050] Assume that CSAR is conducted Each sampling position has a corresponding phase error. Suppose the estimated phase error value of each sampling position is Therefore, we first need to estimate the phase error value , and then use the estimated The phase error is compensated by the phase factor, and finally a high-precision imaging CSAR image is obtained. The expression is: (15) Among them, formula (10) is the BP algorithm imaging expression of CSAR, and the position vector of the antenna phase center is , the position vector of the point target , represents the echo signal after orthogonal demodulation and range pulse compression processing, Represents the vector at any point in the imaging plane The imaging results at Indicates CSAR phase center measurement position Grid points on the imaging plane The slope distance between Indicates CSAR phase center position Grid points on the imaging plane The slope distance between Indicates The projection vector of the echo signal at the sampling position to all grid points in the imaging scene, is the total number of CSAR azimuth sampling points, located at a certain point in the plane The back projection value at In Chinese , It means that the projection vectors of all grid points in the imaging scene are accumulated to obtain a high-precision image with phase error compensation.

[0051] It should be noted that positioning sensors are usually used to measure the motion trajectory of the carrier aircraft. However, due to factors such as sensor measurement errors, the complexity of CSAR dynamic motion, the limitations of the positioning system and the special requirements of high-resolution imaging, existing positioning sensors are still difficult to meet the measurement requirements of high-resolution CSAR imaging, and continuous improvement of sensor technology, data processing algorithms and system integration methods are needed.

[0052] According to the above technical features, when there is motion error in the CSAR platform, the echo signal can be modeled as the original echo data multiplied by the phase error exponential factor. The imaging manifestation is that the received echo signal cannot be effectively accumulated, resulting in blurred imaging. The phase error needs to be estimated and compensated to obtain high-precision imaging results.

[0053] The specific process of step S4 is: using the coordinate descent geometry solution autofocus algorithm based on the sharpest image criterion to process each sub-aperture image, and estimate the phase error of each sub-aperture image. In each iteration, the sub-aperture image will be compensated according to the current error estimation value, and the phase error will be re-estimated based on the compensated sub-aperture image. After each iterative processing, the phase error will be smaller. Finally, the sub-aperture image is multiplied by the compensation factor of the phase error to achieve autofocus processing of the sub-aperture image.

[0054] It should be noted that both the contrast optimization algorithm and the contrast enhancement phase correction algorithm are based on the establishment of a phase error compensation model, aiming to improve the contrast and quality of SAR images. However, studies have shown that these models have certain limitations in practical applications, especially they ignore the spatial variability of the phase error, which means that these models assume that the phase error is constant across the entire image. However, in actual situations, the phase error may vary with the change of spatial position, especially in complex ground scenes. Therefore, when applying a model-based autofocus algorithm, it is necessary to first evaluate whether the phase error satisfies the model. This assumption, that is, the phase error is independent of the grid point position, is not satisfied. If the above conditions are not met, directly applying these algorithms may lead to a decrease in imaging quality or even failure to obtain an effectively focused image.

[0055] The self-focusing algorithm based on BP processing is also based on the above model and uses the sharpest image criterion to estimate the phase error, that is, to seek the optimal solution of the following formula: (16) in is a function for evaluating the sharpness of an image, namely, the energy evaluation function. The larger the value of is, the sharper the image is, that is, the more obvious the target features in the image are. Since equation (16) has no analytical solution, the coordinate descent method is used to iteratively optimize and estimate it. At this time, the energy evaluation function is the objective function used to measure image quality. After iterations, a set of estimates is obtained. , then in the next iteration The estimation of the parameters It will be calculated by the following formula (17): (17) Equation (17) shows that each update of the estimate depends on the sequence of the previous estimate, where the update of each parameter takes into account maintaining the best estimate state of other parameters in the current or previous iteration.

[0056] In order to further optimize the imaging results, the expression of the imaging results is rewritten to more intuitively show the changes in imaging quality during the iteration process. The imaging result after the estimation iteration is: (18) Find ,and: (19) Let the fixed constant part be , which contains the parameters to be estimated , then: (20) right After sorting: (twenty one) make ,have: (twenty two) Finally, through , the phase error can be estimated.

[0057] It should be noted that according to the terrain undulation error analysis, CSAR imaging needs to calculate the distance from the transmitting antenna to the target point. When the target scene has terrain undulation errors, the calculated distance will be biased, resulting in poor imaging quality. However, DEM data has the height information of the scene grid points, which is easy to combine with the BP imaging algorithm. Therefore, using DEM data to set the imaging scene grid can make the height of the imaging scene grid consistent with the height of the target, so that CSAR can project to the accurate image point when performing slant range projection, thereby reducing the impact of terrain height undulation errors and facilitating imaging processing and geometric correction.

[0058] For the motion error compensation of airborne CSAR platform, the existing methods usually perform self-focusing processing on the full-aperture CSAR echo signal. However, the imaging quality is not improved much, mainly because the imaging data quality of some sub-aperture angles is not high, which makes the sub-aperture imaging quality poor, resulting in low quality of the full-aperture image obtained by coherent accumulation. Therefore, it is necessary to perform self-focusing processing on the sub-aperture to ensure good sub-aperture imaging quality, thereby ensuring good quality of the full-aperture imaging obtained by coherent accumulation.

[0059] According to the above technical features, the present invention analyzes the influence of terrain undulation error on CSAR imaging, establishes a signal model of CSAR motion error, studies the self-focusing algorithm based on back projection, and analyzes the method of solving the motion error estimation value based on the sharpest image criterion. Then, a sub-aperture self-focusing CSAR high-precision imaging method combined with terrain undulation error compensation is proposed. The method first uses the observation scene DEM data to perform terrain undulation error compensation on the observation scene, and then uses the self-focusing algorithm combined with BP to focus the sub-aperture echo signal for imaging, and finally accumulates the focused sub-aperture image to obtain a high-precision full-aperture CSAR image, and the effectiveness of the method is verified by experimental data. The present invention can ensure that CSAR can achieve high-quality imaging in a complex environment. The obtained high-precision CSAR image can provide richer target feature information, which is helpful for high-precision identification, classification, positioning and tracking of different targets, and obtaining the position and motion trajectory of the target, thereby improving the image interpretation effect.

[0060] Example 2 Based on the above embodiment 1, combined with Figure 1-Figure 14 The present invention first analyzes the influence of terrain undulation error on CSAR imaging, establishes a signal model of CSAR motion error, studies the autofocus algorithm based on back projection, and analyzes the method of solving the motion error estimation value based on the sharpest image criterion. Then, a sub-aperture autofocus CSAR high-precision imaging method combined with terrain undulation error compensation is proposed. The detailed steps of the whole invention are explained below with reference to the accompanying drawings and examples.

[0061] In this embodiment, the following steps are specifically included: (1) Terrain undulation error compensation First, read the DEM data of the observation scene, and add the height information to the grid points in the imaging area, that is, the height error Compensation is performed to eliminate the height error. In the compensated imaging area grid, the echo signal of any point in the scene can be projected onto the correct imaging area grid, and then the projection results of different antenna phase centers are coherently superimposed to finally obtain the imaging result. In the BP algorithm, the echo signal of each CSAR antenna phase center needs to be projected onto the compensated imaging area grid according to the instantaneous slant range.

[0062] (2) Echo signal distance pulse compression The echo signal collected by CSAR is the historical phase data of the target scene. The echo signal is multiplied by the reference signal in the frequency domain and then converted into a time domain signal to complete the range pulse compression.

[0063] (3) Sub-aperture segmentation In order to perform subaperture self-focusing imaging later, the full circular aperture is divided into 120 non-overlapping subapertures, and the azimuth accumulation angle of each subaperture is 3°.

[0064] (4) Sub-aperture data imaging processing In order to achieve high resolution and low sidelobe in CSAR imaging, the BP algorithm is used to image the sub-aperture data after CSAR range pulse compression data. In addition, the Graphic Process Unit (GPU) is used for parallel processing to improve the imaging efficiency of the BP algorithm.

[0065] (5) Sub-aperture image self-focusing processing The coordinate descent geometry based on the sharpest image criterion is used to solve the autofocus algorithm, process each sub-aperture image, and estimate the phase error of each sub-aperture image. In order to improve the accuracy of the phase error estimation, multiple iterative estimates are generally required. In each iteration, the sub-aperture image is compensated according to the current error estimate, and the phase error is re-estimated based on the compensated sub-aperture image. After each iterative processing, the phase error will be smaller. Finally, the sub-aperture image is multiplied by the compensation factor of the phase error to achieve the autofocus processing of the sub-aperture image.

[0066] (6) Sub-aperture self-focusing image accumulation processing The images obtained by sub-aperture self-focusing are accumulated to obtain the final high-precision full-aperture CSAR image.

[0067] In order to verify the effectiveness of the sub-aperture self-focusing CSAR high-precision imaging method combined with terrain undulation error compensation proposed in the present invention, the effectiveness of the method is verified by point target simulation data, electromagnetic simulation data and measured data.

[0068] (1) Terrain error compensation based on point target simulation data The simulation experiment of point target imaging is carried out below. The parameter settings of the simulation system are shown in Table 1. It is easy to calculate that the CSAR beam downward observation angle is 45°.

[0069] Table 1 Point target simulation system parameters

[0070] There are 9 point targets with the same scattering coefficient and different heights in the observation scene, and the imaging plane height is 0m. Their two-dimensional positions and heights are shown in Table 2.

[0071] Table 2 Point target coordinates

[0072] First, when the point target has terrain ups and downs errors, the imaging quality is not high. Then, the DEM data of the target is read and the imaging plane grid is set so that the height of the imaging plane where the target is located is consistent with the actual height of the target, ensuring that the image point can be projected accurately during imaging. Figure 5 For imaging point targets with terrain undulation errors, Figure 6 This is the focused imaging result after terrain undulation error compensation using DEM data, where the colorbar unit is dB.

[0073] From the imaging results, it can be found that when there is a terrain undulation error, the target will be offset when projecting the image, so the imaging result appears as a ring. After compensating for the terrain undulation error, the BP imaging algorithm projects the point target to the correct position, making the point target well focused, the energy more concentrated, and the resolution significantly improved, which fully demonstrates the necessity and effectiveness of terrain undulation error correction measures.

[0074] In order to more intuitively compare the imaging results of point targets with height errors and those with height error compensation, Figure 5 and Figure 6 The profiles of the four point targets A, C, E, and I are compared. Figure 7 (a) Figure 7 (b) Figure 7 (c) and Figure 7 (d) shows the azimuth profiles when the height error of the point target is 14m, 18m, 10m, and 6m, respectively. At the same time, since the imaging of the ideal point target is centrally symmetric in the azimuth and range directions, the experimental results only compare the azimuth profiles.

[0075] like Figure 7 As shown in the figure, the magenta curve is the azimuth profile curve with height error, and the cyan curve is the azimuth profile curve after using DEM data to compensate for the height. Comparing the azimuth profile of the point target with height error and the compensated height error, it can be found that when the height error of the point target is larger, the point target defocus is more serious, the main lobe peak is lower, and the main lobe widening is more serious. When the height error is too large, the point target imaging will become a ring, and the azimuth profile will show a double peak.

[0076] After compensating for the height error of the point target, the point target can be refocused, the main lobe is significantly more concentrated, the side lobe is lower, and the 3dB width resolution is higher. It can be found that after compensating for the terrain height error, the focusing effect is good, which further confirms the effectiveness of taking terrain undulation compensation measures combined with DEM data to improve imaging quality.

[0077] (2) Compensation of vehicle target terrain fluctuation error based on electromagnetic simulation data Civilian Vehicle Domes (CVDomes) simulation data is electromagnetic simulation data of 10 civilian vehicles imaged using X-band electromagnetic waves in CSAR mode. The main parameters of CVDomes data are shown in Table 3, including 4 sets of simulation data with different pitch angles from 30° to 60°. The following terrain undulation error compensation experiment is conducted on the data of Toyota Avalon at a 30-degree bird's-eye view in the CVDome electromagnetic simulation data to verify the effectiveness of the terrain undulation error compensation method.

[0078] Table 3 Main parameters of CVDome simulation data

[0079] First, a terrain relief error of ±1m is introduced into the target scene. Figure 11-Figure 14 The BP algorithm imaging results with terrain undulation error and with terrain undulation error compensation method are shown in Figure 2. Figure 8 As shown in (a), when the imaging plane is 1m (higher than the actual plane of the target), there is a 1m height error. , the target's imaging projection shifts in the distance direction, and the vehicle's contour shrinks toward the center, forming a closed imaging contour with a smaller area. Figure 8 As shown in (b), when the imaging plane is -1m (lower than the actual plane of the target), there is a -1m height error. , the four sides of the vehicle contour are offset outward, and the car imaging contour will expand to form a larger closed imaging contour. Only when the imaging plane is the same as the actual plane of the target can accurate imaging results be obtained, which is consistent with the theoretical analysis of the target imaging results when there is terrain undulation error.

[0080] When there is a height error in the terrain undulation, the imaging position of the scattering points on the vehicle surface will be offset, and the superposition of scattering points at different heights on the imaging plane will cause the vehicle's imaging to overlap and the vehicle's imaging details to be seriously lost. Figure 8 (c) and Figure 8 In (d), the white box area is the image of the four wheel hubs. When there is terrain undulation error, the image of the wheel hub is blurred. Only after compensating for the height error can many details of the vehicle image be retained, which illustrates the necessity and effectiveness of terrain undulation error measures.

[0081] (3) Terrain undulation error compensation based on Gotcha measured data The following experiments are conducted on Gotcha measured data to verify the correctness and effectiveness of the terrain undulation error compensation method proposed in the present invention. The parameters of the Gotcha measured data system are shown in Table 4. The system works in the X-band.

[0082] Table 4 Gotcha measured data system parameters

[0083] A terrain undulation error of -1m is introduced into the target scene, and the imaging scene size is set to 120m×120m. The imaging results are as follows: Fig. 9 As shown. Fig. 9 It can be found that when the imaging plane is lower than the actual height of the vehicle, the target imaging projection is offset due to the terrain undulation error, and the vehicle target imaging contour expands, which is consistent with the theoretical analysis of the impact of terrain undulation error on the CSAR two-dimensional imaging quality.

[0084] Fig.10 This is the imaging result after using DEM data to compensate for terrain undulation errors. Fig. 9 and Fig.10 It can be found that when there is a terrain undulation error, the car imaging in the dense vehicle area overlaps and is difficult to distinguish. For targets with strong omnidirectional scattering characteristics, the incident wave can be evenly reflected in all directions (such as objects such as top hats), resulting in the inability to focus in a specific direction during imaging, thus forming a circular defocused image. For targets with strong reflective energy at specific angles, the energy is mainly reflected in specific directions (such as corner reflectors, etc.), so that its imaging results are mainly developed along these directions, forming an obvious arc-shaped imaging result.

[0085] After using DEM data to compensate for terrain undulation errors, the reflected echo of the vehicle target can be projected onto the accurate imaging plane grid, the vehicle target is better focused, the outline is more complete, and the overlap effect is improved, and the cars in dense vehicle areas are more clearly distinguished. Street lamps and four-sided corner reflectors are also well focused, and the outline of the top hat is more complete, proving the effectiveness of this method.

[0086] (4) Sub-aperture self-focusing imaging like Fig.11 The figure shows the flight trajectory of the airborne CSAR platform in the Gotcha dataset. It can be found that during the flight of the airborne CSAR, its altitude is not always on the same plane, and there is a large motion error. The difference between the highest and lowest flight altitudes is 20m, which affects the imaging quality. Therefore, it is necessary to use the autofocus algorithm for motion error compensation and imaging processing.

[0087] When performing self-focusing imaging on the sub-aperture data of the full-circle trajectory, phase angle data is required to participate in the calculation. This method first divides the full-circle data into 360 sub-apertures, that is, into sub-aperture data of every 1 degree, and then performs self-focusing imaging on every 3 1-degree sub-aperture data to obtain the coherent sub-aperture image of self-focusing imaging, and finally performs incoherent accumulation processing on the coherent sub-aperture image of self-focusing imaging to obtain a higher quality full-circle aperture imaging result.

[0088] Fig.12 The results of imaging using the BP algorithm directly on the sub-aperture data and the results of self-focusing imaging combined with the BP algorithm. Among them, (a) is the original BP algorithm imaging of the sub-aperture 30-60°, (b) is the self-focusing imaging of the sub-aperture 30-60°, (c) is the original BP algorithm imaging of the sub-aperture 150-180°, (d) is the self-focusing imaging of the sub-aperture 150-180°, (e) is the original BP algorithm imaging of the sub-aperture 300-330°, and (f) is the self-focusing imaging of the sub-aperture 300-330°. It can be found that due to the motion error of the airborne CSAR, the sub-aperture imaging will be defocused, and the imaging of the vehicle target will be deformed. The imaging of cars in dense vehicle areas overlaps with each other, making it difficult to distinguish adjacent vehicles, and the vehicle contours are not complete. After the sub-aperture imaging results are processed by self-focusing, the vehicle target focusing effect is better, the contours are more complete, and the imaging of cars in dense vehicle areas is easier to distinguish. The top hat and tetrahedral reflectors are also well focused.

[0089] In order to more intuitively compare the results of sub-aperture self-focusing imaging, Fig.13 The enlarged image and azimuth cross-section of the imaging results of the top hat and tetrahedral reflector in the upper right corner obtained by the two algorithms are given, including the sub-aperture imaging of the original BP algorithm and the sub-aperture self-focusing imaging combined with the BP algorithm. Fig.13 It can be found that the sub-aperture imaging without self-focusing processing has ghosting and double peaks, while after self-focusing processing, the ghosting and double peaks of the sub-aperture imaging are significantly improved, and the sub-aperture imaging quality is good, which shows the effectiveness of the self-focusing processing. Fig.13 (c), (f) and (i) are Fig.13 The azimuth profiles of the targets in the white boxes in (a), (b), (d), (e), (g), and (h). Fig.13(a) is an enlarged image of the original BP algorithm imaging of subaperture 30-60°, (b) is an enlarged image of the self-focusing imaging of subaperture 30-60°, (c) is a comparison of the azimuth profile of subaperture 30-60°, (d) is an enlarged image of the original BP algorithm imaging of subaperture 150-180°, (e) is an enlarged image of the self-focusing imaging of subaperture 150-180°, (f) is a comparison of the azimuth profile of subaperture 150-180°, (g) is an enlarged image of the original BP algorithm imaging of subaperture 300-330°, (h) is an enlarged image of the self-focusing imaging of subaperture 300-330°, and (i) is a comparison of the azimuth profile of subaperture 150-180°. It can be found that after the subaperture is self-focused, the main lobe peak value remains almost unchanged, the main lobe width becomes narrower, the side lobe level and side lobe peak value are also reduced, and the resolution is improved, which proves the effectiveness of the self-focusing method.

[0090] (5) Sub-aperture self-focusing CSAR high-precision imaging combined with terrain undulation error compensation Next, we will conduct a high-precision imaging experiment of sub-aperture self-focusing CSAR combined with terrain undulation error compensation. After terrain undulation error compensation, the BP algorithm is directly used to image the Gotcha measured data. The results are as follows: Fig.14 As shown in (a), it can be found that due to the uncompensated CSAR platform motion error, there are many imaging noise points, the vehicle target outline is not clear enough, and the car images in the dense vehicle area overlap with each other, making it difficult to distinguish adjacent vehicles. Therefore, further processing is required to obtain high-quality CSAR images.

[0091] This method first uses the DEM data of the Gotcha scene to set the imaging plane grid, performs terrain undulation error compensation processing, and then performs distance pulse compression on the echo signal, and then divides the full circular aperture into 360 non-overlapping sub-apertures. Since the phase data of sub-aperture imaging is required when performing the autofocus algorithm, this method uses the autofocus processing combined with the BP algorithm to perform sub-aperture coherent imaging to compensate for the motion error of CSAR. Since the incoherent image can filter out part of the coherent speckle noise and make the imaging result smoother, the present invention takes the amplitude value of the sub-aperture coherent image after the autofocus processing, and finally incoherently accumulates to obtain the full-aperture CSAR image, such as Fig.14 (b) as shown.

[0092] from Fig.14It can be found in (b) that the sub-aperture data is processed by self-focusing imaging, and then the sub-aperture images are accumulated to obtain the full-aperture image. The full-aperture CSAR image has good quality and a high signal-to-noise ratio. The vehicle target focusing effect is better, and the vehicle contours on the left and right sides of the vehicle are symmetrical, reflecting the characteristics of CSAR's omnidirectional observation. The vehicle contour is more complete, and the imaging results of cars in dense vehicle areas are easier to distinguish. The street lamps and four-hedral corner reflectors are also well focused, and the contours of the top hat are more complete, and the imaging of the top hat is closer to the standard circle, which proves the effectiveness of the method proposed in the present invention in obtaining high-precision CSAR images. Example 3 like Fig.15 As shown, the present invention also provides a sub-aperture self-focusing CSAR imaging system combined with terrain undulation error compensation. The system is used for the sub-aperture self-focusing CSAR imaging method combined with terrain undulation error compensation, and includes: a data processing module, a signal processing module, an error compensation module and an imaging module.

[0093] The data processing module reads the digital elevation model data of the observation scene, supplements the height information for the grid points in the imaging area, and eliminates the height error. The signal processing module transforms the echo signal collected by the CSAR into the frequency domain, multiplies it by the reference signal and converts it into the time domain signal to complete the range pulse compression. The error compensation module receives the data output by the data processing module and the signal processing module, divides the full circular aperture into a plurality of non-overlapping sub-apertures, uses the back-projection algorithm to image the sub-aperture data to obtain the sub-aperture image, and uses the self-focusing algorithm based on the sharpest image criterion to process each sub-aperture image for motion error compensation, estimates the phase error, and inputs it into the imaging module after multiple iterative estimations. The imaging module accumulates the sub-aperture images after the self-focusing processing to obtain a high-precision full-aperture CSAR image.

[0094] In the embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium, which, when executed, executes the steps of the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media capable of storing program codes.

[0095] Alternatively, if the above-mentioned embodiments of the present invention are implemented in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media capable of storing program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0096] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. The icons describing the structural positional relationships in the accompanying drawings are only used for exemplary purposes and are not to be construed as limiting the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and is not possible to list all the embodiments here. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the claims of the present invention.

Claims

1. A sub-aperture self-focusing CSAR imaging method combined with terrain relief error compensation, characterized in that: The following steps are involved: S1, read the digital elevation model data of the observation scene, supplement the height information of the grid points in the imaging area, and eliminate the height error; S2, transform the echo signal collected by CSAR into the frequency domain, multiply it by the reference signal and then convert it into the time domain signal to complete the range pulse compression; S3, dividing the full circumferential aperture into a plurality of non-overlapping sub-apertures, imaging the sub-aperture data using a back-projection algorithm, and obtaining a sub-aperture image; S4, using the autofocus algorithm based on the sharpest image criterion to process each sub-aperture image to perform motion error compensation, estimate the phase error, and perform multiple iterative estimates; S5. Accumulate the sub-aperture images after the self-focusing process to obtain a high-precision full-aperture CSAR image.

2. The sub-aperture self-focusing CSAR imaging method combined with terrain relief error compensation according to claim 1, characterized in that: In step S1, the height information is added to the grid points in the imaging area, and the calculation process to eliminate the height error is as follows: according to the position of the point target Height difference from the imaging area plane , and slow time Time point target To the radar antenna phase center Slope distance , calculate the error in the projection position of the point target on the imaging plane caused by the terrain undulation error, and then calculate the distance displacement caused by the terrain undulation according to the positional relationship between the point target and the imaging area plane, so as to compensate for the terrain undulation error.

3. The sub-aperture self-focusing CSAR imaging method combined with terrain relief error compensation according to claim 2, characterized in that: The calculation formula of step S1 is: in, Indicates point target Image point, and point target With the same slope distance, i.e. point target The position in the CSAR image after imaging, Indicates the ground distance of CSAR in the imaging plane; if the point target There is an error between the height and the height of the imaging plane , then the point target The projected ground distance in the imaging plane can be calculated using the following geometric relationship: The error of the projection position of the point target on the imaging plane caused by the terrain undulation error is expressed as: If the target is above the imaging plane, the imaging position of the target in the CSAR image will move toward the direction close to the CSAR; if the target is below the imaging plane, the imaging position of the target will move away from the CSAR; if the actual height of the target is consistent with the height of the imaging plane, there is no deviation in the imaging position of the target.

4. The sub-aperture self-focusing CSAR imaging method combined with terrain relief error compensation according to claim 3, characterized in that: The distance displacement caused by terrain undulation is calculated based on the positional relationship between the point target and the imaging area plane as follows: first, the point target is calculated The beam incident angle , and correct the error of the target projection position on the imaging plane caused by the terrain undulation error, and jointly calculate the distance displacement caused by the terrain undulation , the formula is as follows: in, Indicates the CSAR antenna's focus on the center point during imaging For an incident angle of 10°, the range displacement caused by terrain undulation is proportional to the terrain undulation error, and the closer the point target is to the projection point of the CSAR on the imaging plane, the greater the range displacement caused by the terrain undulation.

5. The sub-aperture self-focusing CSAR imaging method combined with terrain relief error compensation according to claim 2, characterized in that: In step S2, the echo signal collected by CSAR is transformed into the frequency domain, multiplied by the reference signal and converted into a time domain signal. The specific process of completing the range pulse compression is as follows: Calculate the CSAR first The position vector of the antenna phase center at the azimuth sampling position Position vector to point target One-way slope distance According to the influence of motion error on the quality of CSAR two-dimensional imaging, the echo signal received by the antenna is orthogonally demodulated and range-direction pulse compressed.

6. The sub-aperture self-focusing CSAR imaging method combined with terrain relief error compensation according to claim 5, characterized in that: The step S2 includes the following expression: Among them, The position vector of the antenna phase center at the azimuth sampling position , the position vector of the point target , Represents the echo signal after orthogonal demodulation and range pulse compression processing.

7. The sub-aperture self-focusing CSAR imaging method combined with terrain relief error compensation according to claim 6, characterized in that: The step S3 is specifically as follows: the full circumferential aperture is divided into 120 non-overlapping sub-apertures, the azimuth accumulation angle of each sub-aperture is 3°, and the sub-aperture data after the CSAR range pulse compression data is imaged by a back-projection algorithm. The formula of the back-projection algorithm is as follows: in, Represents the position vector of any point in the imaging plane The imaging results at is the total number of CSAR azimuth sampling points, Indicates The projection vector of the echo signal at the sampling position to all grid points in the imaging scene is located at a certain point in the plane. The back projection value at In Chinese ; The sensor measures the The phase center coordinates of the CSAR are , Indicates CSAR phase center measurement position Grid points on the imaging plane The slope distance between Indicates CSAR phase center position Grid points on the imaging plane The slope distance between Indicates The phase error caused by the CSAR phase center measurement error is express There is a phase shift, represents the estimated value of the phase error, It means that the projection vectors of all grid points in the imaging scene are accumulated to obtain a high-precision image that achieves phase error compensation.

8. The sub-aperture self-focusing CSAR imaging method combined with terrain relief error compensation according to claim 7, characterized in that: The specific process of step S4 is: using the coordinate descent geometry solution autofocus algorithm based on the sharpest image criterion to process each sub-aperture image, and estimate the phase error of each sub-aperture image. In each iteration, the sub-aperture image will be compensated according to the current error estimation value, and the phase error will be re-estimated based on the compensated sub-aperture image. After each iterative processing, the phase error will be smaller. Finally, the sub-aperture image is multiplied by the compensation factor of the phase error to achieve autofocus processing of the sub-aperture image.

9. The sub-aperture self-focusing CSAR imaging method combined with terrain relief error compensation according to claim 8, characterized in that: The specific calculation process of estimating the phase error of the sub-aperture image and performing multiple iterative estimations is as follows: First, evaluate whether the phase error satisfies the model. This assumption is then used to estimate the phase error using the sharpest image criterion, that is, to find the optimal solution to the following formula: in is a function for evaluating the sharpness of an image, namely, the energy evaluation function. The larger the value, the sharper the image, that is, the more obvious the target features in the image are. The coordinate descent method is used to iteratively optimize and estimate it. At this time, the energy evaluation function is the objective function used to measure image quality. After iterations, a set of estimates is obtained. , then in the next iteration The estimation of the parameters It will be calculated by the following formula: Among them, The imaging result after the estimation iteration is: Find ,and: Let the fixed constant part be , which contains the parameters to be estimated. , then: right After sorting: make ,have: Finally, through , the phase error can be estimated.

10. A sub-aperture self-focusing CSAR imaging system combined with terrain relief error compensation, the system being used in a sub-aperture self-focusing CSAR imaging method combined with terrain relief error compensation as claimed in any one of claims 1 to 9, characterized in that: It includes: a data processing module, a signal processing module, an error compensation module and an imaging module; The data processing module reads the digital elevation model data of the observation scene, supplements the height information for the grid points in the imaging area, and eliminates the height error. The signal processing module transforms the echo signal collected by the CSAR into the frequency domain, multiplies it by the reference signal and converts it into the time domain signal to complete the range pulse compression. The error compensation module receives the data output by the data processing module and the signal processing module, divides the full circular aperture into a plurality of non-overlapping sub-apertures, uses the back-projection algorithm to image the sub-aperture data to obtain the sub-aperture image, and uses the self-focusing algorithm based on the sharpest image criterion to process each sub-aperture image for motion error compensation, estimates the phase error, and inputs it into the imaging module after multiple iterative estimations. The imaging module accumulates the sub-aperture images after the self-focusing processing to obtain a high-precision full-aperture CSAR image.

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