Master-slave star group cooperative sar-gmti system and method

Abstract

The application provides a master-slave type star cluster cooperative SAR-GMTI system and method, which comprises: an azimuth multi-channel master star M1, a small satellite cluster M2 along a heading direction, and an on-board joint processing module M3; the azimuth multi-channel master star M1 is used for transmitting radar signals in a full array and receiving radar signals in an azimuth direction; the small satellite cluster M2 along the heading direction is composed of at least two small satellites in a receiving mode only, and the small satellite cluster is linearly distributed along the heading direction; and the on-board joint processing module M3 is used for fusing SAR image data of the azimuth multi-channel master star M1 and the small satellite cluster M2 along the heading direction, and completing target detection and relocation through space-time adaptive processing. The application effectively solves problems such as a large minimum detectable speed of a single platform system and speed ambiguity of a distributed system, and provides important technical support for subsequent spaceborne SAR earth imaging observation.

Description

Technical Field

[0001] This invention relates to the field of aerospace remote sensing technology, specifically to a master-slave constellation collaborative SAR-GMTI system and method. Background Technology

[0002] Currently, spaceborne SAR systems face three major technical bottlenecks in slow target detection and adaptability to complex terrain: (1) Limited minimum detectable velocity (MDV): Traditional single-satellite systems (such as Sentinel-1) have an MDV > 2 m / s, making them unable to detect congested vehicles in urban areas (0.1-1 m / s); (2) Terrain incoherence: Insufficient phase compensation in steep slope areas leads to false alarms; (3) Insufficient multi-source data fusion: Low efficiency of cross-platform data collaboration and poor velocity estimation accuracy. Therefore, it is necessary to study new moving target detection systems and methods to improve the performance of moving target detection.

[0003] Patent CN118425941B discloses a distributed satellite cooperative flexible multi-baseline GMTI experimental device, which is used to deploy on various satellites in close formation to improve detection performance. However, this patent does not address satellite-only design, terrain phase compensation, and unambiguous estimation of target motion velocity, making it difficult to guarantee the display performance of moving land targets.

[0004] Patent CN108020835B discloses a method for suppressing strong clutter in a spaceborne synthetic aperture radar (SAR) ground moving target indication (GMTI). However, this patent only considers clutter suppression methods under a single platform and does not yet consider clutter suppression methods under a distributed system.

[0005] Patent CN104076343B discloses a spaceborne three-channel SAR-GMTI adaptive clutter suppression method. However, this patent only considers clutter suppression methods for three-channel systems on a single platform and does not yet consider clutter suppression in distributed multi-satellite multi-channel systems.

[0006] Patent CN118746833A discloses a multi-channel SAR-GMTI method based on improved robust principal component analysis, which solves the shortcomings of traditional channel equalization registration algorithms in handling local errors and has better target detection capabilities. However, this patent only considers clutter suppression methods under a single platform and has not yet considered clutter suppression under a distributed system.

[0007] Patent CN118746833A discloses an improved SAR GMTI moving target relocation method. This method relocates the moving target on a processed multi-channel SAR image based on its radial velocity, azimuth velocity, detection position, yaw angle compensation residue, and terrain elevation map, achieving improved positioning accuracy. However, this patent only addresses target relocation on a single platform and does not consider distributed systems.

[0008] In summary, current research on spaceborne SAR for displaying moving land targets mainly adopts a single-platform approach. The problem of severe velocity ambiguity has not yet been solved in the research on distributed platforms, and there is a lack of research on multi-satellite and multi-channel joint methods for displaying moving land targets. This will lead to problems such as large minimum detectable velocity of targets and severe velocity ambiguity.

[0009] In summary, given the problems of the existing technologies, researching a master-slave constellation cooperative SAR-GMTI system and method has become a critical task that urgently needs to be addressed. Summary of the Invention

[0010] To address the shortcomings of existing technologies, the purpose of this invention is to provide a master-slave constellation cooperative SAR-GMTI system and method.

[0011] The master-slave constellation cooperative SAR-GMTI system provided by the present invention includes: an azimuth multi-channel master satellite M1, a heading-side small satellite constellation M2, and an onboard joint processing module M3; the azimuth multi-channel master satellite M1 is used to transmit radar signals across the entire array and receive radar signals in the azimuth multi-channel configuration; the heading-side small satellite constellation M2 consists of at least two small satellites in receive-only mode, and the small satellite constellation is linearly distributed along the heading; the onboard joint processing module M3 is used to fuse the SAR image data of the azimuth multi-channel master satellite M1 and the heading-side small satellite constellation M2, and complete target detection and relocation through space-time adaptive processing.

[0012] Preferably, the heading-side small satellite group M2 establishes a time, phase, and spatial synchronization link with the azimuth multi-channel main satellite M1 for high-coherence reception of echo signals; the heading-side small satellite group M2 is used to transmit the original echo signals to the azimuth multi-channel main satellite M1 for multi-satellite data joint coherent processing.

[0013] Preferably, the on-board joint processing module M3 includes: a multi-satellite independent imaging processing module M3.1, an image registration and interferometric phase compensation module M3.2, an image domain spatiotemporal adaptive processing module M3.3, a constant false alarm rate detection and target extraction module M3.4, a moving target parameter estimation module M3.5, and a target relocation and geocoding module M3.6.

[0014] The multi-satellite independent imaging processing module M3.1 is used to process the raw echo data of the primary satellite and the small satellite constellation into high-resolution SAR images, wherein the primary satellite generates high-resolution SAR images from the echo data. The module M3.1 is used to retain the complex image data of each channel of the primary star. , For the number of channels, These are the azimuth coordinates. For distance coordinates; The image registration and interferometric phase compensation module M3.2 is used to register all SAR images with the first azimuth channel image of the primary satellite and compensate for the interferometric phase between the small satellite and the primary satellite. The image domain spatiotemporal adaptive processing module M3.3 is used to construct a spatiotemporal adaptive processor, calculate and output the image after clutter cancellation. ; The constant false alarm rate (CFAR) detection and target extraction module M3.4 is used to estimate the background power of the image output from the spatiotemporal adaptive processing by segmentation, and to extract the target using ordered statistical CFAR detection, wherein the detection threshold is:

[0015] in, For background power, The threshold coefficient is determined by the detection rate and the false alarm rate; The moving target parameter estimation module M3.5 is used to jointly register the SAR images and calculate the target's radial velocity. :

[0016] in, and For the complex values ​​of the SAR image, As the time baseline, Indicates the primary star and satellite numbers, Indicates the channel number. Indicates the orientation pixel position. Indicates the distance to the pixel position. Radial velocity, λ is the wavelength.

[0017] The target relocation and geocoding module M3.6 is used to convert the pixel coordinates of the target image into real geographic coordinates.

[0018] Preferably, the image registration and interferometric phase compensation module M3.2 is used to introduce external high-precision ground elevation information and perform radar geometric compensation of the vertical baseline between the small satellite and the main satellite. The introduced interference phase; module M3.2 is used to locate each pixel in the main image based on the range-Doppler equation and external high-precision ground elevation information, thereby obtaining the spatial position of the pixel. The module M3.2 is used to calculate the compensated interference phase.

[0019] in, It is the spatial position vector of the pixel. for Time of the first The spatial position of a small satellite for The spatial position of the phase center of the primary satellite antenna at any given moment. The time is the pixel at the th The corresponding azimuth time on each auxiliary image For radar wavelength, This is the radar slant range.

[0020] Preferably, the image domain spatiotemporal adaptive processing module M3.3 is used to arrange the registered multi-satellite images into a three-dimensional matrix according to channels and pulses. ,in, Number of main star channels The number of small satellites along the heading. This represents the number of pixels in the orientation. The distance is the number of pixels; module M3.3 is used to select a static scene region as a training sample and estimate the clutter covariance matrix:

[0021] in, For the training sample set, For the sample size, A three-dimensional matrix Chinese position ,distance Corresponding column vectors for The conjugate transpose of the ; module M3.3 is used to calculate the optimal weights based on the minimum variance criterion:

[0022] in, The target motion guide vector, for The conjugate transpose of . Here is the clutter covariance matrix. for The inverse matrix; module M3.3 is used to calculate the output image after clutter cancellation. :

[0023] in, for The conjugate transpose of . A three-dimensional matrix Chinese position ,distance Corresponding column vector.

[0024] This invention also provides a master-slave constellation cooperative SAR-GMTI method, comprising: Step S1: Radar signals are transmitted across the entire array of the azimuth multi-channel main satellite, and the main satellite and the small satellite constellation along the course receive the echo signals; Step S2: The echo signal is processed by imaging to obtain SAR images of each channel of the main star and the small satellite constellation; Step S3: Register each SAR image with the image of the first azimuth channel of the primary satellite, and compensate for the interference phase between the small satellite and the primary satellite; Step S4: Construct a space-time adaptive processor to calculate and output the image after clutter cancellation. ; Step S5, for the image Background power is estimated in blocks, and ordered statistical constant false alarm rate detection is used to extract the position of the target in the image; Step S6: Calculate the target radial velocity using the jointly registered SAR images; Step S7: Based on the radial velocity of the target, the target's trajectory is compensated in reverse to reposition the target position and the target pixel coordinates are converted into latitude and longitude coordinates.

[0025] Preferably, the step of registering each SAR image with the image of the first azimuth channel of the primary satellite and compensating for the interferometric phase between the small satellite and the primary satellite includes: By introducing external high-precision ground elevation information, and locating each pixel in the main image based on the range-Doppler equation and the external high-precision ground elevation information, the spatial position of each pixel is obtained. ; Based on the spatial position of the pixel Calculate the compensated interference phase:

[0026] in, It is the spatial position vector of the pixel. for Time of the first The spatial position of a small satellite for The spatial position of the phase center of the primary satellite antenna at any given moment. The time is the pixel at the th The corresponding azimuth time on each auxiliary image For radar wavelength, Radar slant range; The vertical baseline between the small satellite and the main satellite is compensated by radar geometry. The introduced interference phase.

[0027] Preferably, the spatiotemporal adaptive processor is constructed to calculate and output the image after clutter cancellation. This includes arranging the registered multi-satellite images into a three-dimensional matrix according to channels and pulses. ,in, Number of main star channels The number of small satellites along the heading. This represents the number of pixels in the orientation. The distance is the number of pixels; a static scene region is selected as the training sample, and the clutter covariance matrix is ​​estimated:

[0028] in, For the training sample set, For the sample size, A three-dimensional matrix Chinese position ,distance Corresponding column vectors for The conjugate transpose of the clutter covariance matrix; and the optimal weights are calculated based on the minimum variance criterion.

[0029] in, The target motion guide vector, for The conjugate transpose of . Here is the clutter covariance matrix. for The inverse matrix; based on the optimal weights, calculate the output image after clutter cancellation. :

[0030] in, for The conjugate transpose of . A three-dimensional matrix Chinese position ,distance Corresponding column vector.

[0031] Preferably, the image Block-based estimation of background power and use of ordered statistical constant false alarm rate detection to extract the target's position in the image, including: The method employs ordered statistical constant false alarm rate (CFAR) detection, where the detection threshold is:

[0032] in, For background power, The threshold coefficient is determined by the detection rate and the false alarm rate.

[0033] Preferably, the calculation of the target radial velocity from the jointly registered SAR image includes: Jointly registered SAR images and calculate target radial velocity :

[0034] in, and For the complex values ​​of the SAR image, The time baseline is defined by m, which represents the primary satellite and small satellite number, n, which represents the channel number, j, which represents the azimuth pixel position, and k, which represents the range pixel position. Radial velocity, λ is the wavelength.

[0035] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention proposes a collaborative architecture of "azimuth multi-channel main star + small satellite constellation along the heading". The small satellites are connected by a sufficiently long baseline, which makes the system extremely sensitive to the phase difference of moving targets, significantly reduces the minimum detectable speed, improves the detection capability of slow targets and suppresses velocity ambiguity.

[0036] 2. This invention achieves multi-star joint spatiotemporal adaptive processing in the image domain, avoiding the massive data transmission and complex calculations required for signal domain processing.

[0037] 3. This invention introduces external high-precision ground elevation information for vertical baseline phase compensation, which improves the compensation accuracy under complex terrain.

[0038] 4. This invention proposes a joint velocity estimation formula for cross-platform multi-source images, which comprehensively utilizes multi-channel images of the main satellite and small satellite constellation images to achieve optimal fusion estimation of the target velocity vector. Attached Figure Description

[0039] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1This is a schematic diagram of a master-slave constellation cooperative SAR-GMTI system provided in an embodiment of the present invention.

[0040] Figure 2 The flowchart of image domain spatiotemporal adaptive processing provided in the embodiments of the present invention is shown.

[0041] Figure 3 The output signal-to-noise ratio (SNR) of the multi-channel system of the primary satellite as a function of velocity is shown in the simulation scenario.

[0042] Figure 4 It is the signal-to-noise ratio (SNR) of the distributed formation system output as a function of velocity in a simulated scenario.

[0043] Figure 5 It is the output signal-to-noise ratio (SNR) versus speed response curve under the new system in a simulation scenario. Detailed Implementation

[0044] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0045] Figure 1 A schematic diagram of a master-slave satellite cluster collaborative SAR-GMTI system provided for an embodiment of the present invention includes: an azimuth multi-channel master satellite M1, a small satellite cluster along the heading M2, and an on-board joint processing module M3; Specifically, the azimuth multi-channel main satellite M1 is used to transmit radar signals across the entire array and receive radar signals in the azimuth multi-channel configuration; the heading-side small satellite constellation M2 consists of at least two small satellites in receive-only mode, and the small satellite constellation is linearly distributed along the heading; the onboard joint processing module M3 is used to fuse the SAR image data of the azimuth multi-channel main satellite M1 and the heading-side small satellite constellation M2, and complete target detection and relocation through space-time adaptive processing.

[0046] Furthermore, the small satellite constellation M2 along the course establishes a time, phase, and spatial synchronization link with the azimuth multi-channel primary satellite M1 for highly coherent reception of echo signals; M2 is used to transmit the raw echo signals to the azimuth multi-channel primary satellite M1 for joint coherent processing of multi-satellite data. The on-board joint processing module M3 includes: a multi-satellite independent imaging processing module M3.1, an image registration and interferometric phase compensation module M3.2, an image domain spatiotemporal adaptive processing module M3.3, a constant false alarm rate (CFAR) detection and target extraction module M3.4, a moving target parameter estimation module M3.5, and a target relocation and geocoding module M3.6. The multi-satellite independent imaging processing module M3.1 processes the raw echo data of the primary satellite and the small satellite constellation into high-resolution SAR images. The image registration and interferometric phase compensation module M3.2 registers all SAR images with the image of the first azimuth channel of the primary satellite and compensates for the interferometric phase between the small satellites and the primary satellite. The image domain space-time adaptive processing module M3.3 constructs a space-time adaptive processor to calculate and output the image after clutter cancellation. The constant false alarm rate (CFAR) detection and target extraction module M3.4 is used to estimate the background power of the image output from the spatiotemporal adaptive processing and to extract the target using ordered statistical CFAR detection; the moving target parameter estimation module M3.5 is used to jointly register the SAR image and calculate the radial velocity of the target; the target relocation and geocoding module M3.6 is used to convert the pixel coordinates of the target image into the real geographic coordinates.

[0047] Figure 2 The image domain spatiotemporal adaptive processing flowchart provided in this embodiment of the invention includes the following steps: Step 1: Collaborative acquisition of multi-source data.

[0048] Specifically, in this embodiment, the satellite orbit parameters are a sun-synchronous orbit with an altitude of 514 km and an inclination of 97.4°. The primary satellite, equipped with an X-band eight-channel SAR antenna (16 m long), is located at the center of the formation and is responsible for transmitting signals and primary reception. Four small satellites are distributed along the flight path, two in front of the primary satellite and two behind it. The baseline lengths of adjacent satellites are 50 m, 100 m, 150 m, and 200 m, respectively, forming a 500-meter baseline along the flight path. The primary satellite transmits linear frequency modulated (LFM) signals for Earth observation; the primary small satellites synchronously receive and image process the signals to obtain 12 SAR images, each with a resolution of 3 meters and a noise equivalent backscattering coefficient (NESZ) better than -13 dB.

[0049] Step 2: Image registration and interferometric phase correction.

[0050] First, geometric and coherent correlation methods were used to register each SAR image with the image of the first azimuth channel of the main satellite, achieving sub-pixel accuracy. Then, high-precision external ground elevation information (DEM) was introduced through methods such as lidar and microwave mapping. Radar geometric inversion interferometric phase compensation was used to determine the vertical baseline between the small satellite and the main satellite. The introduced interference phase specifically includes: The spatial location of each pixel in the main image is obtained by locating the pixels based on the distance-Doppler equation and the external DEM. ; Calculate the compensated interference phase:

[0051] In the formula, It is the spatial position vector of the pixel. for The spatial position of the j-th small satellite at time j. for The spatial position of the phase center of the primary satellite antenna at any given moment. The time is the orientation time of the pixel on the j-th auxiliary image. For radar wavelength, This is the radar slant range.

[0052] Step 3: Automated spatiotemporal processing of the image domain.

[0053] In this step, the registered multi-star images are first arranged into a three-dimensional matrix according to channels and pulses. ,in, Number of main star channels The number of small satellites along the heading. This represents the number of pixels in the orientation. This represents the distance in pixels.

[0054] Then, a static scene region is selected as the training sample to estimate the clutter covariance matrix:

[0055] in, For the training sample set, For the sample size, A three-dimensional matrix The column vectors corresponding to the position x and distance r in the Chinese region are as follows: for The conjugate transpose of .

[0056] Next, the optimal weights are calculated based on the minimum variance criterion:

[0057] in, The target motion guide vector, for The conjugate transpose of . Here is the clutter covariance matrix. for The inverse matrix.

[0058] Finally, the image after clutter cancellation is calculated and output. :

[0059] in, This is the optimal weight vector output. for The conjugate transpose of . A three-dimensional matrix The column vectors corresponding to the position x and distance r in the center.

[0060] Step 4: Constant False Alarm Rate (CFAR) and Target Extraction.

[0061] The image output from the above steps Background power is estimated in blocks, and ordered statistical constant false alarm rate (OS-CFAR) detection is used. Specifically, the detection threshold is:

[0062] In the formula, For background power, The threshold coefficient is determined by the detection rate and the false alarm rate.

[0063] Step 5: Estimation of moving target parameters.

[0064] Combine multi-channel images of the primary satellite and images of small satellites along the heading to calculate the target's radial velocity. :

[0065] in, and For the complex values ​​of the SAR image, The time baseline is defined by m, which represents the primary satellite and small satellite number, n, which represents the channel number, j, which represents the azimuth pixel position, and k, which represents the range pixel position. Radial velocity, λ is the wavelength.

[0066] Step Six: Target Relocation and Geocoding.

[0067] Based on the estimated velocity, the target's trajectory is compensated in reverse, and combined with satellite orbital parameters and DEM data, the target pixel coordinates (x, r) are converted into latitude and longitude. .

[0068] This invention also provides a background with an incident angle of 40° and a bare ground surface. In this simulation scenario, the signal-to-clutter ratio (SCR) of the primary satellite's multi-channel system output as a function of velocity is shown in the following figure. Figure 3 As shown, the signal-to-noise ratio (SNR) of the distributed formation system output as a function of velocity is as follows: Figure 4 As shown, the output signal-to-noise ratio (SNR) versus speed response curve under the new system is as follows: Figure 5 As shown, this invention can effectively solve problems such as large minimum detectable speed on a single platform and severe speed ambiguity in distributed systems, and can provide important technical support for the design of subsequent spaceborne SAR-GMTI systems.

[0069] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0070] In the description of this application, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0071] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A master-slave constellation cooperative SAR-GMTI system, characterized in that, include: Azimuth multi-channel primary satellite M1, heading-side small satellite constellation M2, on-board joint processing module M3; The azimuth multi-channel main satellite M1 is used for transmitting radar signals across the entire array and receiving radar signals in the azimuth multi-channel configuration. The heading-side small satellite constellation M2 consists of at least two small satellites in receive-only mode, and the small satellite constellation is linearly distributed along the heading. The on-board joint processing module M3 is used to fuse the SAR image data of the azimuth multi-channel main satellite M1 and the small satellite group M2 along the course, and complete the target detection and relocation through space-time adaptive processing.

2. The master-slave constellation cooperative SAR-GMTI system according to claim 1, characterized in that, The heading-side small satellite group M2 establishes a time, phase, and spatial synchronization link with the azimuth multi-channel main satellite M1 for high-coherence reception of echo signals; The heading-side small satellite group M2 is used to transmit the original echo signal to the azimuth multi-channel master satellite M1 for multi-satellite data joint coherent processing.

3. The master-slave constellation cooperative SAR-GMTI system according to claim 1, characterized in that, The on-board joint processing module M3 includes: a multi-satellite independent imaging processing module M3.1, an image registration and interferometric phase compensation module M3.2, an image domain spatiotemporal adaptive processing module M3.3, a constant false alarm rate detection and target extraction module M3.4, a moving target parameter estimation module M3.5, and a target relocation and geocoding module M3.

6. The multi-satellite independent imaging processing module M3.1 is used to process the raw echo data of the primary satellite and the small satellite constellation into high-resolution SAR images, wherein the primary satellite generates high-resolution SAR images from the echo data. The module M3.1 is used to retain the complex image data of each channel of the primary star. , For the number of channels, These are the azimuth coordinates. For distance coordinates; The image registration and interferometric phase compensation module M3.2 is used to register all SAR images with the first azimuth channel image of the primary satellite and compensate for the interferometric phase between the small satellite and the primary satellite. The image domain spatiotemporal adaptive processing module M3.3 is used to construct a spatiotemporal adaptive processor, calculate and output the image after clutter cancellation. ; The constant false alarm rate (CFAR) detection and target extraction module M3.4 is used to estimate the background power of the image output from the spatiotemporal adaptive processing by segmentation, and to extract the target using ordered statistical CFAR detection, wherein the detection threshold is: in, For background power, The threshold coefficient is determined by the detection rate and the false alarm rate; The moving target parameter estimation module M3.5 is used to jointly register the SAR images and calculate the target's radial velocity. : in, and For the complex values ​​of the SAR image, The time baseline is defined by m, which represents the primary satellite and small satellite number, n, which represents the channel number, j, which represents the azimuth pixel position, and k, which represents the range pixel position. Radial velocity, λ is the wavelength. The target relocation and geocoding module M3.6 is used to convert the pixel coordinates of the target image into real geographic coordinates.

4. A master-slave constellation cooperative SAR-GMTI system according to claim 3, characterized in that, The image registration and interferometric phase compensation module M3.2 is used to introduce external high-precision ground elevation information and compensate the vertical baseline between the small satellite and the main satellite through radar geometry. Introduced interference phase; The module M3.2 is used to locate each pixel in the main image based on the distance-Doppler equation and external high-precision ground elevation information, thereby obtaining the spatial position of the pixel. ; Module M3.2 is used to calculate the compensated interference phase: in, It is the spatial position vector of the pixel. for Time of the first The spatial position of a small satellite for The spatial position of the phase center of the primary satellite antenna at any given moment. The time is the pixel at the th The corresponding azimuth time on each auxiliary image For radar wavelength, This is the radar slant range.

5. A master-slave constellation cooperative SAR-GMTI system according to claim 3, characterized in that, The image domain spatiotemporal adaptive processing module M3.3 is used to arrange the registered multi-satellite images into a three-dimensional matrix according to channels and pulses. ,in, Number of main star channels The number of small satellites along the heading. This represents the number of pixels in the orientation. The distance is in pixels; Module M3.3 is used to select a static scene region as a training sample and estimate the clutter covariance matrix: in, For the training sample set, For the sample size, A three-dimensional matrix Chinese position ,distance Corresponding column vectors, for The conjugate transpose of; Module M3.3 is used to calculate the optimal weights based on the minimum variance criterion. in, The target motion guide vector, for The conjugate transpose of . Here is the clutter covariance matrix. for The inverse matrix; The module M3.3 is used to calculate and output the image after clutter cancellation. : in, for The conjugate transpose of . A three-dimensional matrix Chinese position ,distance Corresponding column vector.

6. A master-slave constellation cooperative SAR-GMTI method, based on the master-slave constellation cooperative SAR-GMTI system according to any one of claims 1-5, characterized in that, include: Step S1: Radar signals are transmitted across the entire array of the azimuth multi-channel main satellite, and the main satellite and the small satellite constellation along the course receive the echo signals; Step S2: The echo signal is processed by imaging to obtain SAR images of each channel of the main star and the small satellite constellation; Step S3: Register each SAR image with the image of the first azimuth channel of the primary satellite, and compensate for the interference phase between the small satellite and the primary satellite; Step S4: Construct a space-time adaptive processor to calculate and output the image after clutter cancellation. ; Step S5, for the image Background power is estimated in blocks, and ordered statistical constant false alarm rate detection is used to extract the position of the target in the image; Step S6: Calculate the target radial velocity using the jointly registered SAR images; Step S7: Based on the radial velocity of the target, the target's trajectory is compensated in reverse to reposition the target position and the target pixel coordinates are converted into latitude and longitude coordinates.

7. The master-slave constellation cooperative SAR-GMTI method according to claim 6, characterized in that, The process of registering each SAR image with the image of the first azimuth channel of the primary satellite and compensating for the interferometric phase between the small satellite and the primary satellite includes: By introducing external high-precision ground elevation information, and locating each pixel in the main image based on the range-Doppler equation and the external high-precision ground elevation information, the spatial position of each pixel is obtained. ; Based on the spatial position of the pixel Calculate the compensated interference phase: in, It is the spatial position vector of the pixel. for Time of the first The spatial position of a small satellite for The spatial position of the phase center of the primary satellite antenna at any given moment. The time is the pixel at the th The corresponding azimuth time on each auxiliary image For radar wavelength, Radar slant range; The vertical baseline between the small satellite and the main satellite is compensated by radar geometry. The introduced interference phase.

8. The master-slave constellation cooperative SAR-GMTI method according to claim 6, characterized in that, The constructed space-time adaptive processor calculates and outputs the image after clutter cancellation. ,include: The registered multi-star images are arranged into a three-dimensional matrix according to channels and pulses. ,in, Number of main star channels The number of small satellites along the heading. This represents the number of pixels in the orientation. The distance is in pixels; Selecting a static scene region as the training sample, the clutter covariance matrix is ​​estimated: in, For the training sample set, For the sample size, A three-dimensional matrix Chinese position ,distance Corresponding column vectors for The conjugate transpose of; Based on the clutter covariance matrix, the optimal weights are calculated using the minimum variance criterion: in, The target motion guide vector, for The conjugate transpose of . Here is the clutter covariance matrix. for The inverse matrix; Based on the optimal weights, calculate the output image after clutter cancellation. : in, for The conjugate transpose of . A three-dimensional matrix Chinese position ,distance Corresponding column vector.

9. The master-slave constellation cooperative SAR-GMTI method according to claim 6, characterized in that, The image Block-based estimation of background power and use of ordered statistical constant false alarm rate detection to extract the target's position in the image, including: The method employs ordered statistical constant false alarm rate (CFAR) detection, where the detection threshold is: in, For background power, The threshold coefficient is determined by the detection rate and the false alarm rate.

10. The master-slave constellation cooperative SAR-GMTI method according to claim 6, characterized in that, The target radial velocity is calculated from the jointly registered SAR image, including: Jointly registered SAR images and calculate target radial velocity : in, and For the complex values ​​of the SAR image, The time baseline is defined by m, which represents the primary satellite and small satellite number, n, which represents the channel number, j, which represents the azimuth pixel position, and k, which represents the range pixel position. Radial velocity, λ is the wavelength.

Images