Three-dimensional motion velocity inversion method and system based on fusion of sar and ins

Abstract

The application discloses a kind of three-dimensional motion velocity inversion method and system based on SAR and INS fusion, belong to SAR auxiliary navigation, unmanned aerial vehicle autonomous navigation and SAR motion compensation technical field.The method comprises: collecting triangular frequency-modulated continuous wave SAR echo and INS data;Obtain the slant range and radial velocity observation of target by using up and down sweep frequency beat decoupling, and convert into the constraint to platform three-dimensional motion velocity;Establish SAR / INS tight coupling state estimation model, fuse SAR observation and INS information and invert platform three-dimensional motion velocity;Utilize inversion velocity to correct platform trajectory and execute SAR motion compensation and refocusing imaging;Based on image quality evaluation result, three-dimensional velocity is closed loop fine tuning optimization.The application effectively suppresses INS long-time drift, while improving SAR imaging quality, and is suitable for high dynamic navigation and imaging integrated application of platform such as unmanned aerial vehicle.

Description

Technical Field

[0001] This invention belongs to the fields of SAR-assisted navigation, UAV autonomous navigation and SAR motion compensation technology, specifically involving a three-dimensional motion velocity inversion method and system based on SAR and INS fusion. Background Technology

[0002] Global Navigation Satellite Systems (GNSS), such as GPS and Galileo, provide precise positioning, timing, and velocity information for mobile platforms like drones and vehicles, making them central to modern navigation applications. However, GNSS signals can be completely blocked in environments with electromagnetic interference, signal shielding, or complex terrain (such as urban canyons). In such cases, platforms typically rely on Inertial Navigation Systems (INS) for autonomous navigation. However, INS is inherently an "error integration system," where small biases in its accelerometers and gyroscopes accumulate rapidly over time, causing velocity, heading, and position errors to diverge exponentially. Drifts can typically reach hundreds of meters within minutes, making it difficult to meet the demands of long-endurance, high-precision navigation missions. Therefore, under GNSS rejection conditions, additional auxiliary sensors must be introduced to suppress INS drift.

[0003] Synthetic Aperture Radar (SAR), as an active microwave imaging sensor, possesses all-weather, all-day operational capabilities, making it an ideal source of auxiliary navigation information. Existing SAR / INS integrated navigation methods generally follow a "position correction" technical approach. The typical process is as follows: First, motion compensation and imaging of the SAR echo are performed using initial information provided by the INS; then, the real-time generated SAR image is matched with reference images (such as optical maps, digital elevation models, or pre-prepared SAR maps) in the airborne database; finally, the absolute position observations of the platform are obtained through the matching results, and algorithms such as Kalman filtering are used to correct the accumulated errors of the INS.

[0004] However, this type of position-correction-based navigation method has inherent technical drawbacks. First, it is highly dependent on SAR image quality and prior information. In environments lacking significant ground features (such as sea surfaces, deserts, and snowfields) or when the initial INS error is too large, causing severe image defocusing, image matching is prone to failure, making it impossible to obtain reliable position observations. Second, the navigation update frequency is low, limiting real-time performance. Complete SAR imaging and complex image matching algorithms (such as NCC and SIFT) require a long processing time, resulting in a position correction frequency typically below 1 Hz. Between updates, INS errors continue to accumulate rapidly and are difficult to suppress effectively. Third, the system link is too long, making it difficult to form a real-time closed loop. The traditional "imaging → matching → filtering" process cannot provide real-time constraints on platform motion at the pulse repetition period (PRT) level, thus significantly reducing its navigation capability when facing the rapid maneuvers of highly dynamic platforms such as UAVs.

[0005] In summary, how to break through the dependence of traditional SAR / INS navigation on image matching and prior maps, and achieve direct observation and fusion of three-dimensional motion states without relying on maps and with a high update rate, is a key issue that urgently needs to be solved in the current GNSS-denied environment for high-precision, high-dynamic autonomous navigation technology. Summary of the Invention

[0006] To address the aforementioned technical issues, this invention provides a three-dimensional motion velocity inversion method and system based on SAR and INS fusion. It utilizes multi-target radial velocity observations decoupled from triangular frequency modulated continuous wave SAR echoes, constructs a multi-line-of-sight joint observation model to invert the three-dimensional motion velocity of the platform, and tightly couples it with INS to achieve map-free navigation and imaging with a high update rate.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A three-dimensional motion velocity inversion method based on SAR and INS fusion, the method comprising:

[0009] Step 1: Collect triangular frequency modulated continuous wave SAR echo data and INS inertial measurement data, and perform inertial navigation calculations to obtain the initial state of the platform;

[0010] Step 2: Preprocess the SAR echo data to obtain the upper and lower sweep frequency beat signals. Calculate the slant range of the corresponding target by using the sum of the upper and lower sweep frequency beat signals corresponding to the same target within the same pulse repetition period. Calculate the radial velocity of the corresponding target by using the difference between the upper and lower sweep frequency beat signals.

[0011] Step 3: Construct line-of-sight vectors from the platform to at least three targets in the navigation coordinate system, wherein the line-of-sight vectors of the at least three targets are not coplanar; combine the radial velocity observations of the at least three targets into observation vectors, combine the line-of-sight vectors of the at least three targets into observation matrices, and construct a multi-target joint observation equation based on the observation vectors and observation matrices. The observation matrix is ​​used to map the three-dimensional motion velocity of the platform into radial velocity projections in each line-of-sight direction.

[0012] Step 4: Based on the initial state of the platform, use the multi-objective joint observation equation as the observation input of the state estimation algorithm to update the platform state, and obtain the three-dimensional motion velocity of the platform from the updated state vector.

[0013] Step 5: Perform SAR motion compensation and refocusing imaging using the platform's three-dimensional motion velocity obtained by inversion, and perform closed-loop fine-tuning optimization of the platform's three-dimensional motion velocity based on the image quality evaluation of the refocusing imaging results, outputting the optimized platform's three-dimensional motion velocity and high-quality SAR image.

[0014] Furthermore, in step 2: the slant distance measurement is proportional to the sum of the upper sweep frequency signal and the lower sweep frequency signal, and the radial velocity measurement is proportional to the difference between the upper sweep frequency signal and the lower sweep frequency signal.

[0015] Furthermore, in step 3: the line-of-sight vectors are not coplanar.

[0016] Furthermore, in step 4: based on the initial state of the platform, the position, velocity, and attitude of the platform are predicted using inertial measurement unit data to obtain state prediction values; the SAR observation equation is used as the observation input of the state estimation algorithm, and the observation residual is calculated based on the state prediction values ​​and the SAR observation equation; the state prediction values ​​are updated using the observation residuals to obtain the updated state vector; the eastward velocity component, northward velocity component, and celestial velocity component of the platform in the navigation coordinate system are extracted from the updated state vector as the three-dimensional motion velocity of the platform.

[0017] Furthermore, step 5, which involves using the inverted platform's three-dimensional motion velocity to perform synthetic aperture radar motion compensation and refocusing imaging, specifically includes: correcting the platform's trajectory using the inverted platform's three-dimensional motion velocity; recalculating the theoretical slant range based on the corrected trajectory and target position; calculating the residual slant range error based on the difference between the observed slant range and the theoretical slant range; calculating the phase compensation amount based on the residual slant range error; and performing phase compensation and range migration correction on the synthetic aperture radar echo; and performing refocusing imaging processing to obtain a focused synthetic aperture radar image.

[0018] Furthermore, the closed-loop fine-tuning optimization of the platform's three-dimensional motion velocity based on the image quality evaluation of the refocusing imaging results in step 5 specifically includes: extracting the region of interest from the focused synthetic aperture radar image; performing image quality evaluation on the region of interest to obtain an image quality index; constructing a velocity optimization objective function based on the image quality index; obtaining a velocity correction amount by minimizing the objective function; fine-tuning the platform's three-dimensional motion velocity using the velocity correction amount to obtain the optimized platform's three-dimensional motion velocity; and re-executing the motion compensation and refocusing imaging using the optimized platform's three-dimensional motion velocity to form a closed-loop iteration until the convergence condition is met.

[0019] Furthermore, the image quality indicators used in the image quality evaluation include one or more combinations of the following: image entropy, peak sidelobe ratio, integral sidelobe ratio, peak energy, contrast, and image sharpness.

[0020] On the other hand, the present invention provides a three-dimensional motion velocity inversion system based on SAR and INS fusion, comprising:

[0021] The acquisition module is used to acquire triangular frequency modulated continuous wave SAR echo data and INS inertial measurement data and perform inertial navigation calculations to obtain the initial state of the platform.

[0022] The preprocessing module is used to preprocess the SAR echo data to obtain the upper and lower sweep frequency beat signals. The slant range observation of the corresponding target is obtained by using the sum of the upper and lower sweep frequency beat signals corresponding to the same target within the same pulse repetition period. The radial velocity observation of the corresponding target is obtained by using the difference between the upper and lower sweep frequency beat signals.

[0023] A construction module is used to construct line-of-sight vectors from the platform to at least three targets in the navigation coordinate system, wherein the line-of-sight vectors of the at least three targets are not coplanar; the radial velocity observations of the at least three targets are combined into observation vectors, and the line-of-sight vectors of the at least three targets are combined into observation matrices; a multi-target joint observation equation is constructed based on the observation vectors and observation matrices, wherein the observation matrix is ​​used to map the three-dimensional motion velocity of the platform into radial velocity projections in each line-of-sight direction;

[0024] The update module is used to update the platform state based on the initial state of the platform, using the multi-objective joint equation as the observation input of the state estimation algorithm, and to obtain the three-dimensional motion velocity of the platform from the updated state vector.

[0025] The output module is used to perform SAR motion compensation and refocusing imaging using the platform's three-dimensional motion velocity obtained by inversion, and to perform closed-loop fine-tuning and optimization of the platform's three-dimensional motion velocity based on the image quality evaluation of the refocusing imaging results, and output the optimized platform's three-dimensional motion velocity and high-quality SAR image.

[0026] Thirdly, the present invention provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned three-dimensional motion velocity inversion method based on SAR and INS fusion.

[0027] Fourthly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned three-dimensional motion velocity inversion method based on SAR and INS fusion.

[0028] The beneficial effects of this invention are as follows:

[0029] No prior map required, strong environmental adaptability: This invention directly uses triangular frequency modulated continuous wave SAR echo decoupling to obtain slant range and radial velocity observations. It does not rely on prior geographic information such as DEM maps, SAR maps or optical maps, and can construct navigation constraints under GNSS rejection conditions, which significantly improves usability and adaptability in weak texture environments such as sea surface, desert, and snowfield.

[0030] High navigation update frequency and good real-time performance: This invention does not require the long link processing of "SAR imaging → map matching → position estimation". Instead, it directly obtains radial velocity observations from the original echo at the pulse repetition period (PRT) level, which can achieve a navigation observation update frequency of 10Hz to 1000Hz or more. Compared with traditional position correction navigation methods, it can better meet the real-time navigation needs of high dynamic platforms.

[0031] Effective suppression of long-term INS drift and high navigation accuracy: This invention utilizes multiple target scattering points to construct a multi-line-of-sight (LOS) joint observation model. Through SAR / INS tightly coupled state estimation (such as extended Kalman filtering), the three-dimensional velocity, position, attitude, and INS zero bias of the platform are jointly estimated. This can continuously suppress INS velocity drift and position divergence, and achieve long-term, high-precision autonomous navigation under GNSS rejection conditions.

[0032] Improving SAR imaging quality and achieving integrated navigation and imaging: This invention utilizes the high-precision three-dimensional motion velocity obtained through inversion to correct the platform trajectory and completes SAR motion compensation and refocusing processing, effectively reducing residual phase error and range migration error, and improving the focusing quality of SAR images. Simultaneously, it further utilizes image quality indicators such as image entropy and peak-to-sidelobe ratio to perform closed-loop fine-tuning optimization of the three-dimensional velocity, forming a joint optimization mechanism of "velocity inversion → motion compensation → image evaluation → velocity optimization," achieving simultaneous improvement in navigation accuracy and SAR imaging quality. Attached Figure Description

[0033] Figure 1 This is a flowchart of the three-dimensional motion velocity inversion method based on SAR and INS fusion of the present invention;

[0034] Figure 2 Schematic diagram illustrating the decoupling principle of range and radial velocity in triangular frequency modulated continuous wave SAR;

[0035] Figure 3This is a schematic diagram illustrating the principle of three-dimensional velocity inversion under multiple LOS constraints. Detailed Implementation

[0036] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0037] This invention proposes a three-dimensional velocity inversion method based on SAR and INS fusion. The method first utilizes triangular frequency-modulated continuous wave SAR up-and-down frequency sweep decoupling to obtain target slant range and radial velocity observations; then, it constructs a multi-target LOS (Line of Sight) radial velocity observation model and combines it with INS inertial navigation information to establish a SAR / INS tightly coupled extended Kalman filter model, achieving preliminary inversion of the platform's three-dimensional velocity under GNSS rejection conditions; further, it uses the inverted three-dimensional velocity to correct the platform's trajectory and performs SAR motion compensation; finally, it performs closed-loop fine-tuning optimization of the three-dimensional velocity based on SAR image quality evaluation results, achieving high-precision inversion of the platform's three-dimensional velocity and improvement of SAR image quality. Figure 1 As shown, the method specifically includes:

[0038] Step 1: Collect triangular frequency modulated continuous wave SAR echo data and INS inertial measurement data, and perform inertial navigation calculations to obtain the initial state of the platform;

[0039] Step 2: Preprocess the SAR echo data to obtain the upper and lower sweep frequency beat signals. Calculate the slant range of the corresponding target by using the sum of the upper and lower sweep frequency beat signals corresponding to the same target within the same pulse repetition period. Calculate the radial velocity of the corresponding target by using the difference between the upper and lower sweep frequency beat signals.

[0040] Step 3: Construct line-of-sight vectors from the platform to at least three targets in the navigation coordinate system, wherein the line-of-sight vectors of the at least three targets are not coplanar; combine the radial velocity observations of the at least three targets into observation vectors, combine the line-of-sight vectors of the at least three targets into observation matrices, and construct a multi-target joint observation equation based on the observation vectors and observation matrices. The observation matrix is ​​used to map the three-dimensional motion velocity of the platform into radial velocity projections in each line-of-sight direction.

[0041] Step 4: Based on the initial state of the platform, use the multi-objective joint observation equation as the observation input of the state estimation algorithm to update the platform state, and obtain the three-dimensional motion velocity of the platform from the updated state vector.

[0042] Step 5: Perform SAR motion compensation and refocusing imaging using the platform's three-dimensional motion velocity obtained by inversion, and perform closed-loop fine-tuning optimization of the platform's three-dimensional motion velocity based on the image quality evaluation of the refocusing imaging results, outputting the optimized platform's three-dimensional motion velocity and high-quality SAR image.

[0043] Specifically, in step 1:

[0044] The triangular frequency modulated continuous wave SAR module transmits triangular frequency modulated continuous wave signals and receives echoes from scene targets. Simultaneously, the INS inertial navigation module synchronously acquires the platform's angular velocity and acceleration information for inertial navigation calculations. The acquired angular velocity and acceleration are integrated using the inertial navigation calculation algorithm to obtain the platform's initial navigation state in the navigation coordinate system, including the platform's position, velocity, attitude, and trajectory information, which serves as the initial values ​​for subsequent SAR motion compensation and state prediction.

[0045] Without loss of generality, this invention is not limited to triangular frequency modulated continuous wave SAR, but can also be extended to pulse SAR and other systems. The platform in this invention is not limited to UAVs, but is also applicable to vehicle-mounted, ship-mounted, manned aircraft, and satellite platforms.

[0046] In step 2:

[0047] like Figure 2 As shown, this invention employs a triangular frequency-modulated continuous wave SAR system. By jointly processing the upper and lower frequency sweeps, it achieves decoupled acquisition of target slant range and radial velocity information. The triangular frequency-modulated continuous wave signal consists of periodically changing upper and lower frequency sweep signals, and its transmitted signal frequency changes linearly with time.

[0048] Let the carrier frequency be The frequency modulation slope is The instantaneous slant range between the target and the SAR platform at time t is The corresponding propagation delay is :

[0049] ,

[0050] in, Let t be the speed of light, and t represent time.

[0051] When there is relative motion between the SAR device and the target, the radial velocity of the target is denoted as... :

[0052] ,

[0053] After echo reception and deskewing, the corresponding beat frequency signal is obtained.

[0054] For the up-sweep frequency process, its up-sweep frequency beat signal It can be represented as:

[0055] ,

[0056] For the down-sweep frequency process, its down-sweep beat frequency signal It can be represented as:

[0057] ,

[0058] Among them, the Doppler frequency of SAR equipment and target motion for:

[0059] ,

[0060] This represents the wavelength, which is equal to the speed of light divided by the center frequency of the SAR transmitted signal.

[0061] By combining the above formulas, the instantaneous slant range and radial velocity of the target can be decoupled separately:

[0062] ,

[0063] ,

[0064] Therefore, by jointly calculating the upper and lower frequency sweep frequencies, the slant range and radial velocity measurements of the target can be obtained simultaneously within each pulse repetition period (PRT) without relying on SAR imaging results or external map information.

[0065] For the i-th pulse repetition period and the k-th target scattering point, the corresponding observations can be obtained: , Further construct the observation vector .

[0066] The aforementioned observations will serve as inputs for subsequent LOS line-of-sight vector construction, multi-target observation equation establishment, and SAR / INS tightly coupled extended Kalman filter three-dimensional velocity inversion.

[0067] Compared with existing position correction methods based on SAR image matching, this invention directly utilizes SAR echo decoupling to obtain radial velocity observations, enabling navigation observation information to be obtained at the pulse level. This avoids dependence on SAR image matching and prior maps, providing a foundation for high-frequency three-dimensional motion velocity inversion under GNSS rejection conditions.

[0068] In step 3:

[0069] like Figure 3As shown, this invention utilizes multi-target radial velocity observations obtained by SAR to construct a Line of Sight (LOS) observation model in the navigation coordinate system, and further establishes a three-dimensional motion velocity observation equation for the platform, providing observation information for subsequent SAR / INS tightly coupled extended Kalman filter three-dimensional velocity inversion.

[0070] Let the position of the SAR device in the navigation coordinate system at the i-th PRT time be: ,

[0071] in, Indicates the navigation coordinate system; This refers to the platform's location.

[0072] Let the position of the k-th target scattering point in the navigation coordinate system be: ,

[0073] The platform's line-of-sight vector relative to the target can then be expressed as: : ,

[0074] The corresponding slope distance is: ,

[0075] Further obtain the unit line-of-sight vector :

[0076] ,

[0077] in: This represents the line-of-sight vector corresponding to the k-th target. These represent the unit line-of-sight vector components along the x, y, and z axes of the navigation coordinate system.

[0078] For multiple targets at the same time (target 1, target 2, target 3, etc. in the figure), multiple sets of LOS line-of-sight vectors in different directions can be obtained.

[0079] Regarding the slant range observation model: Geometric relationships indicate that the theoretical slant range between the platform and the target can be expressed as... :

[0080] ,

[0081] in, This is the platform state vector;

[0082] Based on the obtained slant range observations of the i-th pulse repetition period and the k-th target scattering point. The slant range observation equation can be constructed as follows:

[0083] ,

[0084] in, This indicates slant range noise.

[0085] Regarding the radial velocity observation model: Let the three-dimensional velocity of the platform in the navigation coordinate system be: ,

[0086] The radial velocity between the target and the platform is essentially the projection of the platform velocity onto the LOS direction. Therefore:

[0087] ,

[0088] Based on the radial velocity observations of the i-th pulse repetition cycle and the k-th target scattering point A radial velocity observation equation can be constructed:

[0089] ,

[0090] in, This indicates the noise level in the radial velocity measurement.

[0091] A single target can only provide one LOS direction constraint, thus failing to uniquely determine the platform's three-dimensional velocity. To achieve three-dimensional velocity inversion, this invention utilizes multiple target scattering points to construct a multi-LOS joint observation model.

[0092] Let the first A total of PRT times were obtained The radial velocity observations of each target are used to construct a radial velocity observation vector. : , in, Indicates the first The PRT time of the first The radial velocity observations corresponding to each target, where M is a positive integer and k∈M; No. The PRT time of the first The unit line-of-sight vector in the navigation coordinate system corresponding to each target is: : , Among them, superscript Indicates the navigation coordinate system. , and These represent the components of the unit line-of-sight vector in the east, north, and sky directions, respectively, with the following symbols. This indicates the matrix transpose.

[0093] Will The unit line-of-sight vectors of each target are arranged in rows to construct the observation matrix. : , No. The platform's three-dimensional velocity at each PRT time point is : .

[0094] , , These represent the components of the platform's three-dimensional velocity in the east, north, and sky directions, respectively.

[0095] The multi-target radial velocity observation model can then be expressed as: : .

[0096] When the number of observed targets M satisfies When the corresponding LOS directions are not coplanar, i.e., the observation matrix... When the column rank is full, multi-target joint observation can be used to estimate the three-dimensional motion velocity of the platform.

[0097] To improve the accuracy of state estimation, this invention simultaneously utilizes slant range observations and radial measurements to construct a joint observation vector: Establish the corresponding nonlinear observation function:

[0098] ,

[0099] in, It includes both slant range observation model and radial velocity observation model. This represents the total observation noise.

[0100] The aforementioned joint observation vector will be used as the SAR observation equation, input into the SAR / INS tightly coupled extended Kalman filter state update process, to realize the three-dimensional motion velocity inversion of the platform under GNSS denial conditions.

[0101] Unlike existing position correction navigation methods based on SAR image matching, this invention directly utilizes radial velocity and slant range observations obtained through SAR echo decoupling to construct a LOS observation model, realizing the inversion of the platform's three-dimensional motion velocity. It can obtain navigation constraint information without relying on map matching, thereby significantly improving the navigation observation update frequency and enhancing navigation stability in GNSS denied environments.

[0102] Furthermore, in step 4:

[0103] This invention utilizes the obtained SAR slant range and radial velocity observations, combined with the established LOS observation model, to construct a SAR / INS tightly coupled Extended Kalman Filter (EKF) state estimation model (which can also be replaced by UKF, particle filtering, graph optimization, factor graph optimization, nonlinear least squares, and other state estimation methods), to achieve the inversion of the platform's three-dimensional motion velocity under GNSS rejection conditions.

[0104] Unlike traditional INS / SAR matching navigation that relies on position correction, this invention directly utilizes radial velocity observations obtained from SAR echo decoupling to construct velocity constraints, enabling high-frequency updates of the platform's three-dimensional motion velocity, thereby improving navigation stability and suppressing INS drift.

[0105] Let the first The state vector at each PRT time step is : , in, The platform position in the navigation coordinate system. For platform speed, These are the platform attitude parameters. and These are the zero bias of the gyroscope and the zero bias of the accelerometer, respectively.

[0106] The prior state estimate is obtained based on the inertial navigation state prediction model: And the corresponding prior state error covariance matrix: Based on the nonlinear observation function Calculate and predict observations: , in, For the first The predicted observation vector at each PRT time point. The SAR measured slant range and radial velocity measurements are combined to form the actual observation vector. And construct the observation residuals: , in, To observe residuals; In the prior state Calculate the Jacobian matrix of the observation function at: , in, This represents the matrix of first-order partial derivatives of the observation function with respect to the state vector.

[0107] Calculate the Kalman gain: , in, Here is the Kalman gain matrix. To observe the noise covariance matrix, the superscript... Indicates transpose, superscript This represents finding the inverse of a matrix.

[0108] Update the state using the observed residuals: ,

[0109] in, This is for posterior state estimation.

[0110] Update the state error covariance matrix: , in, Let be the posterior state error covariance matrix. It is the identity matrix corresponding to the dimension of the state vector.

[0111] After completing the state update, from the posterior state vector Extracting platform speed estimates: .

[0112] Furthermore, in step 5:

[0113] The present invention utilizes the platform's three-dimensional motion velocity inversion results obtained in step 4 to correct the platform's motion trajectory and further complete SAR motion compensation to improve the focusing quality of SAR images.

[0114] Platform trajectory correction can be represented as: , This represents the platform position at the i-th PRT time.

[0115] in, The interval between adjacent PRTs is the period of the triangular frequency modulated continuous wave transmitted by the SAR.

[0116] Based on the corrected trajectory, the theoretical slant distance between the platform and the target is recalculated: ,

[0117] The residual slope error is: ,in This is the actual observation matrix.

[0118] Calculate the first based on the residual slant distance error. The PRT time, the Residual phase error corresponding to each target: ,in, This represents the wavelength corresponding to the SAR carrier frequency. The residual phase error is used to compensate for the SAR echo. ,in, This represents the SAR recovery signal before phase compensation. This represents the SAR complex echo signal after phase compensation. Indicates residual phase error. Represents the imaginary unit and satisfies Distance migration correction (RCMC) is then performed subsequently.

[0119] After motion compensation is completed, the SAR data is imaged to obtain SAR images. By using inverted three-dimensional velocity for motion compensation, the focusing quality of SAR images can be effectively improved and geometric distortion reduced.

[0120] To further improve the accuracy of 3D velocity inversion and SAR imaging quality, this invention constructs an image quality-driven velocity closed-loop optimization mechanism.

[0121] Extracting the region of interest from SAR refocused imaging results: ,in, Indicates the first ROI within the ROI Complex magnitude of a pixel, This represents the total number of pixels within the ROI. A quality evaluation metric for the ROI region is established. Here, image entropy (or other metrics such as peak energy, contrast, PSLR, ISLR, gradient sharpness, etc., or a combination of multiple metrics) is used to measure the SAR image quality after speed adjustment. The first... The normalized energy of each pixel is: , Define the image quality objective function: ,in, Indicates the first ROI within the ROI The proportion of each pixel's energy to the total energy of the ROI, satisfying the following: .

[0122] Finding the optimal speed correction:

[0123] , To extend the three-dimensional velocity estimates obtained from the initial inversion using the Kalman filter, This is the speed correction amount to be searched.

[0124] Obtain the optimized speed estimate: ;

[0125] The optimized velocity is used to re-perform trajectory correction, followed by motion compensation, SAR imaging, and image evaluation, forming a closed-loop iterative process. ,until: The convergence condition has been met. (This represents the convergence threshold). This closed-loop mechanism can further improve the accuracy of 3D velocity estimation and the focusing quality of SAR images.

[0126] On the other hand, this invention provides a three-dimensional motion velocity inversion system based on SAR and INS fusion, whose various modules can implement the various steps of the aforementioned method, specifically including:

[0127] The acquisition module is used to acquire triangular frequency modulated continuous wave SAR echo data and INS inertial measurement data and perform inertial navigation calculations to obtain the initial state of the platform.

[0128] The preprocessing module is used to preprocess the SAR echo data to obtain the upper and lower sweep frequency beat signals. The slant range observation of the corresponding target is obtained by using the sum of the upper and lower sweep frequency beat signals corresponding to the same target within the same pulse repetition period. The radial velocity observation of the corresponding target is obtained by using the difference between the upper and lower sweep frequency beat signals.

[0129] A construction module is used to construct line-of-sight vectors from the platform to at least three targets in the navigation coordinate system, wherein the line-of-sight vectors of the at least three targets are not coplanar; the radial velocity observations of the at least three targets are combined into observation vectors, and the line-of-sight vectors of the at least three targets are combined into observation matrices; a multi-target joint observation equation is constructed based on the observation vectors and observation matrices, wherein the observation matrix is ​​used to map the three-dimensional motion velocity of the platform into radial velocity projections in each line-of-sight direction;

[0130] The update module is used to update the platform state based on the initial state of the platform, using the multi-objective joint equation as the observation input of the state estimation algorithm, and to obtain the three-dimensional motion velocity of the platform from the updated state vector.

[0131] The output module is used to perform SAR motion compensation and refocusing imaging using the platform's three-dimensional motion velocity obtained by inversion, and to perform closed-loop fine-tuning and optimization of the platform's three-dimensional motion velocity based on the image quality evaluation of the refocusing imaging results, and output the optimized platform's three-dimensional motion velocity and high-quality SAR image.

[0132] Thirdly, the present invention provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned three-dimensional motion velocity inversion method based on SAR and INS fusion.

[0133] Fourthly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned three-dimensional motion velocity inversion method based on SAR and INS fusion.

[0134] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A three-dimensional motion velocity inversion method based on SAR and INS fusion, characterized in that, The method includes: Step 1: Collect triangular frequency modulated continuous wave SAR echo data and INS inertial measurement data, and perform inertial navigation calculations to obtain the initial state of the platform; Step 2: Preprocess the SAR echo data to obtain the upper sweep frequency beat signal and the lower sweep frequency beat signal. Calculate the slant range measurement of the corresponding target by using the sum of the upper sweep frequency beat signal and the lower sweep frequency beat signal corresponding to the same target within the same pulse repetition period. Calculate the radial velocity measurement of the corresponding target by using the difference between the upper sweep frequency beat signal and the lower sweep frequency beat signal. Step 3: Construct line-of-sight vectors from the platform to at least three targets in the navigation coordinate system; combine the radial velocity observations of the at least three targets into observation vectors, and combine the line-of-sight vectors of the at least three targets into an observation matrix; construct a multi-target joint observation equation based on the observation vectors and the observation matrix; the observation matrix is ​​used to map the three-dimensional motion velocity of the platform into radial velocity projections in each line-of-sight direction; wherein, the line-of-sight vectors of the at least three targets are not coplanar; Step 4: Based on the initial state of the platform, use the multi-objective joint observation equation as the observation input of the state estimation algorithm to update the platform state, and obtain the three-dimensional motion velocity of the platform from the updated state vector. Step 5: Perform SAR motion compensation and refocusing imaging using the platform's three-dimensional motion velocity obtained by inversion, and perform closed-loop fine-tuning optimization of the platform's three-dimensional motion velocity based on the image quality evaluation of the refocusing imaging results, outputting the optimized platform's three-dimensional motion velocity and high-quality SAR image.

2. The three-dimensional motion velocity inversion method based on SAR and INS fusion according to claim 1, characterized in that, In step 2: the slant distance measurement is proportional to the sum of the upper sweep frequency signal and the lower sweep frequency signal, and the radial velocity measurement is proportional to the difference between the upper sweep frequency signal and the lower sweep frequency signal.

3. The three-dimensional motion velocity inversion method based on SAR and INS fusion according to claim 1, characterized in that, In step 4, the state estimation algorithm includes any one of extended Kalman filtering, unscented Kalman filtering, particle filtering, graph optimization, or nonlinear least squares.

4. The three-dimensional motion velocity inversion method based on SAR and INS fusion according to claim 1, characterized in that, Step 5, which involves using the inverted platform's three-dimensional motion velocity to perform synthetic aperture radar motion compensation and refocusing imaging, specifically includes: correcting the platform's trajectory using the inverted platform's three-dimensional motion velocity; recalculating the theoretical slant range based on the corrected trajectory and target position; calculating the residual slant range error based on the difference between the observed slant range and the theoretical slant range; calculating the phase compensation amount based on the residual slant range error; and performing phase compensation and range migration correction on the synthetic aperture radar echo; and performing refocusing imaging processing to obtain a focused synthetic aperture radar image.

5. The three-dimensional motion velocity inversion method based on SAR and INS fusion according to claim 4, characterized in that, Step 5, which involves image quality evaluation based on refocusing imaging results to perform closed-loop fine-tuning optimization of the platform's three-dimensional motion velocity, specifically includes: extracting the region of interest from the focused synthetic aperture radar image; performing image quality evaluation on the region of interest to obtain image quality indices; constructing a velocity optimization objective function based on the image quality indices; obtaining a velocity correction amount by minimizing the objective function; fine-tuning and optimizing the platform's three-dimensional motion velocity using the velocity correction amount to obtain the optimized platform's three-dimensional motion velocity; and re-executing the motion compensation and refocusing imaging using the optimized platform's three-dimensional motion velocity to form a closed-loop iteration until the convergence condition is met.

6. The three-dimensional motion velocity inversion method based on SAR and INS fusion according to claim 5, characterized in that, The image quality indicators used in the image quality evaluation include, but are not limited to, one or more combinations of the following: image entropy, peak sidelobe ratio, integral sidelobe ratio, peak energy, contrast, and image sharpness.

7. A three-dimensional motion velocity inversion system based on SAR and INS fusion, characterized in that, include: The acquisition module is used to acquire triangular frequency modulated continuous wave SAR echo data and INS inertial measurement data and perform inertial navigation calculations to obtain the initial state of the platform. The preprocessing module is used to preprocess the SAR echo data to obtain the upper sweep frequency beat signal and the lower sweep frequency beat signal. The slant range observation of the corresponding target is obtained by using the sum of the upper sweep frequency beat signal and the lower sweep frequency beat signal corresponding to the same target within the same pulse repetition period. The radial velocity observation of the corresponding target is obtained by using the difference between the upper sweep frequency beat signal and the lower sweep frequency beat signal. A construction module is used to construct line-of-sight vectors from the platform to at least three targets in the navigation coordinate system; to form an observation vector from the radial velocity observations of the at least three targets; to form an observation matrix from the line-of-sight vectors of the at least three targets; and to construct a multi-target joint observation equation based on the observation vectors and the observation matrix. The observation matrix is ​​used to map the three-dimensional motion velocity of the platform to the radial velocity projection in each line-of-sight direction. The line-of-sight vectors of the at least three targets are not coplanar. The update module is used to update the platform state based on the initial state of the platform, using the multi-objective joint equation as the observation input of the state estimation algorithm, and to obtain the three-dimensional motion velocity of the platform from the updated state vector. The output module is used to perform SAR motion compensation and refocusing imaging using the platform's three-dimensional motion velocity obtained by inversion, and to perform closed-loop fine-tuning and optimization of the platform's three-dimensional motion velocity based on the image quality evaluation of the refocusing imaging results, and output the optimized platform's three-dimensional motion velocity and high-quality SAR image.

8. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When one or more programs are executed by the one or more processors, the one or more processors implement the three-dimensional motion velocity inversion method based on SAR and INS fusion as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, It stores executable instructions that, when executed by a processor, enable the processor to implement the three-dimensional motion velocity inversion method based on SAR and INS fusion as described in any one of claims 1-6.

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