Wide-angle staring SAR moving target trajectory reconstruction method and device based on Gaussian Newton method

Abstract

The invention discloses a wide-angle staring SAR moving target trajectory reconstruction method and device based on a Gauss-Newton method. The method comprises the following steps: carrying out sub-aperture division on an original radar echo signal; performing two-dimensional compression on the original echo signal of each channel in distance and azimuth dimensions to obtain a distance-Doppler domain echo of each channel; the distance-Doppler domain echo is preprocessed in the distance-Doppler domain; based on the preprocessed distance-Doppler domain echoes, the phase history and envelope of the moving target are extracted, and the radial speed of the moving target is estimated; estimating each order of phase parameter in the extracted phase history by adopting a maximum likelihood algorithm; based on the relation between the phase parameter of each order and the motion parameter, obtaining the motion parameter of the motion target; obtaining a rough motion track reconstruction result based on the motion parameters of the motion target; segmenting the rough movement track of the moving target to obtain the accurate reconstruction position of the moving target of each segment; and splicing the reconstruction positions to obtain a complete motion trail reconstruction result.

Description

Technical Field

[0001] The invention belongs to the field of radar signal processing, and in particular relates to a method and a device for reconstructing a moving target trajectory of a wide-angle staring SAR based on a Gauss-Newton method. Background Art

[0002] Wide-angle Staring Synthetic Aperture Radar (WasSAR) is a special mode of synthetic aperture radar (SAR). In this mode, the radar illuminates the region of interest (ROI) for a long time to accumulate a large coherent accumulation angle. The main implementation form of wide accumulation angle is spotlight SAR, such as circular SAR (CSAR) and strip spotlight SAR. Combining WasSAR with ground moving target indication (GMTI) technology can achieve long-term continuous monitoring of ROI, which has been widely used in military and civilian fields, such as battlefield surveillance and traffic monitoring. The geolocation of moving targets is an important practical application in the WasSAR-GMTI system, which can generally be divided into two categories: shadow-based geolocation and Doppler energy-based geolocation.

[0003] The shadow of a moving target will be projected at the actual position without any offset, which is effective for the geolocation of moving targets. The most common approach is to detect the shadow of a moving target through morphological operations. The establishment of a background model is the core step of shadow detection. However, the shadow-based geolocation method is limited by the quality of the shadow. Due to the low signal-to-noise ratio of SAR images, the fast movement of the target, and the obstruction by objects (such as buildings and trees), the shadow of the moving target is prone to blurring and smearing. The Doppler energy detection method based on the range-doppler (RD) domain has better robustness and stability in the geolocation of moving targets as the information dimension increases.

[0004] The main task of geolocation is to solve the Doppler shift determined by the azimuth position and radial velocity of the moving target, that is, the azimuth uncertainty. The commonly used method at present is to use multiple perspectives to establish the motion equation and solve the motion parameters. However, due to the large condition number in this method, the parameter estimation equation is ill-conditioned. In order to solve the ill-conditioned equation problem, the moving target is assumed to have a constant velocity during a long observation time. However, during a long observation time, the moving target may undergo nonlinear motion and rotation. Therefore, this method is not applicable in the processing of measured data. In addition, a multi-channel system along the track is used. As the spatial degrees of freedom increase, the radial velocity can be estimated by the interference phase. The additional Doppler shift related to the radial velocity can be directly determined. However, this method is sensitive to velocity estimation and is easily affected by clutter and noise in the actual data. In order to eliminate geolocation errors, traditional methods have searched for multidimensional parameters to find the optimal result and maximize the contrast function. However, the multidimensional search process requires a large amount of calculation, and the ill-conditioned equation easily causes the result to fall into a local extreme value. In addition, some methods use a priori known road information, but this method requires obtaining scene information in advance, which limits practical applications. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method and device for reconstructing moving target trajectories of wide-angle staring SAR based on the Gauss-Newton method.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0007] On the one hand, the present invention provides a method for reconstructing moving target trajectories of wide-angle staring SAR based on Gauss-Newton method, comprising:

[0008] (1) Perform sub-aperture division on the original radar echo signal to obtain a multi-channel original echo signal;

[0009] (2) Performing two-dimensional compression on the original echo signals of each channel in the range and azimuth dimensions to obtain the range-Doppler domain echo of each channel;

[0010] (3) performing preprocessing in the range-Doppler domain based on the range-Doppler domain echo of each channel, wherein the preprocessing includes multi-channel registration and clutter suppression, to obtain the range-Doppler domain echo after preprocessing;

[0011] (4) Based on the pre-processed range-Doppler domain echo, the phase history and envelope of the moving target are extracted to estimate the radial velocity of the moving target;

[0012] (5) The phase parameters of the moving target phase history are coupled with the carrier position and the motion information of the moving target. The maximum likelihood algorithm is used to estimate the phase parameters of each order in the extracted phase history.

[0013] (6) Based on the relationship between each order phase parameter and motion parameter, the Gauss-Newton method is used to decouple the motion parameters and obtain the motion parameters of the moving target;

[0014] (7) Obtaining a rough motion trajectory reconstruction result based on the motion parameters of the moving target;

[0015] (8) Segmenting the rough motion trajectory of the moving target, decoupling the extracted envelope from the rough motion trajectory of each segment using the Gauss-Newton method, and obtaining the precise reconstructed position of the moving target in each segment;

[0016] (9) The reconstructed position of the moving target in each segment of the motion trajectory is spliced ​​together to obtain a complete motion trajectory reconstruction result.

[0017] On the other hand, the present invention provides a wide-angle staring SAR moving target trajectory reconstruction device based on the Gauss-Newton method, comprising:

[0018] The original echo processing module is used to perform sub-aperture division on the original radar echo signal to obtain a multi-channel original echo signal; perform two-dimensional compression on the original echo signal of each channel in the range and azimuth dimensions to obtain the range-Doppler domain echo of each channel; perform preprocessing in the range-Doppler domain based on the range-Doppler domain echo of each channel, wherein the preprocessing includes multi-channel registration and clutter suppression to obtain the range-Doppler domain echo after preprocessing;

[0019] The parameter acquisition module extracts the phase history and envelope of the moving target based on the pre-processed range-Doppler domain echo, and estimates the radial velocity of the moving target. The phase parameters of the moving target phase history are coupled with the carrier position and the motion information of the moving target, and the maximum likelihood algorithm is used to estimate the phase parameters of each order in the extracted phase history. Based on the relationship between each order phase parameter and the motion parameter, the Gauss-Newton method is used to decouple the motion parameter to obtain the motion parameter of the moving target.

[0020] The position reconstruction module obtains the rough motion trajectory reconstruction result based on the motion parameters of the moving target; the rough motion trajectory of the moving target is segmented, and the extracted envelope is decoupled from the rough motion trajectory of each segment using the Gauss-Newton method to obtain the accurate reconstructed position of the moving target of each segment;

[0021] The trajectory reconstruction module is used to splice the reconstructed position of the moving target in each segment of the motion trajectory to obtain a complete motion trajectory reconstruction result.

[0022] The above-mentioned wide-angle staring SAR moving target trajectory reconstruction method and device based on Gauss-Newton method first divides the original echo signal received by the radar into multiple sub-apertures, and performs two-dimensional compression on the original echo signal of each channel in the distance and azimuth dimensions to obtain the range-Doppler domain echo of each channel. After pre-processing the range-Doppler domain echo, the phase history and envelope of the moving target are extracted, the radial velocity of the moving target is estimated, and the polynomial parameters of the phase history are estimated by the maximum likelihood method (Quasi-maximum Likelihood, QML); secondly, the Gauss-Newton algorithm is used to decouple the motion parameters, and the rough trajectory can be reconstructed using the motion parameters; then, the motion trajectory of the moving target is segmented modeled by extracting the envelope history of the moving target, and each segment of the motion trajectory is estimated by using the corrected envelope history; finally, after processing each sub-aperture, the accurate and complete trajectory of the moving target is obtained. The present invention can solve the problem that the existing method is difficult to achieve the geographic positioning of the moving target. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0024] Figure 1 A schematic diagram of a flow chart of a method for reconstructing moving target trajectories of a wide-angle staring SAR based on the Gauss-Newton method provided by an embodiment;

[0025] Figure 2 A schematic diagram of a geometric model for multi-channel WasSAR data acquisition provided by an embodiment;

[0026] Figure 3 A schematic diagram of segmented modeling of a motion trajectory provided by an embodiment;

[0027] Figure 4 Optical images of measured data, imaging results, and cooperative vehicle target images provided by an embodiment;

[0028] Figure 5 Position information measured by an airborne GPS provided by an embodiment;

[0029] Figure 6 A comparison diagram of the PPS coefficients estimated by the QML algorithm and the theoretical values ​​for objective 1 provided in an embodiment, wherein Figure (a), Figure (b), and Figure (c) are the first-order coefficients, the second-order coefficients, and the third-order coefficients, respectively;

[0030] Figure 7A comparison diagram of target motion parameters and true values ​​provided by an embodiment, wherein Figure (a), Figure (b), Figure (c) and Figure (d) are respectively the x-direction velocity, y-direction velocity, x-direction acceleration and y-direction acceleration of target 1; Figure (e), Figure (f), Figure (g) and Figure (h) are respectively the x-direction velocity, y-direction velocity, x-direction acceleration and y-direction acceleration of target 2; Figure (i), Figure (j), Figure (k) and Figure (l) are respectively the x-direction velocity, y-direction velocity, x-direction acceleration and y-direction acceleration of target 3;

[0031] Figure 8 A result diagram of extracting envelope information of target 1 provided by an embodiment;

[0032] Fig. 9 A trajectory reconstruction result diagram provided by an embodiment, wherein Figure (a), Figure (b) and Figure (c) are trajectory reconstruction structure diagrams of target 1, target 2 and target 3 respectively. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] In one embodiment, referring to Figure 1 , provides a wide-angle staring SAR moving target trajectory reconstruction method based on the Gauss-Newton method, including:

[0035] (1) Perform sub-aperture division on the original radar echo signal to obtain a multi-channel original echo signal;

[0036] (2) Performing two-dimensional compression on the original echo signals of each channel in the range and azimuth dimensions to obtain the range-Doppler domain echo of each channel;

[0037] (3) performing preprocessing in the range-Doppler domain based on the range-Doppler domain echo of each channel, wherein the preprocessing includes multi-channel registration and clutter suppression, to obtain the range-Doppler domain echo after preprocessing;

[0038] (4) Based on the pre-processed range-Doppler domain echo, the phase history and envelope of the moving target are extracted to estimate the radial velocity of the moving target;

[0039] (5) The phase parameters of the moving target phase history are coupled with the carrier position and the motion information of the moving target. The maximum likelihood algorithm is used to estimate the phase parameters of each order in the extracted phase history.

[0040] (6) Based on the relationship between each order phase parameter and motion parameter, the Gauss-Newton method is used to decouple the motion parameters and obtain the motion parameters of the moving target;

[0041] (7) Obtaining a rough motion trajectory reconstruction result based on the motion parameters of the moving target;

[0042] (8) Segmenting the rough motion trajectory of the moving target, decoupling the extracted envelope from the rough motion trajectory of each segment using the Gauss-Newton method, and obtaining the precise reconstructed position of the moving target in each segment;

[0043] (9) The reconstructed position of the moving target in each segment of the motion trajectory is spliced ​​together to obtain a complete motion trajectory reconstruction result.

[0044] Reference Figure 2 In one embodiment, in step (1), the original radar echo signal is received by a SAR radar; and step (2) includes the following steps:

[0045] Perform range pulse pressure and range-direction fast Fourier transform on the original echo signals of each channel to obtain the echo baseband signal of the moving target:

[0046] s e (r,t m )=σg e sinc{2B[rR e (t m ; r p )] / c}·exp[-j4πf c R e (t m ; r p ) / c]

[0047] Among them, σ represents the amplitude of the echo, g e Represents the bidirectional antenna pattern of each channel. B, f c and c represent the bandwidth, carrier frequency and light speed of the echo respectively. r represents the slant range unit, t m represents slow time; r p Represents the actual position of the moving target; R e (t m ; r p ) represents the slant range from the carrier to the target; j represents the imaginary unit; exp(·) represents the exponential operation with the natural number e as the base;

[0048] The original echo signals of each channel are subjected to fast Fourier transform in the azimuth dimension to realize azimuth compression and obtain the range-Doppler domain echo.

[0049] Since the antenna spacing causes the delay difference of each channel observing the same scene, the correlation of the multi-channel received signals is destroyed. Therefore, it is necessary to use the speed of the carrier and the baseline length between the multiple channels to compensate for the delay error. After the compensation is completed, the multi-channel alignment is completed. The clutter suppression effect in step (3) is proportional to the spatial degrees of freedom. As the spatial degrees of freedom increase, the effect of multi-channel clutter suppression becomes better and better.

[0050] Traditional multi-channel moving target detection methods include Displaced Phase Center Array (DPCA), Along Track Interferometry (ATI), clutter suppression interference (CSI) and Space-time Adaptive Processing (STAP).

[0051] In step (4), the radial velocity of the moving target can be estimated by weighted adaptive matched filtering (WAMF), subspace projection (SP) and other methods.

[0052] Step (5) comprises the following steps:

[0053] R(t m ; r p ) at the subaperture center t m =t k Taylor expansion:

[0054]

[0055] Where R(t m ; r p ) is the slant distance of the moving target from the carrier, which couples the position of the carrier and the motion information of the moving target; R′(t k ; r p ), R″(t k ; r p ), R″′(t k ; r p ), R″″(t k ; r p ) and ο(t m ; r p ) represent the first, second, third, fourth and higher order Taylor expansion coefficients of the phase history respectively;

[0056] At this time, the echo expression of the moving target can be expressed as

[0057]

[0058] The above formula converts the phase history estimate into a polynomial phase parameter estimate, which can be expanded into a form consisting of the sum of multiple phase parameters, and the polynomial phase parameters are estimated using the maximum likelihood algorithm. The QML estimator is a powerful phase parameter estimation technology that can theoretically estimate phase parameters of any order.

[0059] Because the phase parameters of the moving target phase history are coupled with the carrier position and the motion information of the moving target, the Gauss-Newton method is needed to decouple the motion parameters.

[0060] Step (6) comprises the following steps:

[0061] Using the quadratic and cubic Taylor expansion coefficients R″(t k ; r p )、R″′(t k ; r p ), the improved algorithm of Gauss-Newton method is used to decouple the motion parameters. First, the motion parameter estimation model is established as shown below

[0062]

[0063] Among them, r a,i , v a,i and θ k,i Respectively represent the flight radius, speed and azimuth of the aircraft in the i-th sub-aperture, which can be obtained through the airborne GPS; x pk,i ,y pk,i Respectively represent the x-coordinate position and y-coordinate position of the moving target; represents the radial velocity of the moving target estimated by the i-th sub-aperture;

[0064] The above motion parameter estimation model is converted into a least squares problem to obtain the motion parameters of the moving target:

[0065]

[0066] where x i Represents the motion parameters of the target within sub-aperture i.

[0067] According to x i The motion parameters of the moving target are obtained as follows:

[0068]

[0069] in, They are the x-direction speed and y-direction speed of the moving target respectively; They are respectively the x-direction acceleration and the y-direction acceleration of the moving target.

[0070] Step (7) comprises:

[0071] Reconstruct the rough motion trajectory of the moving target based on the estimated motion parameters of the moving target

[0072]

[0073] Among them, x pk,1 ,y pk,1 They represent the initial x-coordinate position and initial y-coordinate position of the moving target respectively.

[0074] Since the estimation of motion parameters is prone to fall into local extreme values ​​and there is error accumulation in parameter estimation, the reconstructed rough motion trajectory will deviate from the true value. It is necessary to segment the rough motion trajectory and use the envelope of the moving target to correct the segmented rough motion trajectory, so as to obtain the accurate reconstructed position of the moving target.

[0075] Extracting the envelope of the moving target in step (4) includes the following steps:

[0076] Extract the envelope information of the echo, assuming that the echo is M units to the right along the azimuth

[0077] r e =[r e (1)r e (2)...r e (M)]

[0078] where r e Represents the complete envelope history of the echo;

[0079] By extracting the phase of the peak echo in the range profile, the envelope history can be obtained based on the conversion relationship between phase and distance.

[0080]

[0081] Where Angle[g] represents the phase extraction, λ represents the wavelength, s(r,t m ) represents the echo signal at the peak position in the range profile.

[0082] Step (8) comprises:

[0083] The motion state of the moving target is modeled using a segmentation method. crs is the rough trajectory obtained in the previous step. ideal is the actual trajectory, l plane is the platform trajectory; the motion trajectory is evenly divided into K segments according to the time change, each of which contains a slow time unit Each sub-segment is denoted as l crs,1 ,l crs,2 ,...,l crs,k ,...,l crs,K , the corresponding platform trajectory is recorded as l plane,1 ,l plane,2 ,...,l plane,k ,...,l plane,K . crs,k and l plane,k It can be expressed as:

[0084]

[0085] In the kth segment, the target moves at a constant speed; define the starting position (x k ,y k ) and speed (v x,k ,v y,k ), then define the target state variable x under uniform linear motion k It is expressed as follows

[0086] x k =[x k ,y k ,v x,k ,v y,k ] T

[0087] Based on the above target state variables, the measurement equation is:

[0088] r k =||l plane,k -l k ||2

[0089] Among them l k Represents the motion state x k The following motion trajectory,

[0090]

[0091] Where Δt m Indicates a slow time interval;

[0092] Building the first expression

[0093] g1(x k )=r k -r e,k

[0094] where r e,k =r e | m∈[1+(k-1)Q,kQ] ;

[0095] In the connection of each sub-segment, a prediction mechanism is introduced to calculate the time unit n after the end of sub-segment k-1 p The target movement trend within the subsegment k is taken as the first n p The known trajectory within time units,

[0096]

[0097] Among them, l pre,k is the time unit n after the end of subsegment k-1 p The target movement trend within the

[0098]

[0099] The target position information and velocity information provided by the rough trajectory are introduced into the measurement equation. The target position information is

[0100] g3(x k )=||l crs,k -l k ||2

[0101] According to the target speed information, the equation is established as

[0102]

[0103] Using the equations established above, the problem of solving the state parameters of the moving target is transformed into a least squares problem

[0104]

[0105] The Gauss-Newton method is used to solve the above least squares problem and obtain the state of the target in each trajectory segment.

[0106] The reconstructed positions of the moving targets in each segment of the motion trajectory are spliced ​​together to obtain a complete motion trajectory reconstruction result.

[0107] At this point, the wide-angle staring SAR moving target trajectory reconstruction method based on the Gauss-Newton method has been completed.

[0108] In one embodiment, measured WasSAR data is used, and comparative experiments are designed to illustrate the effectiveness and superiority of the algorithm.

[0109] In one embodiment, the measured original WasSAR data is recorded by a Ku-band airborne radar, the aircraft speed is about 50m / s, and the flight altitude is about 2000m. Figure 4 , Figure 5 , Figure 4 Optical images of measured data, imaging results, and cooperative vehicle target images provided by an embodiment; Figure 5This is location information measured by an airborne GPS provided by an embodiment.

[0110] After performing echo range and azimuth compression, multi-channel registration, and clutter suppression, the QML algorithm is used to estimate the target's phase coefficient. Figure 6 , the QML algorithm is used to estimate the third-order phase coefficient of target 1. Then the LM algorithm is used to decouple the motion parameters, referring to Figure 7 , Figure 7 A comparison diagram of target motion parameters and true values ​​provided by an embodiment, wherein Figure (a), Figure (b), Figure (c) and Figure (d) are respectively the x-direction velocity, y-direction velocity, x-direction acceleration and y-direction acceleration of target 1; Figure (e), Figure (f), Figure (g) and Figure (h) are respectively the x-direction velocity, y-direction velocity, x-direction acceleration and y-direction acceleration of target 2; Figure (i), Figure (j), Figure (k) and Figure (l) are respectively the x-direction velocity, y-direction velocity, x-direction acceleration and y-direction acceleration of target 3. Figure 8 , Figure 8 This is a diagram showing the result of extracting the envelope information of target 1 provided by an embodiment. Fig. 9 , Fig. 9 FIG. 1 is a trajectory reconstruction result diagram provided by an embodiment, wherein FIG. (a), FIG. (b) and FIG. (c) are trajectory reconstruction structure diagrams of target 1, target 2 and target 3 respectively. Obviously, from FIG. Fig. 9 It can be seen that the trajectory reconstruction result of the method proposed in this paper is closer to the true value, which illustrates the effectiveness of the method proposed in this invention.

[0111] The comparison of the positioning accuracy of the traditional positioning method and the algorithm proposed in this invention on the moving target is shown in Table 1.

[0112] Table 1 Positioning error of each algorithm

[0113]

[0114] It can be seen from Table 1 that compared with the traditional method, the trajectory reconstruction accuracy of the method of the present invention is higher. Compared with the traditional method, the method provided by the present invention has superiority.

[0115] In one embodiment, a wide-angle staring SAR moving target trajectory reconstruction device based on Gauss-Newton method is provided, comprising:

[0116] The original echo processing module is used to perform sub-aperture division on the original radar echo signal to obtain a multi-channel original echo signal; perform two-dimensional compression on the original echo signal of each channel in the range and azimuth dimensions to obtain the range-Doppler domain echo of each channel; perform preprocessing in the range-Doppler domain based on the range-Doppler domain echo of each channel, wherein the preprocessing includes multi-channel registration and clutter suppression to obtain the range-Doppler domain echo after preprocessing;

[0117] The parameter acquisition module extracts the phase history and envelope of the moving target based on the pre-processed range-Doppler domain echo, and estimates the radial velocity of the moving target. The phase parameters of the moving target phase history are coupled with the carrier position and the motion information of the moving target, and the maximum likelihood algorithm is used to estimate the phase parameters of each order in the extracted phase history. Based on the relationship between each order phase parameter and the motion parameter, the Gauss-Newton method is used to decouple the motion parameter to obtain the motion parameter of the moving target.

[0118] The position reconstruction module obtains the rough motion trajectory reconstruction result based on the motion parameters of the moving target; the rough motion trajectory of the moving target is segmented, and the extracted envelope is decoupled from the rough motion trajectory of each segment using the Gauss-Newton method to obtain the accurate reconstructed position of the moving target of each segment;

[0119] The trajectory reconstruction module is used to splice the reconstructed position of the moving target in each segment of the motion trajectory to obtain a complete motion trajectory reconstruction result.

[0120] Matters not covered by the present invention are known technologies.

[0121] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0122] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

[0123] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for reconstructing moving target trajectories of wide-angle staring SAR based on Gauss-Newton method, characterized in that: include: (1) Perform sub-aperture division on the original radar echo signal to obtain a multi-channel original echo signal; (2) Performing two-dimensional compression on the original echo signals of each channel in the range and azimuth dimensions to obtain the range-Doppler domain echo of each channel; (3) performing preprocessing in the range-Doppler domain based on the range-Doppler domain echo of each channel, wherein the preprocessing includes multi-channel registration and clutter suppression, to obtain the range-Doppler domain echo after preprocessing; (4) Based on the pre-processed range-Doppler domain echo, the phase history and envelope of the moving target are extracted to estimate the radial velocity of the moving target; (5) The phase parameters of the moving target phase history are coupled with the carrier position and the motion information of the moving target. The maximum likelihood algorithm is used to estimate the phase parameters of each order in the extracted phase history. (6) Based on the relationship between each order phase parameter and motion parameter, the Gauss-Newton method is used to decouple the motion parameters and obtain the motion parameters of the moving target; (7) Obtaining a rough motion trajectory reconstruction result based on the motion parameters of the moving target; (8) Segmenting the rough motion trajectory of the moving target, decoupling the extracted envelope from the rough motion trajectory of each segment using the Gauss-Newton method, and obtaining the precise reconstructed position of the moving target in each segment; (9) The reconstructed position of the moving target in each segment of the motion trajectory is spliced ​​together to obtain a complete motion trajectory reconstruction result.

2. The method for reconstructing moving target trajectories of wide-angle staring SAR based on Gauss-Newton method as claimed in claim 1, characterized in that: The step (2) of echoing comprises the following steps: Perform range pulse pressure and range-direction fast Fourier transform on the original echo signals of each channel to obtain the echo baseband signal of the moving target: s e (r,t m )=σg e sinc{2B[r-R e (t m ;r p )] / c}·exp[-j4πf c R e (t m ;r p ) / c] Among them, σ represents the amplitude of the echo, g e Represents the bidirectional antenna pattern of each channel. B, f c and c represent the bandwidth, carrier frequency and light speed of the echo respectively. r represents the slant range unit, t m represents slow time; r p Represents the actual position of the moving target; R e (t m ; r p ) represents the slant range from the carrier to the target; j represents the imaginary unit; exp(·) represents the exponential operation with the natural number e as the base; The original echo signals of each channel are subjected to fast Fourier transform in the azimuth dimension to realize azimuth compression and obtain the range-Doppler domain echo.

3. The method for reconstructing moving target trajectories of wide-angle staring SAR based on Gauss-Newton method as claimed in claim 1, characterized in that: The clutter suppression effect is proportional to the spatial degrees of freedom.

4. The method for reconstructing moving target trajectories of wide-angle staring SAR based on Gauss-Newton method as claimed in claim 1, characterized in that: The step (5) comprises the following steps: R(t m ; r p ) at the subaperture center t m =t k Taylor expansion: Where R(t m ; r p ) is the slant distance of the moving target from the carrier, which couples the position of the carrier and the motion information of the moving target; R′(t k ; r p ), R″(t k ; r p ), R″′(t k ; r p ), R″″(t k ; r p ) and ο(t m ; r p ) represent the first, second, third, fourth and higher order Taylor expansion coefficients of the phase history respectively; At this time, the echo expression of the moving target can be expressed as The above formula converts the phase history estimation into a polynomial phase parameter estimation. The polynomial phase parameter can be expanded into a form of adding multiple phase parameters, and the polynomial phase parameter is estimated by using the maximum likelihood algorithm.

5. The method for reconstructing moving target trajectories of wide-angle staring SAR based on Gauss-Newton method as claimed in claim 4, characterized in that: The step (6) comprises the following steps: Using the quadratic and cubic Taylor expansion coefficients R″(t k ; r p )、R″′(t k ; r p ), the improved algorithm of Gauss-Newton method is used to decouple the motion parameters. First, the motion parameter estimation model is established as shown below Among them, r a,i , v a,i and θ k,i Respectively represent the flight radius, speed and azimuth of the aircraft in the i-th sub-aperture, which can be obtained through the airborne GPS; x pk,i ,y pk,i Respectively represent the x-coordinate position and y-coordinate position of the moving target; represents the radial velocity of the moving target estimated by the i-th sub-aperture; The above motion parameter estimation model is converted into a least squares problem to obtain the motion parameters of the moving target: where x i Represents the motion parameters of the target within sub-aperture i.

6. The method for reconstructing moving target trajectories of wide-angle staring SAR based on Gauss-Newton method as claimed in claim 5, characterized in that: According to x i The motion parameters of the moving target are obtained as follows: in, They are the x-direction speed and y-direction speed of the moving target respectively; They are respectively the x-direction acceleration and the y-direction acceleration of the moving target.

7. The method for reconstructing moving target trajectories of wide-angle staring SAR based on Gauss-Newton method as claimed in claim 1, characterized in that: The step (7) comprises: Reconstruct the rough motion trajectory of the moving target based on the estimated motion parameters of the moving target Among them, x pk,1 ,y pk,1 They represent the initial x-coordinate position and initial y-coordinate position of the moving target respectively.

8. The method for reconstructing moving target trajectories of wide-angle staring SAR based on Gauss-Newton method as claimed in claim 1, characterized in that: Extracting the envelope of the moving target in step (4) comprises the following steps: Extract the envelope information of the echo, assuming that the echo is M units to the right along the azimuth r e ＝[r e (1)r e (2)...r e (M)] where r e Represents the complete envelope history of the echo; By extracting the phase of the peak echo in the range profile, the envelope history can be obtained based on the conversion relationship between phase and distance. where Angle[·] represents the phase extraction, λ represents the wavelength, and s(r,t m ) represents the echo signal at the peak position in the range profile.

9. The method for reconstructing moving target trajectories of wide-angle staring SAR based on Gauss-Newton method as claimed in claim 1, characterized in that: The step (8) comprises: The motion state of the moving target is modeled using a segmentation method. crs is the rough trajectory obtained in the previous step. ideal is the actual trajectory, l plane is the platform trajectory; the motion trajectory is evenly divided into K segments according to the time change, each of which contains a slow time unit Q = M / K; each sub-segment is recorded as l crs,1 ,l crs ,2,...,l crs ,k,...,l crs,K , the corresponding platform trajectory is recorded as l plane,1 ,l plane,2 ,...,l plane,k ,...,l plane,K . crs,k and l plane,k It can be expressed as: In the kth segment, the target moves at a constant speed; define the starting position (x k ,y k ) and speed (v x,k ,v y,k ), then define the target state variable x under uniform linear motion k It is expressed as follows x k =[x k ,y k ,v x,k ,v y,k ] T Based on the above target state variables, the measurement equation is: r k ＝||l plane,k -l k ||2 Among them l k Represents the motion state x k The following motion trajectory, Where Δt m Indicates a slow time interval; Building the first expression g1(x k )=r k -r e,k where r e,k =r e | m∈[1+(k-1)Q,kQ] ; In the connection of each sub-segment, a prediction mechanism is introduced to calculate the time unit n after the end of sub-segment k-1 p The target movement trend within the subsegment k is taken as the first n p The known trajectory within time units, Among them, l pre,k is the time unit n after the end of subsegment k-1 p The target movement trend within the The target position information and velocity information provided by the rough trajectory are introduced into the measurement equation. The target position information is g3(x k )=||l crs,k -l k ||2 According to the target speed information, the equation is established as Using the equations established above, the problem of solving the state parameters of the moving target is transformed into a least squares problem The Gauss-Newton method is used to solve the above least squares problem and obtain the state of the target in each trajectory segment.

10. A wide-angle staring SAR moving target trajectory reconstruction device based on the Gauss-Newton method, characterized in that: include: The original echo processing module is used to perform sub-aperture division on the original radar echo signal to obtain a multi-channel original echo signal; Performing two-dimensional compression on the original echo signals of each channel in the range and azimuth dimensions to obtain the range-Doppler domain echo of each channel; performing preprocessing in the range-Doppler domain based on the range-Doppler domain echo of each channel, wherein the preprocessing includes multi-channel registration and clutter suppression to obtain the range-Doppler domain echo after preprocessing; The parameter acquisition module extracts the phase history and envelope of the moving target based on the pre-processed range-Doppler domain echo and estimates the radial velocity of the moving target; The phase parameters of the moving target phase history are coupled with the carrier position and the moving target's motion information. The maximum likelihood algorithm is used to estimate the phase parameters of each order in the extracted phase history. Based on the relationship between each order of phase parameters and motion parameters, the Gauss-Newton method is used to decouple the motion parameters and obtain the motion parameters of the moving target. The position reconstruction module obtains the rough motion trajectory reconstruction result based on the motion parameters of the moving target; the rough motion trajectory of the moving target is segmented, and the extracted envelope is decoupled from the rough motion trajectory of each segment using the Gauss-Newton method to obtain the accurate reconstructed position of the moving target of each segment; The trajectory reconstruction module is used to splice the reconstructed position of the moving target in each segment of the motion trajectory to obtain a complete motion trajectory reconstruction result.

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