CN115097454A - Synthetic aperture radar incremental imaging processing method and system based on inverse whitening - Google Patents
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Abstract
The invention relates to a synthetic aperture radar incremental imaging processing method and a synthetic aperture radar incremental imaging processing system based on inverse whitening. The method comprises the following steps: acquiring radar working parameters and scene historical data of a current observation area; determining an echo signal matrix of the SAR according to the radar working parameters; constructing a matched filter for realizing distance compression according to the radar working parameters, completing pulse compression on the echo signal matrix according to the matched filter, and determining a distance compression signal; performing ground grid division on an observation scene, and superposing distance compression signals on the same range gate to determine a distance migration correction signal; constructing a sparse dictionary of a transform domain according to the scene historical data parameters; converting a complex signal reconstruction problem into a real signal reconstruction problem based on an inverse whitening process based on the range migration correction signal and the sparse dictionary based on a weight l 1 And performing phase compensation and coherent superposition by using the norm optimization method, and outputting a synthetic aperture radar image. The invention can improve the imaging efficiency.
Description
Technical Field
The invention relates to the field of microwave remote sensing, in particular to a synthetic aperture radar incremental imaging processing method and a synthetic aperture radar incremental imaging processing system based on inverse whitening.
Background
Synthetic Aperture Radar (SAR) is the dominant mode of microwave remote sensing today. The synthetic aperture radar modulates the echo signal in the azimuth direction by using the Doppler efficiency generated by the relative motion between the satellite and the target, so that the two-dimensional resolution capability of the synthetic aperture radar depends on the relative motion between the satellite and the target. The satellite-borne SAR has all-weather and all-time working capability, can be used for observing sunshine and cloud and rain conditions over an observation area, can be used for efficiently observing hot spots and key targets, and has irreplaceable important function. The SAR can perform high-efficiency detection through a multi-level situation perception technical means so as to realize rapid discovery, detection and identification of scene targets. The method is widely applied to disaster monitoring, environment monitoring, ocean monitoring, resource exploration, crop estimation and mapping, military affairs and other aspects.
The images observed two times before and after are not very different considering a static scene (e.g., an urban area). In addition, the Geosynchronous orbit Synthetic Aperture Radar (GEO SAR) has extremely short revisiting period and staring observation capability, so that the region of interest is continuously observed, and the difference of images observed in two times before and after is ensured to be small. Therefore, if the current scene is observed again, the observation does not need to be performed in the conventional sampling manner but only needs to observe the added parts because the basic situation of the scene is known. Since these increased portions are relatively small, i.e., sparse, during the revisit time of day, echo sampling for SAR satellites can be made well below the nyquist sampling rate. Therefore, the observation system of the SAR is simplified, and the echo data volume is greatly reduced. The means of imaging in this manner is referred to herein as incremental imaging. Incremental imaging is a mode of using historical data to assist observation and imaging, and the mode is very suitable for SAR observation of static scenes. However, to ensure that the increment between the two previous observations is sparse, it is necessary to ensure that the two previous scenes and the two subsequent scenes have comparability, which is specifically represented by time and space consistency: the time consistency ensures that the signal increment is sparse, and the space consistency ensures that the scattering characteristics of the observation scene are not greatly different.
Beeche et al proposed the idea of using the previous observation to assist the current observation for Imaging as early as 2015, and called Change Imaging, please refer to Radar Change Imaging With Undersampled Data Based on Matrix Completion and Bayesian Compressive Sensing. However, in that observation, the varying component is at the echo level, requiring subtraction from two echoes to obtain the varying part; because the scattering characteristic of the scene target is closely related to the observation angle, if the result obtained by subtracting twice contains an accurate change part, the geometrical consistency of the two previous and next observations needs to be ensured, and the harsh condition is difficult to apply to practical application. However, this approach of peeld et al also states that to measure the change of two observations before and after, the observation geometry needs to satisfy certain constraints. The most direct expression of the increment is in the airspace, however, the airspace increment imaging algorithm is very sensitive to the amplitude of the solved increment, and once an energy estimation error occurs, the algorithm fails, generally, the solved increment is different from a true value by several orders of magnitude, and is reflected on an image to be light and dark stripes, and the airspace increment imaging stability is low. This situation usually occurs in a scene containing a target with strong scattering property, and most of the energy is provided by the target with strong scattering property, so that the quantization matrix estimation is inaccurate, and the whole algorithm fails. For this situation, the processing method that can be adopted is to re-estimate the point after the fixed quantization coefficient, which obviously results in the decrease of the Imaging efficiency, specifically please refer to Synthetic Aperture radiation incorporation Imaging Based on Compressed Sensing.
Disclosure of Invention
The invention aims to provide a synthetic aperture radar incremental imaging processing method and a synthetic aperture radar incremental imaging processing system based on inverse whitening, so as to solve the problem of low imaging efficiency.
In order to achieve the purpose, the invention provides the following scheme:
an inverse whitening-based synthetic aperture radar incremental imaging processing method comprises the following steps:
acquiring radar working parameters and scene historical data of a current observation area; the radar working parameters comprise a transmitting pulse number, a distance sampling point number, a pulse repetition frequency, a distance direction sampling rate, a radar working bandwidth, a transmitting pulse width, a distance direction modulation frequency, a working wavelength, a sampling delay, a transposed matrix of a time matrix of a simulation center time radar transmitting pulse, a distance direction sampling time matrix from the opening time of a receiving window and a frequency point matrix of a distance frequency domain;
determining an echo signal matrix of the synthetic aperture radar SAR according to the radar working parameters;
constructing a matched filter for realizing distance compression according to the radar working parameters, completing pulse compression on the echo signal matrix according to the matched filter, and determining a distance compression signal;
performing ground grid division on an observation scene, and superposing distance compression signals on the same range gate to determine a distance migration correction signal;
constructing a sparse dictionary of a transform domain according to the scene historical data parameters;
converting a complex signal reconstruction problem into a real signal reconstruction problem based on an inverse whitening process based on the range migration correction signal and the sparse dictionary based on a weighting l 1 And performing phase compensation and coherent superposition by using the norm optimization method, and outputting a synthetic aperture radar image.
Optionally, the echo signal matrix is:
Optionally, the constructing a matched filter for implementing distance compression according to the radar working parameters, completing pulse compression on the echo signal matrix according to the matched filter, and determining a distance compressed signal specifically includes:
performing range Fourier transform on the echo signal matrix, and determining echo signals after range Fourier transform;
constructing a matched filter for realizing distance compression according to the frequency point matrix of the distance frequency domain and the distance directional frequency modulation;
multiplying each row of the echo signals subjected to the Fourier transform of the distance with the matched filter point by point respectively to determine a two-dimensional matrix after multiplication;
zero padding operation is carried out on the distance of the multiplied two-dimensional matrix towards two ends of the dimension, and the matrix after zero padding is determined;
and performing inverse Fourier transform on the zero-padded matrix to determine a distance compression signal.
Optionally, the performing ground mesh division on the observation scene, and superimposing the range compression signals on the same range gate to determine the range migration correction signal specifically includes:
carrying out ground network division on an observation scene, and determining the coordinates of each scene point;
setting a satellite sampling initial position, and determining a satellite sampling position coordinate according to the satellite sampling initial position;
determining the slope distance of all scene points at each sampling moment according to the scene point coordinates and the satellite sampling position coordinates;
determining time delay according to the slant distance, and obtaining a distance compression signal in the time delay time;
and constructing an empty matrix, summing the distance compression signals on the same range gate in the time delay time, putting the summation result into the empty matrix until the empty matrix is filled, and determining the filled matrix as a range migration correction signal.
Optionally, the complex signal reconstruction problem is converted into a real signal reconstruction problem based on an inverse whitening process according to the range migration correction signal and the sparse dictionary, and the problem is based on a weighting l 1 The norm optimization method carries out phase compensation and coherent superposition and outputs the synthetic aperture radar image, and specifically comprises the following steps:
determining an observation matrix according to the projection relation between the range migration correction signal and the scene;
initializing a target on a kth range gate in a scene in the transform domain according to the range migration correction signal, the observation matrix and the sparse dictionary, and determining the sparse dictionaries respectively aiming at a real part and an imaginary part according to a real part and an imaginary part of the initialized target; wherein k is a distance door serial number;
according to the sparse dictionary of the real part and the sparse dictionary of the imaginary part, re-representing the original echo signal and reconstructing an observation matrix;
based on weighting l 1 The norm optimization method comprises the steps of solving the k' th column of scenes of an echo signal matrix in the g +1 th time according to a re-expressed original echo signal and a reconstructed observation matrix, circularly judging each column of scenes according to an iteration criterion until the iteration criterion is met, and outputting a synthetic aperture radar image; wherein g is the number of iterations, and k' is the number of columns of the echo signal matrix.
Optionally, the weighting is based on 1 The norm optimization method includes solving a k' th column of scenes of the echo signal matrix in the g +1 th time according to a re-expressed original echo signal and a reconstructed observation matrix, circularly judging each column of scenes according to an iteration criterion until the iteration criterion is met, and outputting a synthetic aperture radar image, and specifically includes:
based on weighting l 1 The norm optimization method comprises the steps of estimating a weighting matrix of the (g +1) th time and a first process matrix constructed by the k' th column scene;
determining a second process matrix according to the reconstructed observation matrix and the first process matrix;
according to the second process matrix, the re-expressed original echo signals and the reconstructed observation matrix, solving a k' column scene of the echo signal matrix in the g +1 th time;
and circularly judging each column of scenes according to an iteration criterion until the iteration criterion is met, and outputting the synthetic aperture radar image.
An inverse-whitening based synthetic aperture radar incremental imaging processing system, comprising:
the radar working parameter and scene historical data acquisition module is used for acquiring radar working parameters and scene historical data of a current observation area; the radar working parameters comprise a transmitting pulse number, a distance sampling point number, a pulse repetition frequency, a distance direction sampling rate, a radar working bandwidth, a transmitting pulse width, a distance direction modulation frequency, a working wavelength, a sampling delay, a transposed matrix of a time matrix of a simulation center time radar transmitting pulse, a distance direction sampling time matrix from the opening time of a receiving window and a frequency point matrix of a distance frequency domain;
the echo signal matrix determining module is used for determining an echo signal matrix of the synthetic aperture radar SAR according to the radar working parameters;
the distance compression module based on matched filtering is used for constructing a matched filter for realizing distance compression according to the radar working parameters, completing pulse compression on the echo signal matrix according to the matched filter and determining a distance compression signal;
the distance migration correction module is used for carrying out ground grid division on an observation scene, superposing the distance compression signals on the same range gate and determining a distance migration correction signal;
the sparse dictionary construction module of the transform domain is used for constructing a sparse dictionary of the transform domain according to the scene historical data parameters;
a phase compensation and coherent superposition module for converting the complex signal reconstruction problem into a real signal reconstruction problem based on inverse whitening processing according to the range migration correction signal and the sparse dictionary, and based on weighting l 1 And performing phase compensation and coherent superposition by using the norm optimization method, and outputting a synthetic aperture radar image.
Optionally, the echo signal matrix is:
Optionally, the distance compression module based on matched filtering specifically includes:
the distance Fourier transform unit is used for performing distance Fourier transform on the echo signal matrix and determining echo signals subjected to the distance Fourier transform;
the matched filter construction unit is used for constructing a matched filter for realizing distance compression according to the frequency point matrix of the distance frequency domain and the distance modulation frequency;
a two-dimensional matrix determining unit, configured to multiply each row of the echo signal after the distance fourier transform with the matched filter point by point, and determine a two-dimensional matrix after multiplication;
a zero padding operation unit, configured to perform zero padding operation on the distance direction two ends of the multiplied two-dimensional matrix, and determine a zero-padded matrix;
and the distance compressed signal determining unit is used for performing distance inverse Fourier transform on the zero-padded matrix and determining a distance compressed signal.
Optionally, the range migration correction module specifically includes:
the coordinate determination unit of the scene points is used for carrying out ground network division on the observation scene and determining the coordinate of each scene point;
the satellite sampling position coordinate determination unit is used for setting a satellite sampling initial position and determining a satellite sampling position coordinate according to the satellite sampling initial position;
the slope distance determining unit is used for determining the slope distances of all scene points at each sampling moment according to the scene point coordinates and the satellite sampling position coordinates;
the distance compressed signal determining unit on the time delay time is used for determining the time delay according to the slant distance and obtaining a distance compressed signal on the time delay time;
and the distance migration correction signal determining unit is used for constructing an empty matrix, summing the distance compression signals on the same range gate in the time delay time, putting the summation result into the empty matrix until the empty matrix is filled, and determining the filled matrix as the distance migration correction signal.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the invention provides a synthetic aperture radar incremental imaging processing method and a system based on inverse whitening, firstly, the distance direction pulse compression is carried out, the gain is improved, the signal-to-noise ratio is improved, and the possibility of sparse reconstruction algorithm is increased; the additive solving problem is converted into the multiplicative problem based on the transform domain sparse incremental imaging, so that the imaging method is insensitive to the amplitude of the reconstruction result, the stability of imaging processing is improved, compared with an airspace incremental imaging algorithm, the transform domain sparse incremental imaging method is high in imaging efficiency and the imaging efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a synthetic aperture radar incremental imaging processing method based on inverse whitening provided by the present invention;
fig. 2 is a schematic view of an observation scene of the SAR system provided by the present invention;
FIG. 3 is a block diagram of an inverse whitening-based SAR incremental imaging processing system provided by the present invention;
FIG. 4 is a schematic diagram of the 3m resolution scene increment imaging result in the noise-free down-conversion domain provided by the present invention; FIG. 4(a) is a schematic diagram of the 3m resolution scene increment imaging result of a 10-fold down-sampling transform domain under noise-free condition provided by the present invention; FIG. 4(b) is a diagram of the results of the noise-free 3 m-resolution scene incremental imaging with 20 times down-sampling transform domain; FIG. 4(c) is a diagram of the noise-free 3m resolution scene increment imaging result of the down-sampling transform domain with 30 times;
FIG. 5 is a schematic diagram of a 1m resolution scene increment imaging result in a noise-free down-conversion domain provided by the present invention; FIG. 5(a) is a diagram of the result of the noise-free up-sampling transform domain 1m resolution scene increment imaging with 10 times; FIG. 5(b) is a diagram of the results of the noise-free up-sampling transform domain 1m resolution scene increment imaging with 20 times lower resolution provided by the present invention; fig. 5(c) is a schematic diagram of the noise-free 1 m-resolution scene increment imaging result of the down-sampling transform domain with 30 times.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide an incremental imaging processing method and system of a synthetic aperture radar based on inverse whitening, which can improve the imaging efficiency.
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof.
Fig. 1 is a flowchart of an incremental imaging processing method for a synthetic aperture radar based on inverse whitening according to the present invention, and as shown in fig. 1, the incremental imaging processing method for a synthetic aperture radar based on inverse whitening includes:
step 101: acquiring radar working parameters and scene historical data of a current observation area; the radar working parameters comprise a transmitting pulse number, a distance sampling point number, a pulse repetition frequency, a distance direction sampling rate, a radar working bandwidth, a transmitting pulse width, a distance direction modulation frequency, a working wavelength, a sampling delay, a transposed matrix of a time matrix of a simulation center time radar transmitting pulse, a distance direction sampling time matrix from the opening time of a receiving window and a frequency point matrix of a distance frequency domain.
The radar operating parameter is f 1 ＝{N a ,N r ,PRF,F s ,B r ,T p ,K r ,λ,t d ,t center Ta, tr, fr }, historical data parameters of the current observation region
N a Is the number of transmitted pulses, N r Is the number of distance sampling points, PRF is the pulse repetition frequency, F s Is the range-wise sampling rate, B r Is the radar operating bandwidth, T p Is the transmission pulse width, K r Is the distance-modulated frequency, λ is the operating wavelength, t d For sample delay, t center For the simulation of the center time, ta ═ η 1 ,η 2 ,...,η m ,...η Na ] T Wherein eta m ＝(m-1-N a the/2)/PRF represents the moment at which the radar transmits the m-th pulse [. ]] T Representing a transpose operation. tr ═ τ 1 ,τ 2 ,…τ k ,…,τ Nr ]，fr＝[f τ,1 ,f τ,2 ,…f τ,k ,…,f τ,Nr ]In which τ is k ＝t d +(k-1)/F s Denotes the kth distance from the receive window opening time to the sampling instant, f τ,k ＝-F s /2+(k-1)·F s /N r Denotes the kth frequency point in the distance frequency domain, k being 1,2 … …, N r 。
Step 102: and determining an echo signal matrix of the synthetic aperture radar SAR according to the radar working parameters.
The echo pixel points of the SAR are two-dimensional matrixes:
In addition, theFor convenience of presentation, S is echo A column vector S, also denoted as element row vector echo ＝[S echo,1,col … S echo,m,col … S echo,Na,col ] T And a row vector S whose elements are column vectors echo ＝[S echo,row,1 … S echo,row,k … S echo,row,Nr ]。
Wherein S echo,m,col ＝[S echo,m,1 … S echo,m,k … S echo,m,Nr ]Denotes S echo Row m, S of echo,row,k ＝[S echo,1,k … S echo,m,k … S echo,Na,k ] T Denotes S echo The k-th column of (1).
Step 103: and constructing a matched filter for realizing distance compression according to the radar working parameters, completing pulse compression on the echo signal matrix according to the matched filter, and determining a distance compression signal.
The step 103 specifically includes: performing range Fourier transform on the echo signal matrix, and determining echo signals after range Fourier transform; constructing a matched filter for realizing distance compression according to the frequency point matrix of the distance frequency domain and the distance modulation frequency; multiplying each row of the echo signals subjected to the Fourier transform of the distance with the matched filter point by point respectively to determine a two-dimensional matrix after multiplication; zero padding operation is carried out on the distance of the multiplied two-dimensional matrix towards two ends of the dimension, and the matrix after zero padding is determined; and performing inverse Fourier transform on the zero-padded matrix to determine a distance compression signal.
In practical application, the echo S of SAR is used echo Making a distance Fourier change, and recording the signal after the distance Fourier change as S echo,r_fft Then, a matched filter for realizing distance compression is constructed, and the steps are as follows:
(1) firstly, the echo signal matrix S echo The distance-to-Fourier transform is carried out, the Fourier transform and the Fast Fourier Transform (FFT) are different, and the specific operation method comprises the following steps:
extraction of S echo Of the first row of the matrixFront N r 2 element and N r And/2 element transposition positions, and then FFT is carried out on the row matrix after the transposition positions, wherein the FFT method is shown in section 9.6 of the lower book of 'Signal and System' edited by Zhengjunli et al. And after the FFT operation is finished, exchanging the first half element and the second half element of the row matrix after the FFT again, namely finishing the Fourier transform of the distance of the first row data of the echo signal. Sequentially carrying out the above operation on each row of data of the echo matrix to finish the operation of carrying out distance Fourier transformation on the original echo signal matrix, and recording the signal after the distance Fourier transformation as S echo,r_fft 。
(2) Then, a matched filter for realizing distance compression is constructed, and the filter expression is as follows:
will S echo,r_fft Each row of (a) is respectively connected with H rc Multiplying point by point, and recording the multiplied two-dimensional matrix as S echo,rc_fft Then to S echo,rc_fft The distance of (2) is zero-filled by alpha times to two ends of the dimension, and the matrix after zero-filling is
And performing inverse Fourier transform on the distance to complete pulse compression, and recording the signal after completing the distance compression as S echo,rc ，S echo,rc I.e. the output variable of this module. The inverse distance fourier transform method is the same as the step of distance fourier transform, except that the FFT function used in the process is replaced by an IFFT function.
Step 104: and performing ground grid division on the observation scene, and superposing the distance compression signals on the same range gate to determine a distance migration correction signal.
The step 104 specifically includes: carrying out ground network division on an observation scene, and determining the coordinates of each scene point; setting a satellite sampling initial position, and determining a satellite sampling position coordinate according to the satellite sampling initial position; determining the slant distance of all scene points at each sampling moment according to the scene point coordinates and the satellite sampling position coordinates; determining time delay according to the slant distance, and obtaining a distance compression signal in the time delay time; and constructing an empty matrix, summing the distance compression signals on the same range gate in the time delay time, putting the summation result into the empty matrix until the empty matrix is filled, and determining the filled matrix as a range migration correction signal.
FIG. 2 is a schematic view of an observation scene of the SAR system provided by the present invention, as shown in FIG. 2, taking a region not smaller than the size of the observation scene with the center point of the reference scene as the center, meshing the region, assuming that there are N points along the x axis and N points along the y axis, and the distance between the meshes is less than or equal to the azimuth resolution ρ a And the spacing between grids is recorded as l, l is less than or equal to rho a However, the grid interval l should not be too small, because too small a grid interval cannot optimize the reconstruction performance, and further affects the efficiency of the algorithm. According to scene center coordinates (x) n ,y n 0), the coordinates of each scene point can be found as
Assume a satellite sampling initial position of [ x ] track ,0,H]Then the sequentially sampled position coordinates of the satellite are { [ x ] track +V e η 1 ,0,H],[x track +V e η 2 ,0,H],…,[x track +V e η m ,0,H],…[x track +V e η Na ,0,H]Where η is assumed 1 Is the zero time of the space-time pseudorandom sampling. Finally, the slope distance r (eta) of all scene points at each sampling moment is calculated according to the scene point coordinates and the satellite sampling position coordinates m )。
In the present invention, the "spaceborne space geometry relationship" refers to the synthetic aperture radar satellite, Wech bolt, et al, first edition in 2001, 2 months, page 132-135.
In the present inventionCalculating any distance r in a rotating geocentric coordinate system i (η m ) With respect to said r i (η m ) The calculation refers to the synthetic aperture radar satellite, Wech bolt, et al, first edition 2/2001, formulas (7.15), (7.16) and (7.18) at pages 135-137. While said r (η) m ) The time difference between the over-the-center time and the SAR system turn-on time needs to be considered.
At each azimuth sampling instant η m Then, the slope coefficient of all scene points at the sampling time is calculated to be a two-dimensional matrix
Wherein r is n,n Representing a crossing time η m The slope coefficient of the (n, n) th target in the layout scene target, namely the slope coefficient matrix r (eta) m ) The nth row and nth column. In addition, for the convenience of representation, r is n Column vector also denoted as element row vector
And a row vector whose elements are column vectors
Wherein
Denotes r (. eta.) ( m ) The (c) th row (c) of (a),
is represented by r (eta) m ) The nth column of (1).
Then, the time delay is calculated according to the skew distance, i.e. r (eta) m ) Where c is the speed of light, and then find the data distance compressed signal S over this delay time echo,rc (r(η m ) And/2 c). In addition, a Na N empty matrix is constructed, which is marked as S echo,rcm . The data on the same range gate are summed and superposed into S echo,rcm For example, in the case of the first range gate, i.e. n is 1, all the lines are arranged with n being 1Taking out data of scene points, summing and overlapping the data and putting the data into S echo,rcm (η n ) In the first column of the nth row, when the distance gate is the second distance gate, the data of all the arranged scene points with n-2 are taken out, summed, superposed and put into the S echo,rcm (η n ) And in the second column of the nth row, the analogy is repeated, and the range migration correction is completed. Recording distance migration corrected signal is recorded as S echo,rcm ，S echo,rcm I.e. the output variable of this step.
Step 105: and constructing a sparse dictionary of a transform domain according to the scene historical data parameters.
The variable space of the transform domain is denoted as D ═ D 1 ,D 2 ,…,D n ,…,D N }，D n ＝[β n,1 ,β n,2 ,…,β n,n ,…,β n,N ] T The atoms in the variable space are orthogonal to each other. Where D is the tensor space of the transform domain, D n For the nth dimension of the transform domain tensor space, beta n,n Is the (N, N) th element in the tensor space, N belongs to [1, N ]]. Historical data of current observation scene
Wherein σ 0 For the historical observation data of the current scene,
is the nth element value in the historical observation scene. Without loss of generality, here the first atom of the variable space is chosen as historical data, i.e.
The invention adopts a simplest variable space construction method, selects the p-th element as an axis and leads beta to be n,p Is a constant value, generally chosen to be 1, i.e.: when the p-th row in matrix D is selected as the axis, the numbers on this row are all 1.
Then the sparse dictionary of the transform domain is
Wherein,
representing the p element value in the historical observation scene;
representing the p-1 th element value in the historical observation scene;
representing the p +1 th element value in the historical observation scene.
Step 106: converting a complex signal reconstruction problem into a real signal reconstruction problem based on an inverse whitening process based on the range migration correction signal and the sparse dictionary based on a weighting l 1 And performing phase compensation and coherent superposition by using the norm optimization method, and outputting a synthetic aperture radar image.
The step 106 specifically includes: determining an observation matrix according to the projection relation between the range migration correction signal and the scene; initializing a target on a kth range gate in a scene in the transform domain according to the range migration correction signal, the observation matrix and the sparse dictionary, and determining the sparse dictionaries respectively aiming at a real part and an imaginary part according to a real part and an imaginary part of the initialized target; wherein k is a distance door serial number; according to the sparse dictionary of the real part and the sparse dictionary of the imaginary part, re-representing the original echo signal and reconstructing an observation matrix; based on weighting l 1 The norm optimization method comprises the steps of solving the k' th column of scenes of an echo signal matrix in the g +1 th time according to a re-expressed original echo signal and a reconstructed observation matrix, circularly judging each column of scenes according to an iteration criterion until the iteration criterion is met, and outputting a synthetic aperture radar image; wherein g is the number of iterations.
The basis weight/ 1 The norm optimization method comprises the steps of solving the k' th column scene of the echo signal matrix in the g +1 th time according to the re-represented original echo signal and the reconstructed observation matrix, circularly judging each column scene according to an iteration criterion until the iteration criterion is met, and outputting the synthetic aperture radar until the iteration criterion is metThe image acquisition method specifically comprises the following steps: based on weighting l 1 The norm optimization method comprises the steps of estimating a weighting matrix of the (g +1) th time and a first process matrix constructed by the k' th column scene; determining a second process matrix according to the reconstructed observation matrix and the first process matrix; according to the second process matrix, the re-expressed original echo signals and the reconstructed observation matrix, solving a k' column scene of the echo signal matrix in the g +1 th time; and circularly judging each column of scenes according to an iteration criterion until the iteration criterion is met, and outputting the synthetic aperture radar image.
In practical application, (1) based on the echo S echo,rcm And the scene sigma, the projection relationship between the two is first established, i.e. the observation matrix a.
In the invention, at different sampling moments, for different scene points, the form of the obtained observation matrix A is expressed as:
n represents the total number of scene points on the same range gate.
N is a variable, and ranges from 1 to N, and is expressed as the distance to the nth scene point.
Wherein, a 1,1 The azimuth information of the 1 st scene point received by the SAR system at the 1 st azimuth moment acquired in the azimuth direction is shown.
a 1,2 Indicating that the SAR system receives the azimuth information of the 2 nd scene point at the 1 st azimuth moment acquired in the azimuth direction.
a 1,n The azimuth information of the nth scene point is received by the SAR system at the 1 st azimuth moment acquired in the azimuth direction.
a 1,N The azimuth information of the Nth scene point is received by the SAR system at the 1 st azimuth moment acquired in the azimuth direction.
a 2,1 Indicating that the SAR system receives the azimuth information of the 1 st scene point at the 2 nd azimuth time acquired in the azimuth direction.
a m,1 And the azimuth information of the 1 st scene point is received by the SAR system at the mth azimuth moment acquired in the azimuth direction.
a Na,1 And the azimuth information of the 1 st scene point received by the SAR system at the Na-th azimuth moment acquired in the azimuth direction is shown.
d 2,2 Indicating that the SAR system receives the azimuth information of the 2 nd scene point at the 2 nd azimuth moment acquired in the azimuth direction.
a m,2 Indicating that the SAR system receives the azimuth information of the 2 nd scene point at the mth azimuth moment acquired in the azimuth direction.
a Na,2 Indicating that the SAR system receives azimuth information of the 2 nd scene point at the Na-th azimuth moment acquired in the azimuth direction.
a 2,n Indicating that the SAR system receives the azimuth information of the nth scene point at the 2 nd azimuth moment acquired in the azimuth direction.
a m,n And indicating that the SAR system receives azimuth information of the nth scene point at the mth azimuth moment acquired in the azimuth direction.
a Na,n The azimuth information of the nth scene point received by the SAR system at the Na-th azimuth moment acquired in the azimuth direction is shown.
a 2,n And the SAR system receives the azimuth information of the nth scene point at the 2 nd azimuth moment acquired in the azimuth direction.
a m,N And indicating that the SAR system receives azimuth information of the Nth scene point at the mth azimuth moment acquired in the azimuth direction.
a Na,N And indicating that the SAR system receives azimuth information of the Nth scene point at the Nth azimuth moment acquired in the azimuth direction.
In the present invention, considering that the SAR system is controlled by the antenna system in actual operation, the observation matrix a is represented by a control matrix determined by the antenna and a doppler motion matrix G, where a is G W, where "x" is a multiplication of corresponding elements of the matrix, G is a matrix caused by doppler motion, and W is a control matrix caused by the phased array antenna.
The matrix G caused by doppler motion is represented as:
wherein epsilon is an imaginary unit, lambda is the wavelength of the SAR system, and the value of pi is 3.1415.
r 1,1 The distance from the SAR system to the 1 st scene point at the 1 st azimuth time acquired in the azimuth direction is shown.
r 1,2 The distance from the SAR system to the 2 nd scene point at the 1 st azimuth time acquired in the azimuth direction is shown.
r 1,n The distance from the SAR system to the nth scene point at the 1 st azimuth moment acquired in the azimuth direction is shown.
r 1,N The distance from the SAR system to the Nth scene point at the 1 st azimuth moment acquired in the azimuth direction is shown.
r 2,1 The distance of the SAR system to the 1 st scene point at the 2 nd azimuth time acquired in the azimuth direction is shown.
r m,1 The distance from the SAR system to the 1 st scene point at the mth azimuth time acquired in the azimuth direction is shown.
r Na,1 And the distance from the SAR system to the 1 st scene point at the Na-th azimuth moment acquired in the azimuth direction is shown.
r 2,2 The distance from the SAR system to the 2 nd scene point at the 2 nd azimuth time acquired in the azimuth direction is shown.
r m,2 The distance of the SAR system to the 2 nd scene point at the mth azimuth moment acquired in the azimuth direction is shown.
r Na,2 The distance from the SAR system to the 2 nd scene point at the Na-th azimuth time acquired in the azimuth direction is shown.
r 2,n The distance from the SAR system to the nth scene point at the 2 nd azimuth moment acquired in the azimuth direction is shown.
r m,n Indicating the direction along the azimuthAnd the distance from the SAR system to the nth scene point at the mth azimuth moment is acquired.
r Na,n And the distance from the SAR system to the nth scene point at the Na-th azimuth moment acquired in the azimuth direction is represented.
r 2,N And the distance from the SAR system to the Nth scene point at the 2 nd azimuth moment acquired in the azimuth direction is represented.
r m,N And the distance from the SAR system to the Nth scene point at the mth azimuth moment acquired in the azimuth direction is represented.
r Na,N And the distance from the SAR system to the Nth scene point at the Na-th azimuth moment acquired in the azimuth direction is represented.
In the invention, any one of the distances r is calculated in a non-rotating geocentric coordinate system m,n With respect to said r m,n The calculation refers to the synthetic aperture radar satellite, Welch bolt, and the like, first edition 2 month 2001, and formulas (7.15), (7.16), and (7.18) at pages 135-. While said r m,n The time difference between the over-the-center time and the SAR system turn-on time needs to be considered.
The control matrix W resulting from the phased array antenna is represented as:
m is a variable, and ranges from 1 to Na, and is expressed as the m-th sampling point of the azimuth direction.
N is a variable, and ranges from 1 to N, and is expressed as the distance to the nth scene point.
w 1,1 And the method shows whether the 1 st scene point can be observed by the antenna main lobe of the SAR system at the 1 st azimuth moment acquired in the azimuth direction.
w 1,2 And the method shows whether the 2 nd scene point can be observed by the antenna main lobe of the SAR system at the 1 st azimuth moment acquired in the azimuth direction.
w 1,n And the method shows whether the antenna main lobe of the SAR system at the 1 st azimuth moment acquired in the azimuth direction can observe the nth scene point.
w 1,N And the judgment result shows whether the antenna main lobe of the SAR system at the 1 st azimuth moment acquired in the azimuth direction can observe the Nth scene point.
w 2,1 And the method shows whether the 1 st scene point can be observed by the antenna main lobe of the SAR system at the 2 nd azimuth moment acquired in the azimuth direction.
w m,1 And the method shows whether the 1 st scene point can be observed by the antenna main lobe of the SAR system at the m azimuth moment acquired in the azimuth direction.
w Na,1 And the method shows whether the 1 st scene point can be observed by the antenna main lobe of the SAR system at the Na-th azimuth moment acquired in the azimuth direction.
w 2,2 And the 2 nd azimuth point is shown whether the antenna main lobe of the SAR system at the 2 nd azimuth moment acquired in the azimuth direction can observe the 2 nd scene point.
w m,2 Indicating whether the antenna main lobe of the SAR system at the mth azimuth moment acquired upwards along the azimuth can observe the 2 nd scene point or not;
w Na,2 and the judgment result shows whether the 2 nd scene point can be observed by the antenna main lobe of the SAR system at the Na-th azimuth moment acquired in the azimuth direction.
w 2,n And the judgment result shows whether the nth scene point can be observed by the antenna main lobe of the SAR system at the 2 nd azimuth moment acquired in the azimuth direction.
w m,n And the judgment result shows whether the nth scene point can be observed by the antenna main lobe of the SAR system at the mth azimuth moment acquired in the azimuth direction.
w Na,n And the judgment result shows whether the nth scene point can be observed by the antenna main lobe of the SAR system at the Na-th azimuth moment acquired in the azimuth direction.
w 2,N And the judgment result shows whether the antenna main lobe of the SAR system at the 2 nd azimuth moment acquired in the azimuth direction can observe the Nth scene point or not.
w m,N And the judgment result shows whether the antenna main lobe of the SAR system at the mth azimuth moment acquired in the azimuth direction can observe the Nth scene point or not.
w Na,N Showing the Na-th azimuth time SAR system acquired in the azimuth directionWhether the nth scene point can be observed by the main lobe of the antenna.
In the present invention, for w m,n The value of (d) represents the gain of the antenna main lobe of the SAR system at the nth scene point at the mth sampling instant.
(2) The rotation matrix Q is constructed such that the real and imaginary parts of the scene data are approximately equal. The rotation matrix Q is expressed as
(3) Initialization
Initializing a target w on the kth range gate in a scene in the transform domain n 0 ＝[ω n,1 ,…,ω n,n ,…,ω n,N ] T ＝(AD) H S echo,rcm,n Obtaining dictionaries for the real part and the imaginary part respectively according to the real part and the imaginary part judgment signs of the initialized target, which can be written as
Wherein sgn (-) represents the sign of the variable, real (-) represents the real part of the variable, and imag (-) represents the imaginary part of the variable. Rewriting the original echo signal as y ═ real (S) echo,rcm,n )imag(S echo,rcm,n )] T Reconstruction matrix is rewritten as
T＝[D real D imag ] T And E ═ RPT. Wherein, R represents an observation matrix in the reconstruction process after the complex signal is changed into the real signal, and T represents a transformation matrix after inverse whiteningAnd E is a reconstruction matrix after inverse whitening.
(4) Respectively constructing a weighting matrix xi of the (g +1) th time according to the estimated k' th column scene n (g+1) First process matrix U n (g+1) And a second process matrix H n (g+1)
According to the reconstruction matrix E, the weighting matrix xi n (g+1) And a first process matrix U n (g+1) Solving a second process matrix H n (g+1) ＝2E H E+γ 1 U n (g) ξ n (g) Wherein γ is 1 Representing a regularization factor selected based on empirical values; iota and iota
Are respectively set to 10 -3 And 10 -6 。
(5) According to the second process matrix solved in the last step, the matrix E and the original echo signal y which is re-expressed are reconstructed, and the k' th column of the (g +1) th time is solved to be sigma n (g+1) ＝2(H n (g+1) ) -1 E H y, wherein [ ·] -1 Representing an inversion operation.
(6) According to the iteration criterion, performing judgment circulation on each column of scenes until the iteration criterion is met, completing the iteration, and recording as W image . Let S be PD, then the final SAR imaging result is SW image . A fixed number of iterations or minimum mean square error is generally selected as the iteration criterion.
Fig. 3 is a structural diagram of an inverse-whitening-based synthetic aperture radar incremental imaging processing system provided in the present invention, and as shown in fig. 3, an inverse-whitening-based synthetic aperture radar incremental imaging processing system includes:
a radar working parameter and scene history data obtaining module 301, configured to obtain a radar working parameter and scene history data of a current observation area; the radar working parameters comprise a transmitting pulse number, a distance sampling point number, a pulse repetition frequency, a distance direction sampling rate, a radar working bandwidth, a transmitting pulse width, a distance direction modulation frequency, a working wavelength, a sampling delay, a transposed matrix of a time matrix of a simulation center time radar transmitting pulse, a distance direction sampling time matrix from the opening time of a receiving window and a frequency point matrix of a distance frequency domain.
And an echo signal matrix determining module 302, configured to determine an echo signal matrix of the synthetic aperture radar SAR according to the radar operating parameter.
And the distance compression module 303 based on matched filtering is used for constructing a matched filter for realizing distance compression according to the radar working parameters, completing pulse compression on the echo signal matrix according to the matched filter and determining a distance compression signal.
The distance compression module 303 based on matched filtering specifically includes: the distance Fourier transform unit is used for performing distance Fourier transform on the echo signal matrix and determining echo signals subjected to the distance Fourier transform; the matched filter construction unit is used for constructing a matched filter for realizing distance compression according to the frequency point matrix of the distance frequency domain and the distance modulation frequency; a two-dimensional matrix determining unit, configured to multiply each row of the echo signal after the distance fourier transform with the matched filter point by point, and determine a two-dimensional matrix after multiplication; a zero padding operation unit, configured to perform zero padding operation on the distance direction two ends of the multiplied two-dimensional matrix, and determine a matrix after zero padding; and the distance compressed signal determining unit is used for performing distance inverse Fourier transform on the zero-padded matrix and determining a distance compressed signal.
And the distance migration correction module 304 is used for performing ground grid division on the observation scene, superposing the distance compression signals on the same range gate and determining a distance migration correction signal.
And a transform domain sparse dictionary constructing module 305, configured to construct a transform domain sparse dictionary according to the scene history data parameters.
Phase compensation and coherent superpositionAn adding module 306 for converting a complex signal reconstruction problem into a real signal reconstruction problem based on an inverse whitening process according to the range migration correction signal and the sparse dictionary, based on a weighting l 1 And performing phase compensation and coherent superposition by using the norm optimization method, and outputting a synthetic aperture radar image.
The echo signal matrix is:
The range migration correction module 304 specifically includes: the coordinate determination unit of the scene points is used for carrying out ground network division on the observation scene and determining the coordinate of each scene point; the satellite sampling position coordinate determination unit is used for setting a satellite sampling initial position and determining a satellite sampling position coordinate according to the satellite sampling initial position; the slope distance determining unit is used for determining the slope distances of all scene points at each sampling moment according to the scene point coordinates and the satellite sampling position coordinates; the distance compressed signal determining unit on the time delay time is used for determining the time delay according to the slant distance and obtaining a distance compressed signal on the time delay time; and the distance migration correction signal determining unit is used for constructing an empty matrix, summing the distance compression signals on the same range gate in the time delay time, putting the summation result into the empty matrix until the empty matrix is filled, and determining the filled matrix as the distance migration correction signal.
Simulation implementation parameters are shown in table 1, fig. 4 is a schematic diagram of a 3 m-resolution scene incremental imaging result of a noise-free down-conversion domain provided by the present invention, as shown in fig. 4, a scene 1 is set at a resolution of 3m, and represents a low-resolution image with rich texture features, and the main features include urban buildings, rivers, grasslands, and the like; fig. 5 is a schematic diagram of a noise-free down-conversion domain 1m resolution scene increment imaging result provided by the present invention, as shown in fig. 5, a scene 2 is set in a scene of a college road near a new main building of north navigation, and the resolution 1m represents a structural artificial target such as a building. In order to better show the reconstruction effect, the reconstruction performance can be determined by directly comparing the reconstruction result, and the comparison with the original standard image can be found.
TABLE 1 simulation parameters Table
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. An inverse whitening-based synthetic aperture radar incremental imaging processing method is characterized by comprising the following steps:
acquiring radar working parameters and scene historical data of a current observation area; the radar working parameters comprise a transmitting pulse number, a distance sampling point number, a pulse repetition frequency, a distance direction sampling rate, a radar working bandwidth, a transmitting pulse width, a distance direction modulation frequency, a working wavelength, a sampling delay, a transposed matrix of a time matrix of a simulation center time radar transmitting pulse, a distance direction sampling time matrix from the opening time of a receiving window and a frequency point matrix of a distance frequency domain;
determining an echo signal matrix of the synthetic aperture radar SAR according to the radar working parameters;
constructing a matched filter for realizing distance compression according to the radar working parameters, completing pulse compression on the echo signal matrix according to the matched filter, and determining a distance compression signal;
performing ground gridding division on an observation scene, and superposing distance compression signals on the same range gate to determine a distance migration correction signal;
constructing a sparse dictionary of a transform domain according to the scene historical data parameters;
converting a complex signal reconstruction problem into a real signal reconstruction problem based on an inverse whitening process based on the range migration correction signal and the sparse dictionary based on a weighting l 1 And performing phase compensation and coherent superposition by using the norm optimization method, and outputting a synthetic aperture radar image.
2. The inverse-whitening based synthetic aperture radar incremental imaging processing method of claim 1, wherein the echo signal matrix is:
3. The method as claimed in claim 1, wherein the step of constructing a matched filter for implementing range compression according to the radar operating parameters, performing pulse compression on the echo signal matrix according to the matched filter, and determining a range-compressed signal includes:
performing range Fourier transform on the echo signal matrix, and determining echo signals after range Fourier transform;
constructing a matched filter for realizing distance compression according to the frequency point matrix of the distance frequency domain and the distance modulation frequency;
multiplying each row of the echo signals subjected to the Fourier transform of the distance with the matched filter point by point respectively to determine a two-dimensional matrix after multiplication;
zero filling operation is carried out on the distance of the multiplied two-dimensional matrix towards two ends of the dimension, and the matrix after zero filling is determined;
and performing inverse Fourier transform on the zero-padded matrix to determine a distance compression signal.
4. The synthetic aperture radar incremental imaging processing method based on inverse whitening according to claim 1, wherein the ground meshing is performed on the observation scene, and the range-compressed signals on the same range gate are superimposed to determine the range migration correction signal, specifically comprising:
carrying out ground network division on an observation scene, and determining the coordinates of each scene point;
setting a satellite sampling initial position, and determining a satellite sampling position coordinate according to the satellite sampling initial position;
determining the slant distance of all scene points at each sampling moment according to the scene point coordinates and the satellite sampling position coordinates;
determining time delay according to the slant distance, and obtaining a distance compression signal in the time delay time;
and constructing an empty matrix, summing the distance compression signals on the same range gate in the time delay time, putting the summation result into the empty matrix until the empty matrix is filled, and determining the filled matrix as a range migration correction signal.
5. The inverse-whitening based synthetic aperture radar incremental imaging processing method of claim 1, wherein the inverse-whitening based synthetic aperture radar incremental imaging processing converts a complex signal reconstruction problem into a real signal reconstruction problem based on the distance migration correction signal and the sparse dictionary based on a weighted/ 1 The norm optimization method performs phase compensation and coherent superposition, and outputs a synthetic aperture radar image, and specifically comprises the following steps:
determining an observation matrix according to the projection relation between the range migration correction signal and the scene;
initializing a target on a kth' range gate in a scene in the transform domain according to the range migration correction signal, the observation matrix and the sparse dictionary, and determining the sparse dictionaries respectively aiming at a real part and an imaginary part according to a real part and an imaginary part of the initialized target; wherein k' is a distance gate serial number;
according to the sparse dictionary of the real part and the sparse dictionary of the imaginary part, re-representing the original echo signal and reconstructing an observation matrix;
based on weighting l 1 The norm optimization method comprises the steps of solving the k' th column of scenes of an echo signal matrix in the g +1 th time according to a re-expressed original echo signal and a reconstructed observation matrix, circularly judging each column of scenes according to an iteration criterion until the iteration criterion is met, and outputting a synthetic aperture radar image; wherein g is the number of iterations.
6. The inverse-whitening based synthetic aperture radar incremental imaging processing method of claim 5, wherein the weighting based is based on/ 1 The norm optimization method includes solving a k' th column of scenes of the echo signal matrix in the g +1 th time according to a re-expressed original echo signal and a reconstructed observation matrix, circularly judging each column of scenes according to an iteration criterion until the iteration criterion is met, and outputting a synthetic aperture radar image, and specifically includes:
based on weighting l 1 Norm optimization method, estimating g +1 weighting matrix constructed by k' column scene and the first processA matrix;
determining a second process matrix according to the reconstructed observation matrix and the first process matrix;
according to the second process matrix, the re-expressed original echo signals and the reconstructed observation matrix, solving a k' column scene of the echo signal matrix in the g +1 th time;
and circularly judging each column of scenes according to an iteration criterion until the iteration criterion is met, and outputting the synthetic aperture radar image.
7. An inverse-whitening based synthetic aperture radar incremental imaging processing system, comprising:
the radar working parameter and scene historical data acquisition module is used for acquiring radar working parameters and scene historical data of a current observation area; the radar working parameters comprise a transmitting pulse number, a distance sampling point number, a pulse repetition frequency, a distance direction sampling rate, a radar working bandwidth, a transmitting pulse width, a distance direction modulation frequency, a working wavelength, a sampling delay, a transposed matrix of a time matrix of a simulation center time radar transmitting pulse, a distance direction sampling time matrix from the opening time of a receiving window and a frequency point matrix of a distance frequency domain;
the echo signal matrix determining module is used for determining an echo signal matrix of the synthetic aperture radar SAR according to the radar working parameters;
the distance compression module based on matched filtering is used for constructing a matched filter for realizing distance compression according to the radar working parameters, completing pulse compression on the echo signal matrix according to the matched filter and determining a distance compression signal;
the distance migration correction module is used for carrying out ground grid division on an observation scene, superposing the distance compression signals on the same range gate and determining a distance migration correction signal;
the sparse dictionary construction module of the transform domain is used for constructing a sparse dictionary of the transform domain according to the scene historical data parameters;
phase compensation and coherent addition module forConverting a complex signal reconstruction problem into a real signal reconstruction problem based on an inverse whitening process based on the range migration correction signal and the sparse dictionary based on a weighting l 1 And performing phase compensation and coherent superposition by using the norm optimization method, and outputting a synthetic aperture radar image.
8. The inverse-whitening based synthetic aperture radar incremental imaging processing system of claim 7, wherein the echo signal matrix is:
9. The inverse-whitening-based synthetic aperture radar incremental imaging processing system of claim 7, wherein the matched filtering-based distance compression module specifically comprises:
the distance Fourier transform unit is used for performing distance Fourier transform on the echo signal matrix and determining echo signals subjected to the distance Fourier transform;
the matched filter construction unit is used for constructing a matched filter for realizing distance compression according to the frequency point matrix of the distance frequency domain and the distance direction modulation frequency;
a two-dimensional matrix determining unit, configured to multiply each row of the echo signal after the distance fourier transform with the matched filter point by point, and determine a two-dimensional matrix after multiplication;
a zero padding operation unit, configured to perform zero padding operation on the distance direction two ends of the multiplied two-dimensional matrix, and determine a zero-padded matrix;
and the distance compressed signal determining unit is used for performing distance inverse Fourier transform on the zero-padded matrix and determining a distance compressed signal.
10. The inverse-whitening-based synthetic aperture radar incremental imaging processing system of claim 7, wherein the range migration correction module specifically comprises:
the coordinate determination unit of the scene points is used for carrying out ground network division on the observation scene and determining the coordinate of each scene point;
the satellite sampling position coordinate determination unit is used for setting a satellite sampling initial position and determining a satellite sampling position coordinate according to the satellite sampling initial position;
the slope distance determining unit is used for determining the slope distances of all scene points at each sampling moment according to the scene point coordinates and the satellite sampling position coordinates;
the distance compressed signal determining unit on the time delay time is used for determining the time delay according to the slant distance and obtaining a distance compressed signal on the time delay time;
and the distance migration correction signal determining unit is used for constructing an empty matrix, summing the distance compression signals on the same range gate in the time delay time, putting the summation result into the empty matrix until the empty matrix is filled, and determining the filled matrix as the distance migration correction signal.
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