A low oversampling Staggered SAR self-focusing method, system, medium and product based on Lucy-Richardson algorithm

Abstract

The present invention discloses a low oversampling Staggered SAR self-focusing method, system, medium and product based on Lucy-Richardson algorithm, which relates to the field of remote sensing imaging technology, including: obtaining radar operating parameters; obtaining original echo signal matrix; determining the initial image of the observation scene based on the radar operating parameters and the original echo signal matrix; selecting a strong point target in the initial image as a point spread function for error correction, recorded as a second point spread function; inputting the initial image and the second point spread function into the Lucy-Richardson algorithm to obtain a real image. The above scheme in the present invention can improve imaging quality.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing imaging technology, and in particular to a low oversampling Staggered SAR self-focusing method, system, medium and product based on a Lucy-Richardson algorithm. Background Art

[0002] Staggered SAR (Staggered Synthetic Aperture Radar) is a wide-band observation mode that uses variable pulse repetition interval (PRI) and scan-on-receive (SCORE) technology to achieve continuous coverage. Staggered SAR has great potential to achieve both high resolution and wide mapping swaths. Low oversampling Staggered SAR can effectively reduce the amount of onboard data storage and transmission, and alleviate range ambiguity. It has been used as the benchmark mode for Tandel-L and NISAR. In the actual operation of low-oversampling Staggered SAR, there are two problems: 1) Undersampling may be caused by the uneven sampling of echo data and the conflict between sending and receiving, which may lead to data loss, resulting in high side lobes, azimuth ambiguity, false targets and other problems in the azimuth direction during the imaging process; 2) Due to factors such as perturbation, attitude control, and platform jitter, the motion trajectory and attitude of the satellite will change non-ideally, resulting in deviations between the imaging parameters and the actual situation, causing distortion of the azimuth reconstruction results, defocusing, geometric distortion, and resolution reduction in the radar image, which seriously affects the imaging quality or even makes imaging impossible.

[0003] The existing low-oversampling Staggered SAR self-focusing process mainly includes frequency domain method and time domain method. The frequency domain method needs to resample the non-uniformly sampled data into uniformly sampled data first, but this process introduces resampling errors, which increases the difficulty of error correction; the time domain method iterates imaging and error correction at the same time, but in the imaging process, it is mostly based on the compressed sensing algorithm for focusing, which has certain limitations in requiring scene sparse priors.

[0004] In order to solve this problem and improve the imaging quality, this invention proposes a low oversampling Staggered SAR self-focusing method and system based on the Lucy-Richardson algorithm. In order to solve the undersampling problem that may be caused by echo loss, the invention obtains the initial image through Lucy-Richardson time domain filtering deconvolution; in order to solve the error correction problem, the invention uses the strong point target in the initial image as the point spread function, performs Lucy-Richardson filtering on the initial image, realizes self-focusing processing, and obtains a focused SAR image. Summary of the invention

[0005] The purpose of the present invention is to provide a low oversampling Staggered SAR self-focusing method, system, medium and product based on the Lucy-Richardson algorithm to improve imaging quality.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] In a first aspect, the present invention provides a low oversampling Staggered SAR self-focusing method based on the Lucy-Richardson algorithm, comprising:

[0008] Get radar working parameters;

[0009] Obtain the original echo signal matrix;

[0010] Determining an initial image of an observation scene based on the radar operating parameters and the original echo signal matrix;

[0011] Selecting a strong point target in the initial image as a point spread function for error correction, recorded as a second point spread function;

[0012] The initial image and the second point spread function are input into the Lucy-Richardson algorithm to obtain a real image.

[0013] Optionally, the expression of the original echo signal matrix is ​​as follows:

[0014]

[0015] Among them, S echo,m,k Indicates that the azimuth sampling time is η m , the distance sampling time is τ k The echo, that is, the echo matrix S echo The element in the mth row and kth column, N a Represents the number of transmitted pulses, and Nr represents the number of sampling points in the range direction.

[0016] Optionally, determining the initial image of the observation scene based on the radar operating parameters and the original echo signal matrix specifically includes the following steps:

[0017] Obtaining a degraded image of the observed scene;

[0018] determining a first point spread function based on the degraded image of the observation scene;

[0019] Based on the degraded image and the first point spread function, an initial image is determined.

[0020] Optionally, the step of acquiring a degraded image of the observed scene specifically comprises the following steps:

[0021] Calculate scene point coordinates;

[0022] Determine the location of the satellite transmitting the pulse;

[0023] Calculate the slant range of all scene points at each sampling time based on the scene point coordinates and the position of the satellite transmitting pulse;

[0024] Compressing the original echo signal matrix to obtain a time domain signal after range compression;

[0025] calculating a time delay based on the slant range;

[0026] Determining a difference between the time delay and a reference distance;

[0027] Determine the data of all scene points at each sampling time based on the difference;

[0028] Performing phase compensation on the data to obtain compensated data;

[0029] The compensated data are placed at the corresponding layout scene points to obtain a degraded image of the observed scene.

[0030] Optionally, determining the first point spread function based on the degraded image of the observed scene specifically comprises the following steps:

[0031] The center column of the observed scene is selected as the scene of the point target;

[0032] simulating a azimuth signal of the point target based on the slant range;

[0033] Construct the reconstruction matrix;

[0034] A first point spread function is determined based on the reconstruction matrix and the azimuth signal.

[0035] Optionally, determining an initial image based on the degraded image and the first point spread function specifically comprises the following steps:

[0036] Each column of the degraded image and the first point spread function are input into the Lucy-Richardson filtering algorithm for filtering to obtain an initial image.

[0037] Optionally, the expression of the first point spread function is:

[0038] F 1 =D H ·P

[0039] Where D represents the reconstruction matrix and P represents the azimuth signal of the point target.

[0040] In a second aspect, the present invention provides a computer system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described in the first aspect.

[0041] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0042] In a fourth aspect, the present invention provides a computer program product, comprising a computer program, characterized in that when the computer program / instructions are executed by a processor, the steps of the method described in the first aspect are implemented.

[0043] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0044] The present invention proposes a low oversampling Staggered SAR self-focusing method, system, medium and product based on the Lucy-Richardson algorithm. To address the undersampling problem that may be caused by echo loss, the present invention obtains an initial image through Lucy-Richardson time domain filtering deconvolution; to address the error correction problem, the present invention uses a strong point target in the initial image as a point spread function, performs Lucy-Richardson filtering on the initial image, realizes self-focusing processing, obtains a focused SAR image, and improves imaging accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] 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 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 these drawings without paying creative labor.

[0046] Figure 1 A block diagram of the low oversampling Staggered SAR autofocusing method based on the Lucy-Richardson algorithm provided by the present invention. DETAILED DESCRIPTION

[0047] 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.

[0048] The purpose of the present invention is to provide a low oversampling Staggered SAR self-focusing method, system, medium and product based on the Lucy-Richardson algorithm to improve imaging quality.

[0049] Low oversampling Staggered SAR achieves continuous wide coverage under high-resolution observation through variable PRI and SCORE technology. It has great potential to achieve high resolution and wide mapping swath at the same time, and has been used as the benchmark mode of Tandel-L and NISAR. However, in the actual working process of low oversampling Staggered SAR, there are two problems: 1) undersampling caused by the uneven sampling of its echo data and the conflict between sending and receiving, which may lead to data loss; 2) motion error causes deviation between imaging parameters and the actual situation, resulting in defocusing of azimuth reconstruction results. For this reason, the present invention provides a low oversampling Staggered SAR self-focusing method and system based on the Lucy-Richardson algorithm. Medium and product, first, in view of the undersampling problem that may be caused by the blind spot problem of low oversampling non-uniform echo signals, the initial image is obtained by Lucy-Richardson time domain filtering deconvolution; secondly, the strong point target in the initial image is selected as the point spread function, as the input of the Lucy-Richardson algorithm, and the initial image is filtered in the time domain to obtain a focused SAR image.

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] Embodiment 1

[0052] Figure 1 The block diagram of the low oversampling Staggered SAR self-focusing method based on the Lucy-Richardson algorithm provided by the present invention is combined with Figure 1 , the method of the present invention comprises:

[0053] Step 1: Get radar operating parameters.

[0054] The radar operating parameters include the scene center reference slant distance, satellite flight speed, azimuth antenna size, number of transmitted pulses, time vector of radar transmitted pulses and operating wavelength.

[0055] The radar operating parameters are f 1 = {R 0 ,V e ,L a ,N a ,λ,H,Kr,ρ,ta}。 0 is the reference slope distance from the scene center, V e is the satellite flight speed, La is the antenna size in azimuth, N a is the number of transmitted pulses, λ is the operating wavelength, H is the orbital height, Kr is the modulation frequency of the transmitted signal, ρ is the resolution of the radar system, and the time vector of the radar transmitting pulse ta=[η 1 ,η 2 ,...,η m ,...η Na ] T , where η m represents the non-uniform sampling time of the m-th pulse emitted by the under-sampling Staggered SAR, [·] T Indicates the transposition operation. Among them, due to factors such as perturbation, attitude control, and platform jitter, the satellite's trajectory and attitude will change non-ideally. The satellite's flight speed V e Not true flight speed.

[0056] Step 2: Get the original echo signal matrix.

[0057] The original echo signal matrix is ​​a two-dimensional matrix, expressed as

[0058]

[0059] Among them, S echo,m,k Indicates that the azimuth sampling time is η m , the distance sampling time is τ k The echo of . That is, the echo matrix S echo The element in the mth row and kth column.

[0060] Step 3: Determine the initial image of the observation scene based on the radar operating parameters and the original echo signal matrix.

[0061] Specifically include:

[0062] Step 3.1: Obtain a degraded image of the observation scene.

[0063] Step 3.11: Calculate scene point coordinates.

[0064] According to the scene center slant distance and track height, calculate the scene center coordinates (x p ,0,0), where

[0065] Set up a scene of P×Q pixels, with the pixel grid size set to l, which should be equivalent to the resolution of the system. Scene S 0 The coordinates of each scene point in is

[0066]

[0067] Step 3.12: Determine the location of the satellite transmitting the pulse.

[0068] Assume that the initial position of the satellite sampling is [x track ,0,H], then the position coordinates of the satellite transmitting pulses in sequence are {[x track +V e η 1 ,0,H],…,[x track +V e η m ,0,H],…[x track +V e η Na ,0,H]}, where η is assumed m is the mth moment under the low sampling Staggered SAR system. e Similarly, there is a certain error between the position where the satellite transmits the pulse and the position where the pulse is transmitted during the actual flight.

[0069] Step 3.13: Calculate the slant range of all scene points at each sampling time based on the scene point coordinates and the positions of the satellite transmitted pulses.

[0070] Finally, the slant range R of all scene points at each sampling time is calculated based on the scene point coordinates and the position of the satellite transmitting pulse p,q (η m ).

[0071] Step 3.14: compress the original echo signal matrix to obtain a time domain signal after range compression.

[0072] First, the echo matrix S echo Perform distance to Fourier transform;

[0073] Secondly, the signal obtained after the distance Fourier transform is matched filtered to obtain the distance compressed frequency domain signal;

[0074] Finally, the distance compression frequency domain signal is subjected to distance inverse Fourier transform to obtain the distance compression time domain signal S echo,rc .

[0075] Among them, the specific operation method of Fourier transform for processing one-dimensional signals is: swap the positions of the first half of the signal elements with the second half of the signal elements, then perform fast Fourier transform on the swapped signal, and finally swap the positions of the first half of the elements with the second half of the elements, thus completing the Fourier transform. Correspondingly, the inverse Fourier transform has the same operation steps as the above-mentioned Fourier transform, except that all Fourier transform functions in the process are replaced by inverse Fourier transform functions.

[0076] Step 3.15: Calculate the time delay based on the slant range.

[0077] According to the slant distance R of all scene points at each sampling time in step 3.13 p,q (η m ), calculate the delay, that is, 2R p,q (η m ) / c, where c is the speed of light.

[0078] Step 3.16: Determine the difference between the time delay and the reference distance.

[0079] Step 3.17: Determine the data of all scene points at each sampling time based on the difference.

[0080] Step 3.18: Perform phase compensation on the data to obtain compensated data.

[0081] Step 3.19: Place the compensated data at the corresponding layout scene points to obtain a degraded image of the observed scene.

[0082] Then according to the delay and the reference distance delay 2R 0 / c difference, obtain the data S of all scene points at each sampling time echo,rc (2(R p,q (η m )-R 0 ) / c), while compensating the phase exp{j4πR p,q (η m ) / λ} to obtain data S echo,rcmc (2(R p,q (η m )-R 0 ) / c), the compensated data at each sampling moment is placed on the corresponding layout scene point, and finally the degraded image is obtained.

[0083] Step 3.2: Determine a first point spread function based on the degraded image of the observed scene.

[0084] Specifically include:

[0085] Step 3.21: Select the center column of the observed scene as the scene of the point target.

[0086] Select scene S 0 The center column [… (x p ,-l,0) (x p ,0,0) (x p ,l,0) …] P×1 T , the target point (x p ,0,0) is assigned an intensity of 1, and the intensity of the remaining points is assigned to 0.

[0087] Step 3.22: Simulate the azimuth signal of the point target based on the slant range.

[0088] Select the slope distance R calculated in step 3.13 P / 2,Q / 2 (η m ), simulation point target (x p ,0,0) azimuth signal:

[0089]

[0090] Step 3.23: Construct the reconstruction matrix.

[0091] Select the slope distance R calculated in step 3.13 p,Q / 2 (η m ), the reconstruction matrix is ​​expressed as

[0092]

[0093] D m,n =exp{-j4πR p,Q / 2 (η m ) / λ}·ε(((acos(R 0 / R p,Q / 2 (η m ))-0.886*λ / L a ))) indicates that the crossing time is η m , slope distance is R n (η m ) is the reconstruction element of the target point, and ε(·) represents the step function.

[0094] Step 3.24: Determine a first point spread function based on the reconstruction matrix and the azimuth signal.

[0095] The point spread function F = D is obtained by conjugate transposition operation. H ·P.

[0096] Step 3.3: Determine an initial image based on the degraded image and the first point spread function.

[0097] For the degraded image I 1 Each column of , performs Lucy-Richardson filtering, I 1 Each column and F 1 As input, the whole image is finally obtained. The Lucy-Richardson filter solution process is an iterative process, which can be expressed as:

[0098]

[0099] Among them, i is the number of iterations, generally selected from 5 to 15 times, represents the convolution operation, represents the qth column result of the real scene obtained during the i-th iteration of Lucy-Richardson filtering, represents the qth column result of the real scene obtained during the i+1th iteration of Lucy-Richardson filtering, I 1 (q) represents the qth column in the degraded image obtained in step 3.1.

[0100] Perform the above Lucy-Richardson filtering on each column q. In the first iteration, select I 1 (q) As According to the above formula, we can obtain Then iterate until the set number of iterations is reached, and the iteration is terminated to obtain the final The above operations are performed on the Q column in sequence to obtain the final entire real SAR image.

[0101] Step 4: Select a strong point target in the initial image as a point spread function for error correction, recorded as a second point spread function.

[0102] Specifically, the method for selecting strong point targets is as follows: first, the edges of the initial image cannot be selected; second, a point with the most prominent intensity on a certain distance gate is selected. Compared with other points in the distance gate, the intensity of this point is dozens of times higher than that of other points; finally, with this point as the center, the range selected up and down along the distance dimension is the start and end of the defocus position.

[0103] Step 5: Input the initial image and the second point spread function into the Lucy-Richardson algorithm to obtain a real image.

[0104] Take the initial image I obtained in step 3 1 (q) As an object, the point spread function F obtained in step 4 2 As the point spread function of error correction, it is input into the Lucy-Richardson algorithm and the real image I is obtained through multiple iterations. 0 The specific implementation process is the same as step 3.3. The difference is that the initial image I 1 (q) and the point spread function F 2 as input.

[0105] Embodiment 2

[0106] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the low oversampling Staggered SAR autofocusing method based on the Lucy-Richardson algorithm in the first embodiment.

[0107] Embodiment 3

[0108] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the low oversampling Staggered SAR autofocusing method based on the Lucy-Richardson algorithm in the first embodiment.

[0109] Embodiment 4

[0110] A computer program product includes a computer program. When the computer program is executed by a processor, the steps of the low oversampling Staggered SAR self-focusing method based on the Lucy-Richardson algorithm in the first embodiment are implemented.

[0111] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided by the present invention may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processor involved in each embodiment provided by the present invention may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited thereto.

[0112] 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.

[0113] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A low oversampling Staggered SAR autofocusing method based on Lucy-Richardson algorithm, characterized in that: include: Get radar working parameters; Obtain the original echo signal matrix; Determining an initial image of an observation scene based on the radar operating parameters and the original echo signal matrix; Selecting a strong point target in the initial image as a point spread function for error correction, recorded as a second point spread function; Inputting the initial image and the second point spread function into the Lucy-Richardson algorithm to obtain a real image; Determining the initial image of the observation scene based on the radar operating parameters and the original echo signal matrix specifically includes the following steps: Obtaining a degraded image of the observed scene; determining a first point spread function based on the degraded image of the observation scene; Determining an initial image based on the degraded image and a first point spread function; The step of obtaining the degraded image of the observation scene specifically comprises the following steps: Calculate scene point coordinates; Determine the location of the satellite transmitting the pulse; Calculate the slant range of all scene points at each sampling time based on the scene point coordinates and the position of the satellite transmitting pulse; Compressing the original echo signal matrix to obtain a time domain signal after range compression; calculating a time delay based on the slant range; Determining a difference between the time delay and a reference distance; Determine the data of all scene points at each sampling time based on the difference; Performing phase compensation on the data to obtain compensated data; The compensated data is placed at the corresponding layout scene points to obtain a degraded image of the observed scene; Determining a first point spread function based on the degraded image of the observed scene specifically comprises the following steps: The center column of the observed scene is selected as the scene of the point target; simulating a azimuth signal of the point target based on the slant range; Construct the reconstruction matrix; determining a first point spread function based on the reconstruction matrix and the azimuth signal; Based on the degraded image and the first point spread function, determining an initial image specifically comprises the following steps: Inputting each column and the first point spread function of the degraded image into the Lucy-Richardson filtering algorithm for filtering to obtain an initial image; The determining of the first point spread function based on the degraded image of the observed scene specifically includes: Select the center column of the observed scene as the scene of the point target: Select the column [… (x p ,-l,0) (x p ,0,0) (x p ,l,0) …] P×1 T , the target point (x p ,0,0) is assigned an intensity of 1, and the intensity of the remaining points is assigned to 0; Simulating the azimuth signal of the point target based on the slant range: Select slope distance R p / 2,Q / 2 (η m ), simulation point target (x p ,0,0) azimuth signal: Construct the reconstruction matrix: Select slope distance R p,Q / 2 (η m ), the reconstruction matrix is ​​expressed as: D m,n =exp{-j4πR p,Q / 2 (or m ) / λ}·ε(((acos(R0 / R p,Q / 2 (or m ))-0.886*λ / L a ))) The crossing time is η m , slope distance is R n (η m ) is the reconstruction element of the target point, ε(·) represents the step function, and R0 represents the reference slant distance from the center of the scene; Determining a first point spread function based on the reconstruction matrix and the azimuth signal includes: The first point spread function F1=D is obtained by conjugate transposition operation. H ·P; Based on the degraded image and the first point spread function, an initial image is determined: For each column of the degraded image I1, Lucy-Richardson filtering is performed, and each column of I1 and F1 are used as input, and finally the entire image is obtained. The Lucy-Richardson filtering solution process is an iterative process, which can be expressed as: Among them, i is the number of iterations, selected from 5 to 15 times, represents the convolution operation, represents the qth column result of the real scene obtained during the i-th iteration of Lucy-Richardson filtering, represents the qth column result of the real scene obtained during the i+1th iteration of Lucy-Richardson filtering, I1(q) represents the qth column in the degraded image, and F1 represents the first point spread function; Perform the above Lucy-Richardson filtering on each column q. In the first iteration, select I1(q) as According to the above formula, we can obtain Then iterate until the set number of iterations is reached, and the iteration is terminated to obtain the final The above operations are performed on the Q column in sequence to obtain the final entire real SAR image.

2. The low oversampling Staggered SAR autofocusing method based on the Lucy-Richardson algorithm according to claim 1, characterized in that: The expression of the original echo signal matrix is ​​as follows: Among them, S echo,m,k Indicates that the azimuth sampling time is η m , the distance sampling time is τ k The echo, that is, the echo matrix S echo The element in the mth row and kth column, N a Represents the number of transmitted pulses, and Nr represents the number of sampling points in the range direction.

3. The low oversampling Staggered SAR autofocusing method based on the Lucy-Richardson algorithm according to claim 1, characterized in that: The expression of the first point spread function is: F1=D H ·P Where D represents the reconstruction matrix and P represents the azimuth signal of the point target.

4. A computer system comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 3.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

6. A computer program product comprising a computer program, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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