CN113702974A - Method for quickly optimizing airborne/missile-borne synthetic aperture radar image - Google Patents

Method for quickly optimizing airborne/missile-borne synthetic aperture radar image Download PDF

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CN113702974A
CN113702974A CN202111019935.0A CN202111019935A CN113702974A CN 113702974 A CN113702974 A CN 113702974A CN 202111019935 A CN202111019935 A CN 202111019935A CN 113702974 A CN113702974 A CN 113702974A
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CN113702974B (en
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陆钱融
于祥祯
杜科
邱晓燕
奚银
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Shanghai Radio Equipment Research Institute
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
    • G01S13/9021—SAR image post-processing techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/15—Correlation function computation including computation of convolution operations
    • G06F17/156—Correlation function computation including computation of convolution operations using a domain transform, e.g. Fourier transform, polynomial transform, number theoretic transform

Abstract

The invention relates to a method for quickly optimizing an airborne/missile-borne synthetic aperture radar image, which comprises the following steps of: s1, overlapping and partitioning the panoramic SAR echo data in the distance direction and the azimuth direction to generate a plurality of echo sub-arrays; s2, based on each echo subarray, performing motion compensation and imaging processing to generate corresponding defocusing subimages; s3, based on each defocusing sub-image, eliminating the residual aperture error of the RD domain, and generating a corresponding refocusing sub-image; and S4, splicing the SAR panorama based on the refocused sub-images. The method realizes high-precision residual space-variant motion error compensation, has the advantages of simple principle, less calculation amount, high running speed, higher accuracy and stronger robustness, is suitable for actual processing, and has stronger practicability.

Description

Method for quickly optimizing airborne/missile-borne synthetic aperture radar image
Technical Field
The invention relates to the field of synthetic aperture radar motion error compensation, in particular to a method for quickly optimizing an airborne/missile-borne synthetic aperture radar image.
Background
The SAR (Synthetic Aperture Radar) is a high-resolution imaging Radar, is an active microwave remote sensing device, works by means of microwave radiation of the SAR, is not influenced by severe conditions of a battlefield, has the advantage of all-weather work all day long, can overcome the defects that a traditional terrain matching system and a digital scene matching navigation mode are easily influenced by severe weather, night, rain, dust and the like when being loaded on a cruise missile, can obtain a Radar image with high resolution and high imaging precision, and is an effective way for improving the hitting precision of a precision guided weapon. However, in practical applications, some difficult problems exist in the SAR, particularly in the airborne/missile-borne SAR, in the process of irradiating a target, due to the instability of the airflow and the trajectory thereof, a motion error is generated, so that the target is displaced in the distance direction, defocusing is generated in the azimuth direction, and the image quality is deteriorated.
The radar-target instantaneous slant range change caused by the motion error has range null change, azimuth null change and aperture null change. Based on this, many researchers have solved these three problems to different degrees from different perspectives such as echo and image. For example, in foreign countries, a research team adopts a strategy of firstly performing coarse distance compensation and then performing fine compensation on echo data, so that the problems of motion error distance space variation and azimuth space variation under medium and low resolution are solved, but the compensation performance is seriously reduced under a high-resolution and ultrahigh-resolution system; a research team starts from a time domain sub-aperture, and the problem of aperture space-variant is solved to a certain extent according to a strategy of compensating the whole sub-aperture uniformly according to a motion error at a time center, but the compensation performance is reduced under a complex fast-varying track; a research team starts from image self-focusing, and reversely estimates a motion error by estimating a phase error to solve the problems of azimuth space-variant and aperture space-variant, but under a complex track, an image is seriously defocused, the self-focusing precision is reduced, and further the estimation error is sharply increased; in addition, in China, a research team starts from an echo frequency domain, and solves the problems of aperture space-variant and azimuth space-variant by compensating motion errors corresponding to sub-bands in a segmented manner, but the strategy needs accurate sub-band division and paired echoes (namely ghosts) are easy to appear; and a research team starts from an echo slant distance error analytic formula, distance pulse pressure envelope is corrected by pulse and sampling point by sampling point, distance space variation and azimuth space variation are solved, but the algorithm has large calculation amount and envelope errors are easy to occur after calibration under ultrahigh resolution.
Patent CN102288962A ("a method for real-time motion compensation of ultra-wideband synthetic aperture radar") provides a method for real-time motion compensation of ultra-wideband synthetic aperture radar, which is characterized by using motion parameters outputted by a single-point GPS (global positioning system) to perform real-time motion compensation of UWB SAR (ultra-wideband synthetic aperture radar) imaging. The method mainly divides the sight line direction movement error into a distance space invariant part and a distance space variant part which are respectively completed before the distance direction pulse compression and after the distance migration correction, so that the distance space variant and the direction space variant are solved, but the influence of a residual aperture space variant component after the distance space invariant error compensation on a Doppler frequency spectrum is ignored, and the compensation precision of the SAR image is reduced under a complex track.
Patent CN107037430A ("estimation method for airborne SAR motion compensation") introduces an estimation method for airborne SAR motion compensation, which estimates a doppler center and a fuzzy number in a baseband by using an energy balance method, calculates to obtain an absolute doppler center, and performs range migration correction in a two-dimensional frequency domain by using the estimated doppler center; and finally, completing azimuth pulse compression by estimating Doppler frequency modulation to form SAR imaging. According to the principle that the Doppler center can be changed by linear motion errors, linear motion compensation is completed by compensating the Doppler center; in addition, azimuth target broadening caused by residual motion errors is reduced to a certain extent by estimating the azimuth Doppler chirp rate. Generally speaking, the method does not consider the influence of quadratic and higher order motion errors on frequency spectrum and distance displacement, is only suitable for the linear motion error condition, and finally has limited effect improvement though frequency modulation slope is estimated through data rather than theory.
Patent CN103235306A ("a motion compensation method suitable for high-speed motor vehicle-mounted SAR imaging"), discloses a motion compensation method suitable for high-speed motor vehicle-mounted SAR imaging. According to the characteristics of SAR imaging carried by a high-speed maneuvering aircraft, a radar-target slant range model under a motion error is approximately obtained by adopting second-order Taylor expansion, on the basis of the model, after distance direction processing and primary azimuth Deramp (declivity) processing are completed, residual Doppler frequency modulation rate information of an echo is obtained by utilizing inertial navigation information and combining Doppler frequency modulation rate estimation, a corresponding compensation function is constructed according to the residual Doppler frequency modulation rate information, error phase is reasonably compensated, and the focusing quality of the SAR image carried by the high-speed maneuvering aircraft is improved. The method performs compensation function expression reasoning from the echo analytic expression angle, and has the capability of accurately solving motion errors under an assumed slope distance model. However, under a complex trajectory, a higher-order taylor expansion needs to be adopted for the slant range, so that the solution of the doppler spectrum theoretical expression is difficult and is difficult to continue. Therefore, this method is only suitable for compensating for slowly varying motion error conditions.
Patent CN104181514A ("a synthetic aperture radar high-precision motion compensation method") introduces a synthetic aperture radar high-precision motion compensation method, which first performs BP (back projection) imaging with a radar platform uniform linear motion trajectory to obtain a coarsely focused image, then iteratively adjusts an antenna phase center error with the image intensity as a criterion, taking the image intensity as an optimal, and after the completion, obtains an antenna phase center error estimate and an antenna phase center position error, and finally adds the estimated position error to the uniform linear motion trajectory to obtain an absolute position, and performs final high-precision imaging with an algorithm. The method requires two rounds of BP imaging, with O (N) for NxN echo data for a single BP imaging3) The time complexity of (the number of echo points is cubic), so although the problem of space variability of motion errors is completely solved, the computation amount is huge. Secondly, residual motion error estimation based on the maximum image intensity criterion has large estimation error for uniform scenes (sea surface, grassland and the like), and improvement of image refocusing effect is limited.
A document of 'Minimum entropy auto focus correction of residual distance cell migration' disclosed in J.M.Kantor (J.M. cande) in IEEE Radar Conf (IEEE Radar conference) in 2017 improves the condition that the traditional one-dimensional auto focus only carries out phase error correction, introduces a two-dimensional auto focus method of the Minimum entropy criterion, combines the phase error and the distance to displacement error correction, and reduces the energy leakage of a target in the distance direction. However, the two-dimensional compensation precision of the method depends on the azimuth self-focusing phase estimation performance, and the compensation performance is sharply reduced under the condition of uniform scene or image deterioration development; in addition, the method considers that the corresponding relation between the distance displacement and the phase error caused by the motion error is not changed by the distance migration correction, which is not true under the condition of large residual motion error, and the compensation performance is also reduced.
A document A2-DSpace-Variant Motion Estimation and Compensation Method for ultra-high-Resolution Airborne step Frequency SAR With Long Integration Time and ultra-high Resolution moving Integration Time (a two-dimensional space Motion Estimation and Compensation Method) disclosed in Chen J (journal of IEEE (institute of Electrical and electronic Sensing)) in IEEE Transactions on Geoscience and Motion Sensing (IEEE J) 11 of 2017 proposes that a parameterized self-focusing Method is adopted to estimate the azimuth phase error, and the Motion error is estimated by least squares according to the mathematical relationship between the phase and the Motion error. On the basis, the distance space variation compensation is carried out through chirp-z (linear frequency modulation transform) by Taylor expansion of the slope distance error at the reference distance to a primary term. Although the method starts from echo data and does not depend on a GPS/INS (global positioning system/inertial navigation system), the self-focusing estimation precision is greatly influenced by environment and flight attitude, and the variance of a motion error estimation value is easily increased due to the transmission of an azimuth phase error; secondly, chirp-z can only compensate the linear shift of the target in the distance direction, and the situation of insufficient compensation exists in the complex track.
In 2018, 6 th "IEEE j.sel.topics appl.earth observer.remote sensor," Yang M "(IEEE applied earth observation and remote sensing selected subject journal), the document" Efficient space-variant implementation adaptation for ultra-high-resolution SAR based on subband processing "(high-resolution SAR spatial motion compensation method based on subband processing) proposes to adopt a distance interpolation-based target distance-to-envelope correction technique, which interpolates a theoretical position to a real sampling position according to the distance of a target under motion error, and solves the motion error distance space variability through point-to-point processing; meanwhile, all the azimuth pulses are correspondingly processed, so that the azimuth space-variant property is solved. However, the method cannot solve the influence of aperture space variation error on Doppler frequency spectrum under wide beam-complex motion error; secondly, serious distance envelope deformation can be generated due to the approximation of the slant distance model under complex motion errors, and the image quality is reduced; in addition, under wide-width irradiation, the distance direction point-by-point interpolation processing requires a large amount of calculation, and the timeliness is poor.
Disclosure of Invention
The invention aims to provide a method for quickly optimizing an airborne/missile-borne synthetic aperture radar image, which adopts a numerical calculation mode to accurately obtain the residual motion error amount corresponding to the Doppler frequency under any track and improves the phase error compensation precision.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a method for quickly optimizing an airborne/missile-borne synthetic aperture radar image comprises the following steps:
s1, overlapping and partitioning the panoramic SAR echo data in the distance direction and the azimuth direction to generate a plurality of echo sub-arrays;
s2, based on each echo subarray, performing motion compensation and imaging processing to generate corresponding defocusing subimages;
s3, based on each defocused sub-image, eliminating the residual aperture error of the RD (Range Doppler) domain, and generating a corresponding refocused sub-image;
and S4, splicing the SAR panorama based on each refocused sub-image.
Preferably, in step S1:
the requirements for blocking are: overlap region of adjacent sub-blocks in blockingThe number of the overlapping points in the distance direction and the azimuth direction is respectively more than or equal to the distance direction abandon area length N of the panoramic SAR echo datarAnd azimuthal disposal area length Na，Nr、NaThe calculation formula of (2) is as follows:
Figure BDA0003241511590000051
Figure BDA0003241511590000052
wherein, TaIs the synthetic aperture time; PRF is repetition frequency; t ispIs the pulse width; fsIs the range-wise sampling rate.
Preferably, Step S2 is implemented by a TSMC algorithm (Two-Step Motion Compensation) and a Chirp Scaling algorithm (conventional linear Scaling imaging algorithm), and includes the steps of:
s21, performing primary processing on the echo sub-array to generate a primary processed echo sub-array;
and S22, carrying out secondary processing on the primary processing echo subarray to generate a defocusing subimage.
Preferably, step S21 includes:
s211, performing first-order compensation, namely distance direction non-space-variant error compensation, on the echo subarray based on a TSMC algorithm to generate a first-order compensation echo subarray:
denote the echo subarray by ss (k, r '), where k is the slow time sample index, r' is the range-wise spatial domain coordinate, and the subarray size is N0×N1(number of azimuth pixels × number of distance pixels), the expression is:
Figure BDA0003241511590000053
wherein exp (·) is natural exponent operation; j is an imaginary unit; λ is the transmitted signal wavelength; k0＝4πkr/c2Wherein k isrIs the chirp rate of the range-oriented transmit signal, c is the speed of light, ra(k, r) is the slant distance of the bullet eyes under the actual trajectory track, and r is the zero Doppler surface distance of the target; r isa(k, r) contains the slant distance r of the target under the ideal straight-line trackn(k, r), and the skew error caused by the motion error is Δ r (k, r, θ)k) Wherein, thetakIs the instantaneous oblique view angle which varies with the azimuth, i.e. Δ r (k, r, θ)k) Has pore size empty change;
ignoring θ in the TSMC algorithmkAnd Δ r (k, r, θ)k) Decomposed into space-variant skew error amount delta rv(k,r|r0) And the non-space-variant skew error amount Deltar0(k,r0) Wherein r is0Is a reference distance; then r isa(k, r) can be transformed as follows:
ra(k,r)＝rn(k,r)+Δr(k,r,θk)
≈rn(k,r)+Δr(k,r|θk＝0)
＝rn(k,r)+Δrv(k,r|r0)+Δr0(k,r0)；
Δr0(k,r0) The projection component of the instantaneous slope distance error on the zero Doppler surface can be understood as the following formula:
Figure BDA0003241511590000061
wherein Δ x is the lateral motion error; Δ z is the vertical motion error; h is the height to the ground;
based on Δ r0(k,r0) Constructing a compensation function H of the wave number domain with non-space variation of the distance directionR(k,Kr) Namely:
Figure BDA0003241511590000062
wherein, KrIs the distance wave number;
based on HR(k,Kr)，The distance compensation is carried out towards the non-space-variant distance, and the calculation formula is as follows:
sS1(k,Kr)＝sS(k,Kr)·HR(k,Kr)；
wherein, sS (K, K)r) An echo subarray obtained after space-domain Fourier transform is carried out on ss (k, r'); sS1(k,Kr) The first-order compensation echo subarray is generated;
s212, performing range pulse compression on the first-order compensation echo subarray:
construction of spatial matched filter Hrmf(Kr)：
Figure BDA0003241511590000063
Based on Hrmf(Kr) To sS1(k,Kr) Performing range-wise pulse compression to obtain sS2(k,Kr) The calculation formula is as follows:
sS2(k,Kr)＝sS1(k,Kr)·Hrmf(Kr)；
then, for sS2(k,Kr) Performing inverse Fourier transform in space domain to obtain ss2(k, r') is calculated as:
Figure BDA0003241511590000064
wherein, sinc (·) is normalized sinc function operation, and the expression is: sinc (x) sin (pi x)/pi x; ss2(k, r') is the first-order compensation echo subarray after the distance pulse compression;
s213, performing range unit migration correction on the first-order compensation echo sub-array after range-direction pulse compression to generate a first-order processing echo sub-array:
for ss according to the classical interpolation range migration correction method2(k, r') performing migration correction, wherein the calculation formula is as follows:
Figure BDA0003241511590000071
due to delta rv(k,r|r0) Generally much smaller than the distance resolution element, for ss3(k, r') the following approximation is made:
Figure BDA0003241511590000072
ss3(k, r') is the generated primary processed echo sub-array.
Preferably, step S22 includes:
s221, performing second-order compensation on the primary processing echo sub-array based on a TSMC algorithm, namely distance direction space-variant error compensation, and generating a second-order compensation echo sub-array:
by ss3The expression (k, r') shows that the range migration curve of the echo sub-array is completely corrected, but the space-variant phase error still exists in the azimuth direction, so that a range-to-space-variant phase compensation function H is constructedv(k,r′)：
Figure BDA0003241511590000073
And (3) performing distance direction space-variant error compensation in an azimuth time domain distance space domain, wherein the calculation formula is as follows:
Figure BDA0003241511590000074
ss4(k, r') is the generated second order compensation echo sub-array;
s222, performing azimuth pulse compression on the second-order compensation echo sub-array to generate a defocusing sub-image:
ss4the first exponential (exp) term in (k, r') represents the ideal azimuth modulated signal, and the matched filter H is constructed in the Doppler domainamf(fa,r′)：
Figure BDA0003241511590000081
Where v is the equivalent ground speed, faThe direction Fourier frequency points;
based on a filter HamfCompleting azimuth pulse compression in the RD domain; then, the echo subjected to azimuth pulse compression is subjected to inverse Fourier transform to generate a defocus sub-image ss5(k, r') is calculated as:
Figure BDA0003241511590000082
wherein, BaIs the Doppler bandwidth, k0Indexing values for the target azimuth coordinates, "+" is a convolution operation symbol; by ss5The calculation formula (k, r') shows that the residual aperture space-variant phase error is equivalent to a fuzzy filter, resulting in ss5(k, r') appear defocused in the azimuth direction, ss5(k, r') is the generated defocus sub-image.
Preferably, step S3 is implemented by a NuPTA algorithm (numerical landform and aperture dependent algorithm), comprising the steps of:
s31, based on each defocused sub-image, calculating the residual aperture error and the instantaneous slope distance between the radar and the target under the ideal straight-line track, wherein the calculation formula is as follows:
Figure BDA0003241511590000083
Figure BDA0003241511590000084
wherein δ r (k) is a residual aperture space-variant error; r isn(k) The target is the radar and target instantaneous slope distance under an ideal straight line track;
s32, based on each defocused sub-image and delta r (k), rn(k) Calculating the Doppler frequency corresponding to each pulse of the defocused sub-image under the residual aperture error by using the frequency equal to the first derivative of the phase, wherein the calculation formula is as follows:
Figure BDA0003241511590000091
wherein, f (k) is the true Doppler frequency corresponding to the kth pulse of the defocused sub-image;
s33, based on delta r (k) and f (k), calculating a residual aperture compensation function corresponding to the azimuth Fourier frequency points through a sinc interpolation algorithm, wherein the calculation formula is as follows:
Figure BDA0003241511590000092
s34 based on HNuPTA(fa,r0) And finishing the residual aperture error compensation in the defocus subimage RD field:
for defocused sub-images ss5(k, r') performs an azimuthal Fourier transform to generate Ss5(faR'), and then the compensation of the residual aperture space-variant phase error is completed by the following formula:
Ss6(fa,r′)＝Ss5(fa,r′)·HNuPTA(fa,r0)；
then to the Ss6(faR') are inverse Fourier transformed in azimuth to obtain a refocused subimage ss without residual phase error6(k,r′)。
Preferably, step S3 is implemented by PTA algorithm (landform and aperture dependent algorithm), comprising the steps of:
s31, calculating the residual aperture error compensation function H of each defocused sub-image by using the relation between the ideal azimuth frequency and the instantaneous squint anglePTA(fa,r0) The calculation formula is as follows:
Figure BDA0003241511590000093
s32 based on HPTA(fa,r0) And finishing the residual aperture error compensation in the defocus subimage RD field:
for defocused sub-images ss5(k, r') performs an azimuthal Fourier transform to generate Ss5(faR'), and then the compensation of the residual aperture space-variant phase error is completed by the following formula:
Ss6(fa,r′)＝Ss5(fa,r′)·HPTA(fa,r0)；
then to the Ss6(faR') are inverse Fourier transformed in azimuth to obtain a refocused subimage ss without residual phase error6(k,r′)。
Preferably, step S4 includes:
s41, judging whether all the echo subarray processing is finished:
if not, returning to S2 to process the next echo subarray;
if yes, go to S42;
s42, splicing the processed refocused sub-images into a panoramic image:
refocusing sub-images ss6(k, r') performing a peripheral cropping process, discarding the overlapping portion, and generating non-overlapping sub-images ss7(k, r') expressed as:
ss7(k,r′)＝ss6(k+Na,r′+Nr),
Figure BDA0003241511590000101
all of the non-overlapping sub-images ss are then processed7And (k, r') directly splicing according to the sequence before cutting to obtain the final processed SAR panorama.
In summary, compared with the prior art, the method for rapidly optimizing the airborne/missile-borne synthetic aperture radar image provided by the invention has the following beneficial effects:
1. the method does not need to calculate stationary phase points during Fourier transform, does not make any approximation on residual motion errors, and has better robustness and higher image refocusing quality;
2. the method is simple in calculation, residual aperture space-variant error compensation can be completed only by performing one-time azimuth Fourier transform, one-time complex data multiplication and one-time azimuth inverse Fourier transform on the sub-block data, and time complexity is low.
Drawings
FIG. 1 is a flow chart of a method for rapidly optimizing an airborne/missile-borne synthetic aperture radar image based on a NuPTA algorithm according to the present invention;
FIG. 2 is a schematic diagram of a simulation scenario setup of the present invention;
FIG. 3 is a graph of motion error data during echo acquisition in accordance with the present invention;
FIG. 4 is a comparison graph of the fitting situation of true residual aperture space-variant errors using the PTA algorithm and the NuPTA algorithm of the present invention;
FIG. 5a is a panoramic image generated by PTA algorithm processing according to the present invention;
fig. 5b is a panoramic image generated by the NuPTA algorithm process of the present invention;
fig. 6a is a comparison graph of the azimuthal point target diffusion function of the target 1 after compensation by the PTA algorithm and the NuPTA algorithm in the scene of the present invention;
fig. 6b is a comparison graph of the azimuthal point target diffusion function of the target 5 after compensation by the PTA algorithm and the NuPTA algorithm in the scene of the present invention;
fig. 6c is a comparison graph of the azimuthal point target diffusion function of the target 10 after compensation by the PTA algorithm and the NuPTA algorithm in the scene of the present invention;
FIG. 7a is a diagram of the final image focusing effect of the present invention using PTA algorithm under the measured data;
fig. 7b is a final image focusing effect graph of the NuPTA algorithm under the measured data according to the present invention.
Detailed Description
The following describes a method for rapidly optimizing an airborne/missile-borne synthetic aperture radar image according to the present invention in detail with reference to the accompanying drawings and the detailed description. The advantages and features of the present invention will become more apparent from the following description. It should be noted that the drawings are simplified in form and not to precise scale, and are only used for convenience and clarity to assist in describing the embodiments of the present invention, but not for limiting the conditions of the embodiments of the present invention, and therefore, the present invention is not limited by the technical spirit, and any structural modifications, changes in the proportional relationship, or adjustments in size, should fall within the scope of the technical content of the present invention without affecting the function and the achievable purpose of the present invention.
It is to be noted that, in the present invention, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The technical idea of the invention is as follows:
step 1: overlapping and partitioning the panoramic SAR echo data in a distance direction and an azimuth direction to generate a plurality of echo sub-arrays;
step 2: based on each echo subarray, performing motion compensation and imaging processing to generate corresponding defocusing subimages;
and step 3: based on each defocusing sub-image, eliminating the residual aperture error of the RD domain, and generating a corresponding refocusing sub-image;
and 4, step 4: and splicing the SAR panoramic image based on each refocused sub-image.
The embodiment is implemented by adopting the following specific algorithm:
step 2 is realized by a TSMC algorithm and a Chirp Scaling algorithm;
step 3 has two technical schemes:
(1) the method is realized by a traditional PTA algorithm;
(2) the NuPTA algorithm is realized.
With reference to fig. 1 to 7, the specific implementation steps of the present embodiment are as follows:
as shown in fig. 1, the embodiment provides a method for quickly optimizing an airborne/missile-borne synthetic aperture radar image, including the steps of:
s1, overlapping and blocking the SAR echo data of the whole scene in the distance direction and the azimuth direction to generate a plurality of echo sub-arrays;
the requirements for blocking are: the number of overlapping points of the overlapping area of the adjacent sub-blocks in the distance direction and the azimuth direction during the blocking is respectively more than or equal to the distance direction abandon area length N of the panoramic SAR echo datarAnd azimuthal disposal area length Na，Nr、NaThe calculation formula of (2) is as follows:
Figure BDA0003241511590000121
Figure BDA0003241511590000122
wherein, TaIs the synthetic aperture time; PRF is repetition frequency; t ispIs the pulse width; fsIs the range-wise sampling rate.
S2, based on each echo sub-array, performing motion compensation and imaging twice through a TSMC algorithm and a Chirp Scaling algorithm to generate a plurality of corresponding defocus sub-images, wherein generating a corresponding defocus sub-image for each echo sub-array includes:
s21, processing the echo sub-array for the first time to generate a processed echo sub-array, comprising the following steps:
s211, performing first-order compensation on the echo subarray based on a TSMC algorithm, namely compensating the distance-direction non-space-variant error, and generating a first-order compensation echo subarray:
denote the echo subarray by ss (k, r '), where k is the slow time sample index, r' is the range-space coordinate, and the subarray size is N0×N1(number of azimuth pixel pointsX distance-wise pixel points) expressed as:
Figure BDA0003241511590000123
wherein exp (·) is natural exponent operation; j is an imaginary unit; λ is the transmitted signal wavelength; k0＝4πkr/c2Wherein k isrIs the chirp rate of the range-oriented transmit signal, c is the speed of light, ra(k, r) is the slant distance of the bullet eyes under the actual trajectory track, and r is the zero Doppler surface distance of the target; r isa(k, r) contains the slant distance r of the target under the ideal straight-line trackn(k, r), and the skew error caused by the motion error is Δ r (k, r, θ)k) Wherein, thetakIs the instantaneous oblique view angle which varies with the azimuth, i.e. Δ r (k, r, θ)k) Has pore size empty degeneration.
Ignoring θ in the TSMC algorithmkAnd Δ r (k, r, θ)k) Decomposed into space-variant skew error amount delta rv(k,r|r0) And the non-space-variant skew error amount Deltar0(k,r0) Wherein r is0Is a reference distance; then r isa(k, r) can be transformed as follows:
ra(k,r)＝rn(k,r)+Δr(k,r,θk)
≈rn(k,r)+Δr(k,r|θk＝0)
＝rn(k,r)+Δrv(k,r|r0)+Δr0(k,r0)；
Δr0(k,r0) The projection component of the instantaneous slope distance error on the zero Doppler surface can be understood as the following formula:
Figure BDA0003241511590000131
wherein Δ x is the lateral motion error; Δ z is the vertical motion error; h is the height to the ground;
based on Δ r0(k,r0) Distance compensation in structural wavenumber domain to non-space variationPayment function HR(k,Kr) Namely:
Figure BDA0003241511590000132
wherein, KrIs the distance wavenumber.
Based on HR(k,Kr) And compensating the distance to the non-space-variant distance, wherein the calculation formula is as follows:
sS1(k,Kr)＝sS(k,Kr)·HR(k,Kr)；
wherein, sS (K, K)r) An echo subarray obtained after space-domain Fourier transform is carried out on ss (k, r'); sS1(k,Kr) Namely the generated first-order compensation echo sub-array.
S212, performing range pulse compression on the first-order compensation echo subarray:
construction of spatial matched filter Hrmf(Kr)：
Figure BDA0003241511590000133
Based on Hrmf(Kr) To sS1(k,Kr) Performing range-wise pulse compression to obtain sS2(k,Kr) The calculation formula is as follows:
sS2(k,Kr)＝sS1(k,Kr)·Hrmf(Kr)；
then, for sS2(k,Kr) Performing inverse Fourier transform in space domain to obtain ss2(k, r') is calculated as:
Figure BDA0003241511590000141
wherein, sinc (·) is normalized sinc function operation, and the expression is: sinc (x) sin (pi x)/pi x; ss2(k, r') is the first order compensation loop after distance-wise pulse compressionWave array.
S213, performing range unit migration correction on the first-order compensation echo sub-array subjected to range-direction pulse compression to generate a first-order processing echo sub-array:
for ss according to the classical interpolation range migration correction method2(k, r') performing migration correction, wherein the calculation formula is as follows:
Figure BDA0003241511590000142
due to delta rv(k,r|r0) Generally much smaller than the distance resolution element, for ss3(k, r') the following approximation is made:
Figure BDA0003241511590000143
ss3and (k, r') is the generated primary processed echo sub-array.
S22, carrying out secondary processing on the primary processing echo subarray to generate a defocused subimage, comprising the following steps:
s221, performing second-order compensation on the primary processing echo sub-array based on a TSMC algorithm, namely distance direction space-variant error compensation, and generating a second-order compensation echo sub-array:
by ss3The expression (k, r') shows that the range migration curve of the echo sub-array is completely corrected, but the space-variant phase error still exists in the azimuth direction, so that a range space-variant phase compensation function H is constructedv(k,r′)：
Figure BDA0003241511590000144
And (3) performing distance direction space-variant error compensation in an azimuth time domain distance space domain, wherein the calculation formula is as follows:
Figure BDA0003241511590000151
ss4and (k, r') is the generated second-order compensation echo sub-array.
S222, performing azimuth pulse compression on the second-order compensation echo subarray to generate a defocusing subimage:
ss4the first exponential (exp) term in (k, r') represents the ideal azimuth modulated signal, and the matched filter H is constructed in the Doppler domainamf(fa,r′)：
Figure BDA0003241511590000152
Where v is the equivalent ground speed, faThe direction Fourier frequency points;
based on a filter HamfCompleting azimuth pulse compression in the RD domain; then, the echo subjected to azimuth pulse compression is subjected to inverse Fourier transform to generate a defocus sub-image ss5(k, r') is calculated as:
Figure BDA0003241511590000153
wherein, BaIs the Doppler bandwidth, k0Indexing values for the target azimuth coordinates, "+" is a convolution operation symbol; by ss5The calculation formula (k, r') shows that the residual aperture space-variant phase error is equivalent to a fuzzy filter, resulting in ss5(k, r') appear defocused in the azimuth direction, ss5(k, r') is the generated defocus subimage.
S3, based on each defocused sub-image, eliminating the RD-domain residual aperture error by the NuPTA algorithm, generating a plurality of corresponding refocused sub-images, wherein generating a corresponding refocused sub-image for each defocused sub-image comprises the steps of:
s31, based on each defocused sub-image, calculating the residual aperture error and the instantaneous slope distance between the radar and the target under the ideal straight-line track, wherein the calculation formula is as follows:
Figure BDA0003241511590000161
Figure BDA0003241511590000162
wherein δ r (k) is a residual aperture space-variant error; r isn(k) The target is the radar and target instantaneous slope distance under an ideal straight line track.
S32, based on each defocused sub-image and delta r (k), rn(k) Calculating the Doppler frequency corresponding to each pulse of the defocused sub-image under the residual aperture error by using the frequency equal to the first derivative of the phase, wherein the calculation formula is as follows:
Figure BDA0003241511590000163
wherein f (k) is the true Doppler frequency corresponding to the kth pulse of the defocused sub-image.
S33, based on delta r (k) and f (k), calculating a residual aperture compensation function corresponding to the azimuth Fourier frequency points through a sinc interpolation algorithm, wherein the calculation formula is as follows:
Figure BDA0003241511590000164
s34 based on HNuPTA(fa,r0) And finishing the residual aperture error compensation in the defocus subimage RD field:
for defocused sub-images ss5(k, r') performs an azimuthal Fourier transform to generate Ss5(faR'), and then the compensation of the residual aperture space-variant phase error is completed by the following formula:
Ss6(fa,r′)＝Ss5(fa,r′)·HNuPTA(fa,r0)；
then to the Ss6(faR') are inverse Fourier transformed in azimuth to obtain a refocused subimage ss without residual phase error6(k,r′)。
S4, splicing the SAR panorama based on the refocused sub-images, comprising the following steps:
s41, judging whether all echo subarray processing is finished:
if not, returning to S2 to process the next echo subarray;
if yes, the process proceeds to S42.
S42, splicing the processed refocus sub-images into a panoramic image:
refocusing sub-images ss6(k, r') performing a peripheral cropping process, discarding the overlapping portion, and generating non-overlapping sub-images ss7(k, r') expressed as:
ss7(k,r′)＝ss6(k+Na,r′+Nr),
Figure BDA0003241511590000171
all non-overlapping sub-images ss7And (k, r') directly splicing according to the sequence before cutting to obtain the final processed SAR panorama.
The above solution is the method for optimizing the image of the airborne/missile-borne synthetic aperture radar implemented based on the NuPTA algorithm provided in this embodiment, and meanwhile, this embodiment also provides a method for quickly optimizing the image of the airborne/missile-borne synthetic aperture radar implemented based on the conventional PTA algorithm, where the steps S1, S2, and S4 are completely the same as the steps S1, S2, and S4 based on the NuPTA algorithm, and only the step S3 is replaced with the following step S3':
s3', based on each defocused sub-image, eliminating the residual aperture error in the RD domain by the conventional PTA algorithm to generate a refocused sub-image, comprising the steps of:
s31', calculating the residual aperture error compensation function H of each defocused sub-image by using the relation between the ideal azimuth frequency and the instantaneous squint anglePTA(fa,r0) The calculation formula is as follows:
Figure BDA0003241511590000172
s32' based on HPTA(fa,r0) And finishing the residual aperture error compensation in the defocus subimage RD field:
for defocused sub-images ss5(k, r') performs an azimuthal Fourier transform to generate Ss5(faR'), and then the compensation of the residual aperture space-variant phase error is completed by the following formula:
Ss6(fa,r′)＝Ss5(fa,r′)·HPTA(fa,r0)；
then to the Ss6(faR') are inverse Fourier transformed in azimuth to obtain a refocused subimage ss without residual phase error6(k,r′)。
To further illustrate the technical effect of the NuPTA algorithm of the present invention optimized compared with the conventional PTA algorithm, as shown in fig. 2 to 7, the present embodiment provides the following simulation tests:
(1) simulation conditions
As shown in FIG. 2, the imaging scene is set as a cross scene composed of 17 targets, wherein the targets 1-17 are represented by numerals 1-17 in FIG. 2, the distance and azimuth interval of each adjacent target are respectively set to 5 meters and 1.6 meters, the scene distance center is set to 750 meters, and the azimuth center is set to 3000 meters. The vertical and lateral motion errors occurring during the echo data acquisition process are shown in fig. 3, and the simulation parameters of the radar system are shown in table 1.
TABLE 1 System simulation parameters
Figure BDA0003241511590000181
(2) Simulation content and result analysis
Simulation 1:
step 1, calculating N according to parameters in table 1r＝90、NaThus, the adjacent sub-block echo overlapping portions are set to 100 points in the distance direction and 433 points in the azimuth direction; according to the parameters, overlapping and blocking the range direction and the azimuth direction of the SAR echo data of the whole scene to generate a plurality of echo sub-arrays.
And 2, performing motion compensation and image processing on each sub-block based on a TSMC algorithm and a Chirp Scaling imaging algorithm to generate a defocused sub-image.
And step 3, generating refocusing sub-images without residual phase errors by respectively using the PTA algorithm and the NuPTA algorithm for each defocused sub-image, as shown in figure 4, comparing with a true value, it can be known that the accuracy of the NuPTA algorithm is higher than that of the PTA algorithm, and this explains the fundamental reason that the refocusing capability of the NuPTA algorithm is better than that of the PTA algorithm.
Step 4, as shown in fig. 5a, directly splicing the sub-images after each PTA algorithm compensation after cutting off the overlapping part to form a PTA algorithm compensated whole scene image; as shown in fig. 5b, the sub-images compensated by the NuPTA algorithm are spliced according to the same processing flow to form a complete scene image compensated by the NuPTA algorithm. To further analyze the improvement of the compensation performance of the NuPTA algorithm relative to the PTA algorithm, fig. 6a, fig. 6b, and fig. 6c show the point spread function comparison of the target 1, the target 5, and the target 10 in the scene after the processing by the PTA algorithm and the NuPTA algorithm, respectively. As is well known, under the condition of no windowing, the ideal PSLR (peak side lobe ratio) after pulse compression of the chirp signal is-13.6 dB, as can be seen from fig. 5a and 5b, the PSLR of the three targets processed by the PTA algorithm is-3.2 dB, -11dB and-3 dB respectively, and the PSLR of the three targets processed by the NuPTA algorithm is-13.7 dB, -13.9dB and-12.8 dB respectively, which shows that the NuPTA algorithm has more excellent performance of compensating the residual aperture space-variant error.
Simulation 2: the method provided by the embodiment is used for processing the measured data, the measured data comes from a C wave band, the pulse width is 10.4 microseconds, the pulse repetition frequency is 1673Hz, the range-direction sampling rate is 240MHz, the synthetic aperture time is 4.07 seconds, the average value of the vertical motion error is 28.2 meters, and the average value of the horizontal motion error is 11.8 meters. The results of the echo data compensated by the NuPTA algorithm and the PTA algorithm are shown in fig. 7a and 7b, and the NuPTA algorithm again shows better compensation performance than the PTA algorithm by comparing the focusing effect of the strong targets.
In summary, the invention provides a method for rapidly optimizing an airborne/missile-borne synthetic aperture radar image, which accurately obtains a residual motion error amount corresponding to a doppler frequency under any trajectory by adopting a numerical calculation mode, and improves the sub-block phase error compensation precision; and completing the phase error compensation of the whole scene image by a block strategy. The method realizes high-precision residual space-variant motion error compensation, has the advantages of simple principle, less calculation amount, higher accuracy and stronger robustness, and high operation speed, and is suitable for actual processing.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.

Claims (8)

1. A method for quickly optimizing an airborne/missile-borne synthetic aperture radar image is characterized by comprising the following steps:
s1, overlapping and partitioning the panoramic SAR echo data in the distance direction and the azimuth direction to generate a plurality of echo sub-arrays;
s2, based on each echo subarray, performing motion compensation and imaging processing to generate corresponding defocusing subimages;
s3, based on each defocusing sub-image, eliminating the residual aperture error of the RD domain, and generating a corresponding refocusing sub-image;
and S4, splicing the SAR panorama based on each refocused sub-image.
2. The method for fast optimizing airborne/missile-borne synthetic aperture radar images as claimed in claim 1, wherein in step S1:
the requirements for blocking are: the number of overlapping points of the overlapping area of the adjacent sub-blocks in the distance direction and the azimuth direction during the blocking is respectively more than or equal to the distance direction abandoning area length N of the panoramic SAR echo datarAnd azimuthal disposal area length Na，Nr、NaThe calculation formula of (2) is as follows:
Figure FDA0003241511580000011
Figure FDA0003241511580000012
wherein, TaIs the synthetic aperture time; PRF is repetition frequency; t ispIs the pulse width; fsIs the range-wise sampling rate.
3. The method for rapidly optimizing airborne/missile-borne synthetic aperture radar images as claimed in claim 2, wherein the step S2 is realized by a TSMC algorithm and a Chirp Scaling algorithm, and comprises the steps of:
s21, performing primary processing on the echo sub-array to generate a primary processed echo sub-array;
and S22, carrying out secondary processing on the primary processing echo subarray to generate a defocusing subimage.
4. The method for fast optimizing airborne/missile-borne synthetic aperture radar images as claimed in claim 3, wherein the step S21 comprises:
s211, performing first-order compensation, namely distance direction non-space-variant error compensation, on the echo subarray based on a TSMC algorithm to generate a first-order compensation echo subarray:
denote the echo subarray by ss (k, r '), where k is the slow time sample index, r' is the range-wise spatial domain coordinate, and the subarray size is N0×N1(number of azimuth pixels × number of distance pixels), the expression is:
Figure FDA0003241511580000021
wherein exp (·) is natural exponent operation; j is an imaginary unit; λ is the transmitted signal wavelength; k0＝4πkr/c2Which isIn krIs the chirp rate of the range-oriented transmit signal, c is the speed of light, ra(k, r) is the slant distance of the bullet eyes under the actual trajectory track, and r is the zero Doppler surface distance of the target; r isa(k, r) contains the slant distance r of the target under the ideal straight-line trackn(k, r), and the skew error caused by the motion error is Δ r (k, r, θ)k) Wherein, thetakIs the instantaneous oblique view angle which varies with the azimuth, i.e. Δ r (k, r, θ)k) Has pore size empty change;
ignoring θ in the TSMC algorithmkAnd Δ r (k, r, θ)k) Decomposed into space-variant skew error amount delta rv(k,r|r0) And the non-space-variant skew error amount Deltar0(k,r0) Wherein r is0Is a reference distance; then r isa(k, r) can be transformed as follows:
ra(k,r)＝rn(k,r)+Δr(k,r,θk)
≈rn(k,r)+Δr(k,r|θk＝0)
＝rn(k,r)+Δrv(k,r|r0)+Δr0(k,r0)；
Δr0(k,r0) The projection component of the instantaneous slope distance error on the zero Doppler surface can be understood as the following formula:
Figure FDA0003241511580000022
wherein Δ x is the lateral motion error; Δ z is the vertical motion error; h is the height to the ground; based on Δ r0(k,r0) Constructing a compensation function H of the wave number domain with non-space variation of the distance directionR(k,Kr) Namely:
Figure FDA0003241511580000023
wherein, KrIs the distance wave number;
based on HR(k,Kr) Compensating the distance to a non-space-variant distance and calculating the distanceThe formula is as follows:
sS1(k,Kr)＝sS(k,Kr)·HR(k,Kr)；
wherein, sS (K, K)r) An echo subarray obtained after space-domain Fourier transform is carried out on ss (k, r'); sS1(k,Kr) The first-order compensation echo subarray is generated;
s212, performing range pulse compression on the first-order compensation echo subarray:
construction of spatial matched filter Hrmf(Kr)：
Figure FDA0003241511580000034
Based on Hrmf(Kr) To sS1(k,Kr) Performing range-wise pulse compression to obtain sS2(k,Kr) The calculation formula is as follows:
sS2(k,Kr)＝sS1(k,Kr)·Hrmf(Kr)；
then, for sS2(k,Kr) Performing inverse Fourier transform in space domain to obtain ss2(k, r') is calculated as:
Figure FDA0003241511580000031
wherein, sinc (·) is normalized sinc function operation, and the expression is: sinc (x) sin (pi x)/pi x; ss2(k, r') is the first-order compensation echo subarray after the distance pulse compression;
s213, performing range unit migration correction on the first-order compensation echo sub-array after range-direction pulse compression to generate a first-order processing echo sub-array:
for ss according to the classical interpolation range migration correction method2(k, r') performing migration correction, wherein the calculation formula is as follows:
Figure FDA0003241511580000032
due to delta rv(k,r|r0) Generally much smaller than the distance resolution element, for ss3(k, r') the following approximation is made:
Figure FDA0003241511580000033
ss3(k, r') is the generated primary processed echo sub-array.
5. The method for fast optimizing airborne/missile-borne synthetic aperture radar images as claimed in claim 4, wherein the step S22 comprises:
s221, performing second-order compensation on the primary processing echo sub-array based on a TSMC algorithm, namely distance direction space-variant error compensation, and generating a second-order compensation echo sub-array:
by ss3The expression (k, r') shows that the range migration curve of the echo sub-array is completely corrected, but the space-variant phase error still exists in the azimuth direction, so that a range-to-space-variant phase compensation function H is constructedv(k,r′)：
Figure FDA0003241511580000041
And (3) performing distance direction space-variant error compensation in an azimuth time domain distance space domain, wherein the calculation formula is as follows:
Figure FDA0003241511580000042
ss4(k, r') is the generated second order compensation echo sub-array;
s222, performing azimuth pulse compression on the second-order compensation echo sub-array to generate a defocusing sub-image:
ss4the first exponential (exp) term in (k, r') represents the ideal azimuth modulated signal, and the matched filter H is constructed in the Doppler domainamf(fa,r′)：
Figure FDA0003241511580000043
Where v is the equivalent ground speed, faThe direction Fourier frequency points;
based on a filter HamfCompleting azimuth pulse compression in the RD domain; then, the echo subjected to azimuth pulse compression is subjected to inverse Fourier transform to generate a defocus sub-image ss5(k, r') is calculated as:
Figure FDA0003241511580000051
wherein, BaIs the Doppler bandwidth, k0Indexing values for the target azimuth coordinates, "+" is a convolution operation symbol; by ss5The calculation formula (k, r') shows that the residual aperture space-variant phase error is equivalent to a fuzzy filter, resulting in ss5(k, r') appear defocused in the azimuth direction, ss5(k, r') is the generated defocus sub-image.
6. The method for fast optimizing airborne/missile-borne synthetic aperture radar images as claimed in claim 5, wherein the step S3 is realized by NuPTA algorithm, comprising the steps of:
s31, based on each defocused sub-image, calculating the residual aperture error and the instantaneous slope distance between the radar and the target under the ideal straight-line track, wherein the calculation formula is as follows:
Figure FDA0003241511580000052
Figure FDA0003241511580000055
wherein δ r (k) is a residual aperture space-variant error; r isn(k) The target is the radar and target instantaneous slope distance under an ideal straight line track;
s32, based on each defocused sub-image and delta r (k), rn(k) Calculating the Doppler frequency corresponding to each pulse of the defocused sub-image under the residual aperture error by using the frequency equal to the first derivative of the phase, wherein the calculation formula is as follows:
Figure FDA0003241511580000053
wherein, f (k) is the true Doppler frequency corresponding to the kth pulse of the defocused sub-image;
s33, based on delta r (k) and f (k), calculating a residual aperture compensation function corresponding to the azimuth Fourier frequency points through a sinc interpolation algorithm, wherein the calculation formula is as follows:
Figure FDA0003241511580000054
s34 based on HNuPTA(fa,r0) And finishing the residual aperture error compensation in the defocus subimage RD field:
for defocused sub-images ss5(k, r') performs an azimuthal Fourier transform to generate Ss5(faR'), and then the compensation of the residual aperture space-variant phase error is completed by the following formula:
Ss6(fa,r′)＝Ss5(fa,r′)·HNuPTA(fa,r0)；
then to the Ss6(faR') are inverse Fourier transformed in azimuth to obtain a refocused subimage ss without residual phase error6(k,r′)。
7. The method for fast optimizing airborne/missile-borne synthetic aperture radar images as claimed in claim 5, wherein the step S3 is realized by PTA algorithm, comprising the steps of:
s31, calculating the residual aperture error compensation function H of each defocused sub-image by using the relation between the ideal azimuth frequency and the instantaneous squint anglePTA(fa,r0) The calculation formula is as follows:
Figure FDA0003241511580000061
s32 based on HPTA(fa,r0) And finishing the residual aperture error compensation in the defocus subimage RD field:
for defocused sub-images ss5(k, r') performs an azimuthal Fourier transform to generate Ss5(faR'), and then the compensation of the residual aperture space-variant phase error is completed by the following formula:
Ss6(fa,r′)＝Ss5(fa,r′)·HPTA(fa,r0)；
then to the Ss6(faR') are inverse Fourier transformed in azimuth to obtain a refocused subimage ss without residual phase error6(k,r′)。
8. The method for fast optimizing airborne/missile-borne synthetic aperture radar images according to claim 6 or 7, wherein the step S4 comprises:
s41, judging whether all the echo subarray processing is finished:
if not, returning to S2 to process the next echo subarray;
if yes, go to S42;
s42, splicing the processed refocused sub-images into a panoramic image:
refocusing sub-images ss6(k, r') performing a peripheral cropping process, discarding the overlapping portion, and generating non-overlapping sub-images ss7(k, r') expressed as:
Figure FDA0003241511580000062
all of the non-overlapping sub-images ss are then processed7And (k, r') directly splicing according to the sequence before cutting to obtain the final processed SAR panorama.
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