CN103941255A - ISAR motion compensation method based on design structuration Gram matrix - Google Patents

ISAR motion compensation method based on design structuration Gram matrix Download PDF

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CN103941255A
CN103941255A CN201310016499.0A CN201310016499A CN103941255A CN 103941255 A CN103941255 A CN 103941255A CN 201310016499 A CN201310016499 A CN 201310016499A CN 103941255 A CN103941255 A CN 103941255A
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matrix
motion compensation
range
algorithm
process according
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俞翔
宋伟
朱岱寅
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Nanjing University of Aeronautics and Astronautics
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Nanjing University of Aeronautics and Astronautics
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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
    • G01S13/9029—SAR image post-processing techniques specially adapted for moving target detection within a single SAR image or within multiple SAR images taken at the same time
    • 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
    • G01S7/40—Means for monitoring or calibrating
    • 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
    • G01S13/904—SAR modes
    • G01S13/9064—Inverse SAR [ISAR]

Abstract

The invention discloses an ISAR motion compensation method based on a design structuration Gram matrix, which belongs to the technical field of radar imaging. Motion compensation is an important step in an ISAR imaging algorithm. According to the invention, motion compensation is attributed to a multi parameter estimation problem. An optimization theory based on the design structuration Gram matrix provides a new motion compensation method. The method can be divided into two parts of range alignment and phase compensation. According to a range alignment algorithm, the correlations between all range profiles approach the maximum at the same time to realize offset estimation. According to a phase compensation algorithm, by analyzing a signal model, an optimal matrix is derived, and an optimization method is used to extract a phase error. According to the invention, the correlations between echo pulses are fully used; high precision estimation is carried out on the range offset and the phase error; the algorithms have high robustness, and are suitable for the condition of echo random sampling; and a compression sensing technology has a practical application prospect in the field of ISAR imaging.

Description

A kind of ISAR motion compensation process based on project organization Gram matrix
Technical field
The present invention relates to a kind of ISAR motion compensation process based on project organization Gram matrix, belong to the technical field of radar imagery.
Background technology
In general, the motion of the relative radar of noncooperative target is unpredictable.Conventionally the relative motion between radar and target can be decomposed into translation and rotative component, wherein only have rotative component to work to the reconstruction of target picture, and translation component is disadvantageous to imaging.Therefore before imaging, conventionally need to complete operation of motion compensation to remove translation component to each Range Profile.Motion compensation is the gordian technique of ISAR imaging, and its performance will directly affect the quality of final imaging.
Because the kinematic parameter such as athletic posture and flight path of noncooperative target cannot accurately be known, therefore the required parameter of motion compensation all will estimate from echo, and in fact motion compensation problem is exactly multiparameter estimation problem.Motion compensation can be divided into range-aligned and two steps of phase compensation.
Range-aligned is as the basis of phase compensation and imaging, and object is that the side-play amount by proofreading and correct each Range Profile is aimed at each Range Profile.Existing range-aligned algorithm mainly can be divided into Range Profile simple crosscorrelation and aim at and global alignment two classes.The former has by typical algorithm: adjacent envelope cross-correlation method, time domain and frequency domain signal integration cross-correlation method, envelope maximum modified kurtosis method etc.This class algorithm depends on the correlativity between Range Profile.And global alignment class algorithm is generally using certain overall criterion as aiming at foundation, make certain performance index reach optimum, such as global alignment algorithm, overall minimum entropy algorithm etc. by the method for iteration.The correlativity that this class algorithm is adjusted the distance between picture is less demanding, and can suppress preferably kick and drift error, and especially ought run into echo has the situation of interruption or echo sudden change, and these class methods have better robustness compared with the former.This two classes algorithm is all to utilize two correlativitys between Range Profile to realize range-aligned, and therefore range-aligned precision is general.
Range-aligned has solved the Range Profile offset problem of range unit rank substantially, in order further to eliminate the impact of translation on imaging, must carry out the phase error that phase compensation causes to eliminate translation.At present existing multiple phase compensating method, as Doppler Centroid Tracking Method, phase gradient self-focusing method (PGA) and proper vector Phase Compensation Algorithm (MLE) etc.Wherein first two algorithm obtains phase error by the phase gradient of estimating two neighbor distance pictures, owing to there being accumulation of error effect in the process of estimating phase error, has caused the loss of arithmetic accuracy; And being proper vector direct estimation by solving Range Profile covariance matrix, MLE algorithm goes out phase error.Comparatively speaking, the estimated accuracy of MLE algorithm will be higher than first two algorithm, still because this algorithm will, to covariance matrix feature decomposition, be difficult to apply in engineering practice so this algorithm is subject to the restriction of matrix dimension.
Summary of the invention
Technical matters to be solved by this invention is the deficiency for above-mentioned background technology, a kind of ISAR motion compensation process based on project organization Gram matrix is provided, effectively reduce accumulation and the kick error in movement compensation process, introduced, improved robustness and the image quality of algorithm.
The present invention adopts following technical scheme for achieving the above object:
Based on an ISAR motion compensation process for project organization Gram matrix, be divided into range-aligned and Phase Compensation Algorithm two parts, comprise the steps:
Step 1, range-aligned algorithm has adopted the correlativity allowing between any distance picture approach peaked criterion simultaneously, realizes the estimation of Range Profile side-play amount by the method for project organization Gram matrix, is specifically implemented as follows:
Step 1-1, according to range unit size delta r igenerate linear phase side-play amount matrix Q, be expressed as follows:
Q＝[q 1|q 2|…|q M]
q i(f r，Δr i)＝exp(j2πf rΔr i)
Wherein M is Range Profile number, f rrepresent frequency domain discrete sampling point.
Step 1-2, according to above-mentioned criterion, designs structuring Gram matrix and solve be expressed as follows:
G ~ 1 / 2 = 1 M G ~
Step 1-3, by Range Profile two-dimensional matrix along distance to transforming to frequency domain and adopting following formula normalization:
p ^ i ( f r ) = p ~ i ( f r ) ⟨ p ~ i ( f r ) * , p ~ i ( f r ) ⟩
Wherein with it is respectively matrix with column vector, represent the one-dimensional range profile of frequency domain.
Step 1-4, solution matrix pole divisor U, and solve X matrix by following formula
X = U · G ~ 1 / 2
Step 1-5, utilizes matrix Q to estimate the least square solution of following formula, thereby obtains the side-play amount linear phase q of each Range Profile i
Step 1-6, utilizes the side-play amount linear phase q estimating iupgrade respectively with matrix;
Step 1-7, repeating step 1-4～step 1-6, judges stopping criterion for iteration.If iteration finishes, will matrix, along distance to converting back signal domain, obtains the complete Range Profile of range-aligned.
Step 2 has been derived optimum ideal matrix by analyzing echo signal model, thereby has been utilized the method estimating phase error of project organization Gram matrix in Phase Compensation Algorithm, is specifically implemented as follows:
Step 2-1, by analyzing echo signal model, designs desirable structuring Gram matrix and solve be expressed as follows:
G ~ 1 / 2 = 1 M G ~
Step 2-2, by the Range Profile matrix S that completes range-aligned along orientation to transforming to frequency domain, using the strong scattering point in each range unit as Doppler center, this point is moved to Doppler 0Hz by ring shift, and the matrix after ring shift is obtained along orientation to converting back the each column vector of signal domain normalization matrix;
Step 2-3, solution matrix pole divisor U, and solve X matrix by following formula
X = U · G ~ 1 / 2
Step 2-4, utilizes following formula to solve phase error matrix Ф
for the column vector of phase error matrix Ф, can obtain the conjugation of phase error by following formula:
Wherein x m(n) and be respectively matrix X and column vector;
Step 2-5, with the phase error that estimates upgrade respectively S and matrix;
Step 2-6, repeating step 2-5～step 2-3, until meet stopping criterion for iteration;
Step 2-7, repeating step 2-2～step 2-6, until meet stopping criterion for iteration, completes phase compensation.
The present invention adopts technique scheme, has following beneficial effect: aspect range-aligned algorithm, realized the expression to correlativity between any distance picture by structure Range Profile cross-correlation matrix, ensured the precision of range-aligned algorithm; Method by project organization Gram matrix obtains optimal objective matrix, has determined a kind of new range-aligned criterion; And estimate ranging offset amount by the method that replaces iteration, and avoid the evaluated error accumulation of classic method, improve the range-aligned precision of ISAR echoed signal.Aspect Phase Compensation Algorithm, design desirable optimum Gram matrix by evaluating objects echo signal model, determine a kind of new phase compensation criterion; To Signal cross correlation matrix, improve robustness and the estimated accuracy of algorithm by structure orientation; Estimate phase error by the method that replaces iteration, avoided the evaluated error accumulation of classic method, improved the focusing quality of ISAR image.
Brief description of the drawings
Fig. 1 is movement compensating algorithm process flow diagram.
Fig. 2 is the result of yake-42 measured data (256 pulses) through each range-aligned algorithm process. (a) before range-aligned; (b) adjacent cross correlation algorithm; (c) accumulation cross correlation algorithm; (d) RA-DSGM algorithm of the present invention.
Fig. 3 is the result of citatan measured data (128 pulses) through each range-aligned algorithm process. (a) before range-aligned; (b) adjacent cross correlation algorithm; (c) accumulation cross correlation algorithm; (d) RA-DSGM algorithm of the present invention.
Fig. 4 is the imaging results of yake-42 measured data (256 pulses) after each range-aligned algorithm process. (a) range-aligned not; (b) adjacent cross correlation algorithm; (c) accumulation cross correlation algorithm; (d) RA-DSGM algorithm of the present invention.
Fig. 5 is the imaging results of citatan measured data (128 pulses) after each range-aligned algorithm process. (a) range-aligned not; (b) adjacent cross correlation algorithm; (c) accumulation cross correlation algorithm; (d) RA-DSGM algorithm of the present invention.
Fig. 6 is yake-42 measured data (256 pulses) result of each range-aligned algorithm process after random perturbation. (a) range-aligned not; (b) adjacent cross correlation algorithm; (c) accumulation cross correlation algorithm; (d) RA-DSGM algorithm of the present invention.
Fig. 7 is the Monte Carlo simulation contrast experiment of two kinds of Phase Compensation Algorithms in literary composition. (a) average; (b) variance.
Fig. 8 is the imaging results of yake-42 measured data (512 pulses) through two kinds of Phase Compensation Algorithm processing. (a) not phase compensation; (b) PGA algorithm; (c) PC-DSGM algorithm of the present invention; (d) in subgraph the orientation of mark scattering point to the comparison of sectional view.
Fig. 9 be yake-42 measured data orientation to stochastic sampling (128 pulses) imaging results through two kinds of Phase Compensation Algorithm processing. (a) not phase compensation; (b) PGA algorithm; (c) PC-DSGM; (d) in subgraph the orientation of mark scattering point to the comparison of sectional view.
Figure 10 be yake-42 measured data orientation to stochastic sampling (128 pulses) imaging results through two kinds of Phase Compensation Algorithm processing. (a) not phase compensation; (b) PGA algorithm; (c) PC-DSGM algorithm; (d) in subgraph the orientation of mark scattering point to the comparison of sectional view.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention is described in further detail:
More than introduced a kind of brand-new motion compensation process, this motion compensation process is divided into range-aligned and Phase Compensation Algorithm, and this section will utilize ISAR measured data respectively these two kinds of algorithms are made checking and analyzed.Measured data is echo track production being obtained by ground X-band wideband pulse radar, and radar bandwidth is 400MHz, and observed object is the medium-sized jet plane of yake-42 and the small-sized jet plane of citatan.In order to study the motion compensation process in the situation of random pulses recurrence interval (PRI), we to stochastic sampling, and will adopt the image of MP (Matched Pursuit) algorithm reconstruct target to echo data orientation.
Range-aligned experiment:
In range-aligned experiment, we randomly draw respectively 256 and 128 pulses as observation sample to the actual measurement echo data of yake-42 and citatan baby plane, and orientation is made as respectively 20 and 8 to degree of rarefication.In order to obtain target two dimensional image clearly, first our one-dimensional range profile range-aligned after to pulse pressure.Adjacent simple crosscorrelation and accumulation simple crosscorrelation are the classic algorithm in range-aligned algorithm, and the RA-DSGM range-aligned algorithm that experiment has adopted these two kinds of algorithms and the present invention to propose compares.
Fig. 2 is the echo range-aligned result that one group of target is yake-42, wherein Fig. 2 (a) is the situation before range-aligned, and Fig. 2 (b)～(d) is respectively the range-aligned result of adjacent simple crosscorrelation, accumulation simple crosscorrelation and algorithm of the present invention.Fig. 3 is the echo range-aligned result that one group of target is citatan baby plane.The alignment result that can find out adjacent cross correlation algorithm from above two groups of experimental results is not good; The alignment result of accumulation cross correlation algorithm is seen better on the whole, but local error is still larger; The whole and part alignment result of algorithm of the present invention is all comparatively desirable.
For further analyzing the performance of range-aligned algorithm, need to carry out phase compensation and reconstruct the two dimensional image of target the range-aligned result of above each algorithm.For ensureing the quality of final imaging, we have adopted PC-DSGM Phase Compensation Algorithm and MP restructing algorithm that the present invention proposes here.The imaging results of Fig. 4 and Fig. 5 is corresponding one by one with the range-aligned result of Fig. 2 and Fig. 3 respectively, and wherein Fig. 4 (a) and Fig. 5 (a) cannot tell objective contour.Fig. 4 (b) and (c) can roughly tell objective contour, but the still more difficult resolution of the local detail part of target.The image of Fig. 5 (b) and (c) target can not focus on well, is difficult to tell objective contour.And can find out that from Fig. 4 corresponding to RA-DSGM algorithm (d) and Fig. 5 (d) objective contour and detail section are all comparatively clear.Comparison diagram 4 and picture contrast corresponding to the each subgraph of Fig. 5 respectively, result shows that the contrast of Fig. 4 (d) and Fig. 5 (d) is the highest.
In some cases, undergo mutation because the motion of the relative radar of target makes its scattering properties, caused the correlativity of adjacent echo to reduce.For verification algorithm robustness in this case, the echo order of we random perturbation Fig. 2 (a) and distance, to side-play amount, still realize range-aligned with above-mentioned three kinds of algorithms, as shown in Figure 6.Result shows that in this kind of situation, adjacent simple crosscorrelation all can occur larger estimated bias with accumulation cross correlation algorithm, and RA-DSGM range-aligned algorithm is comparatively sane.
Phase compensation experiment:
In phase compensation experiment, we have randomly drawed respectively 128 pulses as observation sample to the echo data of two groups of yake-42, and orientation is made as 20 to degree of rarefication, and all data that need explanation to adopt here have all been passed through range-aligned processing.
In ISAR Phase Compensation Algorithm, PGA algorithm is comparatively classical and conventional Phase Compensation Algorithm.The PC-DSGM Phase Compensation Algorithm and the PGA algorithm that below the present invention are proposed are made comparisons, and analyze the performance at both.Fig. 7 is the performance curve of two kinds of algorithm Monte Carlo simulation experiments, and wherein Fig. 7 (a) and Fig. 7 (b) are respectively mean value and the variance of phase error estimation and phase error.Here need to illustrate SAR the signal interference ratio of ISAR echo data before phase compensation conventionally all below 0dB, therefore we only consider the situation below 0dB, can find out that from figure (6) performance of PC-DSGM algorithm is better.Experiment below will adopt measured data further to compare the performance of two kinds of algorithms.
First, we verify in orientation to the phase compensation effect under uniform sampling condition, as Fig. 8 (b) (c) as shown in, both phase compensation effects are suitable and all more satisfactory, and this conclusion has further been confirmed to sectional view in the orientation of the contrast of image and Fig. 8 (d).Next continue relatively in orientation to the performance of two kinds of algorithms under random PRI echo condition, as Fig. 9 (b) (c) and Figure 10 (b) (c) as shown in, PGA algorithm evaluated error under this condition is larger, focusedimage well, and PC-DSGM algorithm can normally focus on, target image profile is comparatively clear.The orientation of the contrast of image and Fig. 9 (d), Figure 10 (d) shows to the result of sectional view, and the Phase Compensation Algorithm that the present invention proposes is more effective.
The Optimum Theory that the present invention is based on project organization Gram matrix has proposed a kind of brand-new movement compensating algorithm.Experimental result shows, RA-DSGM algorithm has higher accuracy and stronger robustness, and can be directly used in the range-aligned problem of orientation to stochastic sampling echo that solve.PC-DSGM algorithm is applicable to the phase compensation of orientation to uniform sampling and stochastic sampling echo data, and from Monte Carlo simulation experiment, this algorithm still has higher estimated accuracy in low signal-to-noise ratio situation.Therefore the proposition of this motion compensation process will make compressed sensing technology have actual application prospect in ISAR imaging field.

Claims (14)

1. the ISAR motion compensation process based on project organization Gram matrix, comprises the steps:
(1) range-aligned:
<1-1> generates the linear phase matrix taking ranging offset amount as variable;
The optimum Gram matrix of <1-2> design ideal;
<1-3> by Range Profile matrix along distance to transforming to frequency domain and doing normalized;
<1-4> obtains correction matrix by the pole divisor that solves specified matrix;
<1-5> is approached correction matrix and is completed the estimation of Range Profile side-play amount by least squares estimate;
<1-6> Range Profile offset compensation;
<1-7> by Range Profile matrix along distance to converting back signal domain.
(2) phase compensation:
The optimum Gram matrix of <2-1> design ideal;
<2-2> by the orientation of echoed signal matrix to average Doppler frequency zero setting normalization;
<2-3> obtains correction matrix by the pole divisor that solves specified matrix;
<2-4> approaches correction matrix by least squares estimate and utilizes the empty unchangeability of phase error to estimate phase error;
<2-5> phase error compensation;
The imaging of <2-6> direction Fourier transform.
2. motion compensation process according to claim 1, wherein step <1-1> generates linear phase side-play amount matrix according to range unit size.
3. motion compensation process according to claim 1, wherein step <1-2> is the high correlation of utilizing ISAR echoed signal, designs desirable optimum Gram matrix.Matrix form is as follows:
4. motion compensation process according to claim 1, wherein step <1-3> is along distance to transforming to frequency domain and by the Range Profile normalization obtaining by Range Profile two-dimensional matrix.
5. motion compensation process according to claim 1, wherein step <1-4> is the pole divisor that utilizes the result solution matrix of step <1-2> and step <1-3>, to obtain correction matrix.
6. motion compensation process according to claim 1, wherein step <1-5> utilizes least squares estimate to complete the estimation of Range Profile side-play amount by the result of approximation step <1-4>.
7. motion compensation process according to claim 1, wherein step <1-6> utilizes the result of step <1-5> to realize the offset linear phase compensation at frequency domain to one-dimensional range profile.
8. motion compensation process according to claim 1, wherein step <1-7> be to the result distance of step <1-6> to convert back signal domain, the adjust the distance range-aligned of picture of realization.
9. motion compensation process according to claim 1, wherein step <2-1> is the phase characteristic that utilizes ISAR echoed signal, designs desirable optimum Gram matrix.Matrix form is as follows:
10. motion compensation process according to claim 1, wherein step <2-2> has utilized the result of step <1-7>, by signal matrix orientation complete range-aligned to average Doppler frequency zero setting and to orientation to signal normalization.
11. motion compensation process according to claim 1, wherein step <2-3> utilizes the pole divisor of the result solution matrix of step <2-1> and step <2-2>, to obtain correction matrix.
12. motion compensation process according to claim 1, wherein step <2-4> has utilized least squares estimate, and the result by approximation step <2-4> also utilizes the empty unchangeability of phase error to realize the estimation of phase error.
13. motion compensation process according to claim 1, wherein step <2-5> utilizes the result of step <2-4> to eliminate orientation to phase error.
14. motion compensation process according to claim 1, wherein step <2-6> obtains fine-focused image by the result orientation to step <2-5> to Fourier transform.
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CN108415015A (en) * 2018-03-14 2018-08-17 哈尔滨工业大学 Ship Target three-dimensional InISAR imaging methods under a kind of sparse aperture
CN108732555A (en) * 2018-06-04 2018-11-02 内蒙古工业大学 A kind of method for the method and automatic Pilot array microwave imaging motion compensation obtaining kinematic error vector
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CN109031299B (en) * 2018-08-30 2022-03-04 西安电子科技大学 ISAR (inverse synthetic aperture radar) translation compensation method based on phase difference under low signal-to-noise ratio condition
CN109031299A (en) * 2018-08-30 2018-12-18 西安电子科技大学 ISAR translational compensation method under Low SNR based on phase difference
CN110286375B (en) * 2019-05-16 2021-06-08 哈尔滨工业大学（深圳） LS high-order fast motion compensation method and system for near real-time ISAR imaging
CN110286375A (en) * 2019-05-16 2019-09-27 哈尔滨工业大学（深圳） The quick motion compensation process of LS high-order and system towards near real-time ISAR imaging
CN115618187A (en) * 2022-12-21 2023-01-17 西南交通大学 Method, device and equipment for optimizing observation matrix and readable storage medium
CN115618187B (en) * 2022-12-21 2023-03-17 西南交通大学 Method, device and equipment for optimizing observation matrix and readable storage medium

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Application publication date: 20140723