CN113435299B - Bistatic forward-looking SAR clutter suppression method based on space-time matching - Google Patents

Bistatic forward-looking SAR clutter suppression method based on space-time matching Download PDF

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CN113435299B
CN113435299B CN202110697657.8A CN202110697657A CN113435299B CN 113435299 B CN113435299 B CN 113435299B CN 202110697657 A CN202110697657 A CN 202110697657A CN 113435299 B CN113435299 B CN 113435299B
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李中余
刘竹天
叶宏达
于飞
孙稚超
武俊杰
杨建宇
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University of Electronic Science and Technology of China
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Abstract

The invention discloses a space-time matching-based bistatic forward-looking SAR clutter suppression method, which comprises the following steps: s1, establishing a BFSAR space geometric model and initializing system parameters; s2, performing range pulse compression on the echo signal, and performing preprocessing and migration correction on the echo signal after the range pulse compression; s3, establishing a space-time clutter model according to the BFSAR space geometric model, and acquiring space-time frequency information of the clutter of the unit to be detected; s4, designing an optimal matching space-time filter of a unit to be detected to obtain a constraint optimization problem; s5, solving a constraint optimization problem by utilizing a particle swarm optimization algorithm to obtain an optimal solution; and S6, reconstructing the optimal matching space-time filter according to the optimal solution. The invention effectively avoids clutter covariance matrix estimation in the traditional STAP algorithm, eliminates the influence of bistatic foresight SAR clutter non-stationarity, can establish an optimally matched space-time filter under any configuration, and realizes the inhibition of bistatic foresight SAR strong non-stationarity clutter.

Description

Bistatic forward-looking SAR clutter suppression method based on space-time matching
Technical Field
The invention belongs to the technical field of radars, and particularly relates to a space-time matching-based bistatic forward-looking SAR clutter suppression method.
Background
By separately arranging a receiving station and a transmitting station on different independent platforms, the inherent limitation of the traditional single-base SAR can be broken through, and a two-dimensional high-resolution image in front of the flying platform can be obtained. With the continuous improvement of the requirements on the remote sensing system in modern application, the BFSAR ground moving target detection technology is increasingly urgent in the field of military and civilian. However, when BFSAR surveys over the ground, moving target echoes will typically be swamped in the stationary strong clutter background of the ground, and therefore clutter suppression is one of the key steps in moving target detection.
In the single-base side-looking SAR, clutter echoes are irrelevant in distance, namely clutter angles and Doppler traces have the same characteristic in different distance units, but under the condition of the double-base side-looking SAR, ground clutter has strong non-stationarity, the angle and Doppler traces of the clutter change along with the change of the distance units, and distance correlation exists, so that great difficulty is brought to effective inhibition of the double-base side-looking SAR clutter.
At present, the research and literature of Bistatic Forward SAR mainly focuses on Imaging algorithms of static scenes, see the literature "R.Wang, O.Loffeld, Y.neo, et al.Focus Bistatic SAR Data in Airborner/static Configuration [ J ]. IEEE Transactions on Geoscience and motion Sensing,2010,48(1):452 & 465" and "H.Sun and J.Lim.Omega-k Algorithm for Airborner Forward-tracking Bistatic Spotlight Imaging [ J ]. IEEE geostationary Sensing Letter,2009,6(2):312 & 316". For the aspect of bistatic forward-looking SAR moving target imaging, related research is disclosed in recent years. See the documents "Z.Li, J.Wu, Y.Huang, Z.Sun and J.Yang.group-Moving Target Imaging and vector Estimation Based on the mismatch Compression for Bistatic Forward-Looking SAR [ J ]. IEEE Transactions on Geoscience and motion Sensing,2016,54(6):3277 + 3291" and "Z.Li, J.Wu, Z.Liu, Y.Huang, H.Yang and J.Yang.An Optima 2-D spectra Matching Method for group Moving Target Imaging. IEEE Transactions on Geoscience and motion Sensing,2018,56(10): 5961: 5974". The two methods can realize refocusing and parameter estimation processing of the moving target, but the influence of BFSAR ground strong clutter is not considered in the processing process of the moving target signal. The above method will face severe performance loss when BFSAR clutter is present. In order to effectively realize BFSAR moving target indication, clutter in an echo needs to be suppressed first, and main clutter suppression methods comprise a phase center offset antenna (DPCA) method and a space-time adaptive processing (STAP) method. See documents "D.Cerutti-Maori and I.Sikaneta.A.A.Generation of Multichannel SAR/GMTI radars. IEEE Transactions on genetics and removal Sensing,2013,51(1): 560-. The DPCA method realizes clutter suppression by canceling multi-channel echoes, but the DPCA method requires that the speed, the channel spacing and the pulse repetition frequency of the SAR system meet strict conditions, so that equivalent phase centers of different channels can be superposed in different time domains; however, in the BFSAR, the transmit-receive separation will cause the above conditions to be difficult to satisfy, thereby affecting the processing effect of the BFSAR-DPCA. The STAP method is used as the extension of DPCA, and expands the one-dimensional signal processing to a space-time two-dimensional domain for processing; however, due to the range migration, doppler spectrum spread and non-stationary characteristics of the clutter echo of BFSAR, the clutter suppression performance of the method is seriously deteriorated.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a bistatic foresight SAR nonstationary clutter suppression method which adopts a processing strategy of directly designing and constructing a space-time filter matched with a clutter spectrum, avoids clutter covariance matrix estimation in the traditional STAP algorithm, effectively eliminates the influence of bistatic foresight SAR clutter nonstationary, and can establish an optimally matched space-time filter under any configuration.
The purpose of the invention is realized by the following technical scheme: the space-time matching-based bistatic forward-looking SAR clutter suppression method comprises the following steps:
s1, establishing a BFSAR space geometric model and initializing system parameters;
s2, performing range pulse compression on the echo signal, and performing preprocessing and migration correction on the echo signal after range pulse compression;
s3, establishing a space-time clutter model according to the BFSAR space geometric model, acquiring space-time frequency information of the clutter of the unit to be detected, and acquiring space-time distribution information of the clutter spectrum according to the coupling relation of the space-time frequency of the clutter;
s4, designing an optimal matching space-time filter of the unit to be detected according to clutter space-time distribution information to obtain a constraint optimization problem;
s5, solving the constrained optimization problem by utilizing a particle swarm optimization algorithm to obtain an optimal solution of the constrained optimization problem;
s6, solving the obtained optimal solution according to the particle swarm optimization algorithm, reconstructing the optimal matching space-time filter, and filtering the unit to be detected by using the reconstructed optimal matching space-time filter to obtain a signal after non-stationary clutter suppression.
Further, the specific implementation method of S2 is as follows: for a BFSAR system, setting a transmitting signal as a linear frequency modulation signal, preprocessing an echo signal by adopting a filtering mode, and performing migration correction on the echo signal through trapezoidal distortion correction;
filter H in preprocessingpre(t,fτ) Comprises the following steps:
Figure BDA0003128483990000021
wherein f isrefIs the Doppler centroid of the reference point, fτAnd fcRespectively distance frequency and carrier frequency;
the keystone correction function is expressed as:
t＝fct1/(fτ+fc)
wherein, t1Is the new azimuth time after the transformation;
after the above processing, the signal received by the nth channel is represented as:
Figure BDA0003128483990000031
wherein, P represents clutter scattering points or moving targets in an observation scene, and σ (P) is a backscattering coefficient of P, ωa() represents an azimuthal envelope; parameter τ, t, Bτλ, c and tPRespectively representing distance time, azimuth time, distance bandwidth, wavelength, light speed and beam center time; rs(0, n; P) is the bistatic distance of the point P relative to the nth channel at the azimuth moment of 0; rs(t1N; p) is the double base distance of point P relative to the nth receive channel;
the processing time is divided into a plurality of sub-time periods by adopting a time-sharing processing means, and in the time-sharing processing, each sub-time period satisfies the following relation:
Figure BDA0003128483990000032
wherein, Delta T is the length of the time-sharing processing sub-time interval, KaFor adjusting the frequency, Δ δ, by DoppleraFor Doppler resolution, TsynIs the synthetic aperture time;
after the migration correction and the time-sharing processing are carried out on the echo signal, the data in each sub-period is subjected to column vectorization to obtain space-time sample data in each sub-period, which is expressed as Svec(t)。
Further, the specific implementation method of S3 is as follows: the space-time frequency information of the clutter of the unit to be detected is as follows:
Figure BDA0003128483990000033
Figure BDA0003128483990000034
wherein f isdAnd fsNormalized Doppler frequency and normalized spatial frequency, f, of ground clutter scattering points, respectivelyrIs the pulse repetition frequency, VTAnd VRThe flight speeds of the transmitter and receiver, respectively,. psiTAnd psiRThe spatial cone angle, theta, of the ground clutter scattering point with respect to the transmitter and receiver, respectivelyRAnd
Figure BDA0003128483990000035
azimuth and elevation angles, theta, respectively, of ground clutter scattering points relative to the receiverpThe included angle between the receiver array and the flight direction is shown, and d and lambda respectively represent the channel spacing and the signal wavelength;
the space-time distribution of the clutter spectrum in the cells to be examined is expressed as:
Figure BDA0003128483990000041
θTand
Figure BDA0003128483990000042
azimuth and elevation angles, delta, respectively, of clutter scattering sites relative to the transmitterTAnd deltaRRespectively, the angles of the transmitter and receiver flight directions relative to the bistatic baseline.
Further, the specific implementation method of S4 is as follows: for the system with N channels and K pulses, the weight coefficient of the designed filter is Wd，WdIs an NK × 1 dimensional complex vector, expressed as:
Wd＝[Wd1,Wd2,...,Wdnk,...,WdNK]T
wherein, WdnkIs the weight coefficient component of the nk dimension;
the two-dimensional space-time frequency response of this filter is expressed as:
Figure BDA0003128483990000043
wherein the content of the first and second substances,
Figure BDA0003128483990000044
is a space-time steering vector; stE.g. Kx 1 and SsE N × 1 are respectively time and space steering vectors, which are respectively expressed as:
Figure BDA0003128483990000045
Figure BDA0003128483990000046
the response of the filter to a moving object is represented as
Figure BDA0003128483990000047
Wherein, VtE.g. K x 1 and VsThe epsilon is N multiplied by 1 and is respectively the time and space guide vector of the moving target;
uniformly dividing a receive beam into Q sub-beams, Q being the synthetic aperture length LsynAnd azimuth resolution ρaDetermining, expressed as Q ═ Lsyn/ρa(ii) a After beam division, the clutter signals in the receiver are obtained by superposing clutter echoes in the Q directions; therefore, in order to effectively suppress the non-stationary clutter of the bistatic forward-looking SAR, the space-time frequency response of the filter in the directions of the Q sub-beams is only required to be zero; according to the above thought, the following constraint optimization problem is established:
Figure BDA0003128483990000048
wherein epsilon is the error tolerance of the observation noise; { fu(Wd) | u ═ 1,2,3} is an objective function expressed as:
Figure BDA0003128483990000051
Figure BDA0003128483990000052
Figure BDA0003128483990000053
wherein f isdiAnd fsiNormalized Doppler frequency and normalized space frequency in the ith sub-beam direction; objective function f1(Wd) And f2(Wd) Respectively representing the notch mean and variance of the space-time filter arranged in the direction of the Q sub-beams; objective function f3(Wd) Will determine the width of the space-time filter notch, f3(Wd) The device consists of two parts: sumRL(Wd) And sumRR(Wd)，sumRL(Wd) And sumRR(Wd) The sum of the two-dimensional frequency responses, left and right of the filter notch, respectively, is expressed as:
Figure BDA0003128483990000054
Figure BDA0003128483990000055
wherein, Δ fdIs a constant doppler frequency.
Further, the specific implementation method of S5 is as follows:
s51, initializing the population quantity X and the iteration times G, and setting the boundary of a decision variable in a particle swarm optimization algorithm;
s52, weighting the filter weight coefficient WdiSplitting into a real part and an imaginary part:
Wdi＝xi+jxl,i＝1,2,…,NK
where, the index of sequence number l ═ i + NK, and the decision vector x ═ x1,x2,x3,…,x2NK]TIs a particle in the particle swarm optimization, namely an optimized solution, the optimized solution is solved by adopting the particle swarm optimization, and the obtained optimal solution is recorded as
Figure BDA0003128483990000056
Further, the specific implementation method of S6 is as follows: solving the optimal solution obtained according to the particle swarm optimization algorithm
Figure BDA0003128483990000057
Reconstructing optimal matching space-time filter weight coefficients
Figure BDA0003128483990000058
As follows below, the following description will be given,
Figure BDA0003128483990000059
for the unit to be detected, performing space-time filtering by using the reconstructed optimal matching space-time filter to obtain a target signal after the bistatic forward-looking SAR non-stationary clutter suppression
Figure BDA00031284839900000510
The invention has the beneficial effects that: the invention adopts a processing strategy of directly designing and constructing the space-time filter matched with the clutter spectrum, and effectively solves the problem of the suppression performance deterioration of the bistatic forward-looking SAR caused by clutter non-stationarity. The method comprises the steps of firstly obtaining the space-time characteristic of the clutter through BFSAR clutter modeling, and designing a clutter suppression filter according to obtained clutter information. And then converting the filter weight coefficient solving problem into an optimization problem with constraint. And finally, directly solving and constructing the optimal matching space-time filter of the unit to be detected by utilizing a particle swarm optimization algorithm, and further realizing the suppression processing of the non-stationary clutter of the bistatic foresight SAR through space-time filtering. The invention has the innovation points that clutter covariance matrix estimation in the traditional STAP algorithm is effectively avoided, the influence of clutter non-stationarity of the bistatic forward-looking SAR is effectively eliminated, an optimally matched space-time filter can be established under any configuration, and the suppression of strong non-stationarity clutter of the bistatic forward-looking SAR is realized.
Drawings
FIG. 1 is a flow chart of a bistatic forward-looking SAR non-stationary clutter suppression method of the present invention;
fig. 2 is a schematic diagram of a BFSAR spatial geometric model according to the present embodiment;
FIG. 3 is a diagram of echo signals of the present embodiment;
FIG. 4 is a diagram illustrating echo domain signals after non-stationary clutter suppression according to the embodiment;
FIG. 5 is the image domain signal after the non-stationary clutter suppression of the present embodiment;
FIG. 6 is a cross-sectional comparison along the X-axis before and after the treatment in the present example;
FIG. 7 is a cross-sectional comparison along the Y-axis before and after the treatment in this example.
Detailed Description
The invention mainly adopts a simulation experiment mode to carry out verification, and the simulation verification platform is MATLAB2020 a. The technical scheme of the invention is further explained by combining the drawings and the specific embodiment.
As shown in fig. 1, the method for suppressing double-base forward-looking SAR clutter based on space-time matching of the present invention includes the following steps:
s1, establishing a BFSAR space geometric model and initializing system parameters; the BFSAR geometry used in this example is shown in fig. 2, and the system parameters of the BFSAR are shown in table 1, where the location coordinates of the transmitter at time zero are (x)T,yT,zT) Transmitter along Y axis at VTFlying at a speed of (1); the position coordinate of the nth channel of the receiver at the zero moment is (x)R,yR+(n-1)d,zR) With receiver along Y axis at VRFlying at a speed of (1); the speed of light is c.
TABLE 1
Figure BDA0003128483990000061
Figure BDA0003128483990000071
S2, performing range pulse compression on the echo signal, and performing preprocessing and migration correction on the echo signal after the range pulse compression;
the specific implementation method comprises the following steps: for a BFSAR system, a transmitting signal is a linear frequency modulation signal, an echo signal is calculated, and distance pulse compression is carried out on the echo signal; in order to eliminate the influence of an echo over-distance unit on clutter suppression processing, the echo signal is preprocessed in a filtering mode, and Doppler centroid ambiguity is eliminated; and after the range-direction pulse compression, the echo signal contains coupling terms of range frequency and azimuth time, and the coupling is removed through trapezoidal distortion correction, so that the echo range migration correction is realized.
Filter H in preprocessingpre(t,fτ) Comprises the following steps:
Figure BDA0003128483990000072
wherein f isrefIs the Doppler centroid of the reference point, fτAnd fcRespectively distance frequency and carrier frequency;
the keystone correction function is expressed as:
t＝fct1/(fτ+fc)
wherein, t1Is the new azimuth time after the transformation;
after the above processing, the signal received by the nth channel is represented as:
Figure BDA0003128483990000073
wherein, P represents clutter scattering points or moving targets in an observation scene, and σ (P) is a backscattering coefficient of P, ωa() represents an azimuthal envelope; parameter τ, t, Bτλ, c and tPRespectively representing distance time, azimuth time, distance bandwidth, wavelength, light speed and beam center time; rs(0, n; P) is the double-base distance of the point P relative to the nth channel when the azimuth time is 0, namely the sum of the distances from the point P to the transmitting station and the receiving station (channel n) at the time 0; rs(t1N; p) is the double base distance of point P relative to the nth receive channel;
considering that the Doppler broadening is caused by long observation time in BFSAR, so that the traditional STAP method can not be directly applied, the invention adopts a time-sharing processing method to divide the processing time into a plurality of sub-time periods, thereby eliminating the influence of the Doppler broadening. In the time-sharing process, each sub-period satisfies the following relationship:
Figure BDA0003128483990000074
wherein, Delta T is the length of the time-sharing processing sub-time interval, KaFor adjusting the frequency, Δ δ, by DoppleraFor Doppler resolution, TsynIs the synthetic aperture time;
after the migration correction and the time-sharing processing are carried out on the echo signal, the data in each sub-period is subjected to column vectorization to obtain space-time sample data in each sub-period, which is expressed as Svec(t), the signal obtained after the processing of S2 in this embodiment is shown in fig. 3.
S3, in order to realize the design of the optimal matching space-time filter, firstly, a space-time clutter model is established according to a BFSAR space geometric model, the space-time frequency information of the clutter of the unit to be detected is obtained, and the space-time distribution information of the clutter spectrum is obtained according to the coupling relation of the space-time frequency of the clutter;
the specific implementation method comprises the following steps: the space-time frequency information of the clutter of the unit to be detected is as follows:
Figure BDA0003128483990000081
Figure BDA0003128483990000082
wherein, fdAnd fsNormalized Doppler frequency and normalized spatial frequency, f, of ground clutter scattering points, respectivelyrIs the pulse repetition frequency, VTAnd VRThe flight speeds of the transmitter and receiver, respectively,. psiTAnd psiRThe spatial cone angle, θ, of the ground clutter scattering point with respect to the transmitter and receiver, respectivelyRAnd
Figure BDA0003128483990000083
azimuth and elevation angles, theta, respectively, of ground clutter scattering points relative to the receiverpRepresenting the angle of the receiver array to the flight direction, and d and lambda represent the channel spacing and signal wavelength, respectively.
Space cone angle psiTAnd psiRIs obtained by the following formula,
Figure BDA0003128483990000084
Figure BDA0003128483990000085
wherein, thetaTAnd
Figure BDA0003128483990000086
azimuth and elevation angles, delta, respectively, of clutter scattering sites relative to the transmitterTAnd deltaRRespectively, the angles of the transmitter and receiver flight directions relative to the bistatic baseline. Thus, the space-time distribution of the clutter spectrum in the cell to be detected is expressed as:
Figure BDA0003128483990000087
visible, clutter space-time distribution and angle
Figure BDA0003128483990000088
Correlation will vary with the distance ring and clutter of the bistatic forward-looking SAR will have non-stationary characteristics. Furthermore, the spatial-temporal distribution information of the clutter will be determined by the spatial position of the scattering point relative to the dual platforms, which will be referred to as angle θ belowR、
Figure BDA0003128483990000089
θTAnd
Figure BDA00031284839900000810
and (6) solving.
For the cell to be detected, the instantaneous bistatic distance history is represented as:
Figure BDA00031284839900000811
the above formula is expanded, the distance between the two bases and rewritten as:
Figure BDA0003128483990000091
wherein the content of the first and second substances,
Figure BDA0003128483990000092
the general ellipse is expressed as:
ax2+bxy+cy2+dx+ey+1＝0
comparing the general expressions of the ellipses, obtaining the coefficients of the non-standard ellipses corresponding to the equal distance rings as follows:
Figure BDA0003128483990000093
Figure BDA0003128483990000094
Figure BDA0003128483990000095
Figure BDA0003128483990000096
Figure BDA0003128483990000097
further, the inclination angle theta of the major axis and the geometric center (x) of the non-standard ellipse can be obtainedc,yc) Major semi-axis LmaAnd a short semi-axis LmiComprises the following steps:
Figure BDA0003128483990000098
Figure BDA0003128483990000099
Figure BDA00031284839900000910
Figure BDA00031284839900000911
according to the obtained geometric parameters and the relation (rotation and translation) between the non-standard ellipse and the standard ellipse, the coordinate information of each point on the non-standard ellipse can be obtained as
Figure BDA00031284839900000912
Wherein the content of the first and second substances,
Figure BDA00031284839900000913
the point coordinates on the standard ellipse corresponding to the non-standard ellipse can be obtained according to the parameter equation of the standard ellipse.
Therefore, according to the point coordinates on the equidistant ring obtained by the solution, the space relation (theta) between each point and the carrier platform can be obtainedR,TIs thetaRAnd thetaTIs to be given a uniform representation of,
Figure BDA00031284839900000914
is composed of
Figure BDA00031284839900000915
And
Figure BDA00031284839900000916
of (1) unified representation
Figure BDA0003128483990000101
Figure BDA0003128483990000102
Wherein | | · | | represents a 2 norm, R0And T0Respectively, representing the projections of the receiver and transmitter on the ground. Coordinate xR,TIs xRAnd xTIs uniformly expressed, yR,TIs yRAnd yTIs uniformly expressed, zR,TIs zRAnd zTIs shown in unified form. Vector in the above formula
Figure BDA0003128483990000103
LRPTP and
Figure BDA0003128483990000104
is shown below (L)RP,TPIs LRPAnd LTPIs represented in a unified manner in the (c),
Figure BDA0003128483990000105
is composed of
Figure BDA0003128483990000106
And
Figure BDA0003128483990000107
of (1) unified representation
Figure BDA0003128483990000108
S4, designing the optimal matching space-time filter of the unit to be detected according to the clutter space-time distribution information to obtain a constraint optimization problem;
the specific implementation method comprises the following steps: for the system with N channels and K pulses, the weight coefficient of the designed filter is Wd，WdIs an NK × 1 dimensional complex vector, expressed as:
Wd＝[Wd1,Wd2,...,Wdnk,...,WdNK]T
wherein, WdnkIs the weight coefficient component of the nk dimension;
the two-dimensional space-time frequency response of this filter is expressed as:
Figure BDA0003128483990000109
wherein the content of the first and second substances,
Figure BDA00031284839900001013
is a space-time steering vector; stE.g. Kx 1 and SsE N × 1 are respectively time and space steering vectors, which are respectively expressed as:
Figure BDA00031284839900001010
Figure BDA00031284839900001011
the response of the filter to a moving object is represented as
Figure BDA00031284839900001012
Wherein, VtE.g. K x 1 and VsThe epsilon is N multiplied by 1 and is respectively the time and space guide vector of the moving target;
uniformly dividing a receive beam into Q sub-beams, Q being the synthetic aperture length LsynAnd azimuth resolution ρaDetermining, expressed as Q ═ Lsyn/ρa(ii) a After beam division, the clutter signals in the receiver are obtained by superposing clutter echoes in the Q directions; therefore, in order to effectively suppress the non-stationary clutter of the bistatic forward-looking SAR, the space-time frequency response of the filter in the directions of the Q sub-beams is only required to be zero; according to the above thought, the following constraint optimization problem is established:
Figure BDA0003128483990000111
wherein epsilon is the error tolerance of the observation noise; { fu(Wd) | u ═ 1,2,3} is an objective function expressed as:
Figure BDA0003128483990000112
Figure BDA0003128483990000113
Figure BDA0003128483990000114
wherein, fdiAnd fsiNormalized Doppler frequency and normalized space frequency in the ith sub-beam direction; objective function f1(Wd) And f2(Wd) Respectively representing the notch mean and variance of the space-time filter arranged in the direction of the Q sub-beams; when f is1(Wd) And f2(Wd) The smaller the function value of (a), the deeper and smoother notch will be generated by the filter at the corresponding location in the space-time domain, thereby ensuring that the two-dimensional frequency response of the filter matches the clutter spectral distribution and that the clutter is sufficiently suppressed after space-time filtering. Objective function f3(Wd) Will determine the width of the space-time filter notch, f3(Wd) The device consists of two parts: sumRL(Wd) And sumRR(Wd)，sumRL(Wd) And sumRR(Wd) The sum of the two-dimensional frequency responses, left and right of the filter notch, respectively, is expressed as:
Figure BDA0003128483990000115
Figure BDA0003128483990000116
wherein, Δ fdIs a constant doppler frequency. Minimizing the objective function f3(Wd) The frequency responses of the left side and the right side of the notch of the filter can have gain as large as possible, and a certain notch width is ensured, so that the result of notch broadening in the solving process is avoided, and the suppression performance of the bistatic forward-looking SAR nonstationary clutter is improved.
To this end, the solution problem for the space-time filter has been transformed into a constrained mathematical optimization problem, the sum objective function F (W)d) When the minimum value is obtained, the space-time filter with the double-base forward-looking SAR being optimally matched can be obtained.
S5, solving the constrained optimization problem by utilizing a particle swarm optimization algorithm to obtain an optimal solution of the constrained optimization problem;
the specific implementation method comprises the following steps:
s51, initializing the population quantity X and the iteration times G, and setting the boundary of a decision variable in a particle swarm optimization algorithm;
s52, weighting the filter weight coefficient WdiSplitting into a real part and an imaginary part:
Wdi＝xi+jxl,i＝1,2,…,NK
where, the index of sequence number l ═ i + NK, and the decision vector x ═ x1,x2,x3,…,x2NK]TIs a particle in the particle swarm optimization, namely an optimized solution, the optimized solution is solved by adopting the particle swarm optimization, and the obtained optimal solution is recorded as
Figure BDA0003128483990000121
The particle swarm optimization method comprises the following specific steps:
s521, initializing relevant parameters of the particle swarm algorithm, including a decision space VDParticle dimension D, particle number omega and iteration maximum number G;
s522, initializing particle group gamma1. Let the iteration number g equal to 1, in the decision space VDTo generate omega particle component particle group gamma1. Order to
Figure BDA0003128483990000122
Representing the ith particle in the g generation particle population, and totally containing D independent variables;
Figure BDA0003128483990000123
is the velocity of the particle in the decision space. During initialization, the position of the ith particle will be randomly generated with a uniform distribution between its argument maximum and minimum, with the initial velocity of all particles being zero.
Initializing to obtain a particle population gamma1Thereafter, an adaptation value F (x) for each particle in the population is calculatedi(g))。
S523, when the iteration times meet G E [1, G ], continuing to perform the step S524, otherwise, ending the iteration and entering the step S526.
And S524, recording the extreme value of each particle according to the calculated adaptive value, and taking the extreme value as the individual optimal solution pBest of the particle. And comparing the individual optimal solutions through information sharing among the particles to find the group optimal solution gBest of the current population.
And S525, updating the position and the speed of the particle according to the individual optimal solution pBest and the group optimal solution gBest. The particle velocity is updated as follows
vi(g+1)＝κvi(g)+Δvi(g)
Where κ is a non-negative inertia factor, Δ vi(g) Update the variable for the velocity of the ith particle in the population of the g-th generation particles, denoted as Δ νi(g)＝C1×rand[0,1]×(pBesti-xi(g))+C2×rand[0,1]×(gBest(g)-xi(g))，C1And C2The individual learning factor and the social learning factor of the particle are respectively. rand [0,1 ]]Is a random number uniformly distributed between 0 and 1.
The particle position is updated as follows:
xi(g+1)＝xi(g)+vi(g+1)
after the particle attribute is updated, the particle group gamma of the next generation is obtainedg+1. The adaptive value of each particle is recalculated, and g +1 is updated, followed by returning to step S523.
S526, obtaining the particle group gamma of the last generation after the iteration is finishedGEach particle in the population will cluster at the location of the global optimal solution, which is the solution to the constrained optimization problem
Figure BDA0003128483990000131
S6, solving the obtained optimal solution according to the particle swarm optimization algorithm, reconstructing the optimal matching space-time filter, and filtering the unit to be detected by using the reconstructed optimal matching space-time filter to obtain a signal after non-stationary clutter suppression.
The specific implementation method comprises the following steps: solving the optimal solution obtained according to the particle swarm optimization algorithm
Figure BDA0003128483990000132
Reconstructing optimal matching space-time filter weight coefficients
Figure BDA0003128483990000133
As follows below, the following description will be given,
Figure BDA0003128483990000134
for the unit to be detected, performing space-time filtering by using the reconstructed optimal matching space-time filter to obtain a target signal after the bistatic forward-looking SAR non-stationary clutter suppression
Figure BDA0003128483990000135
Finally, the target signal S can be alignedMSTFAnd (t) carrying out subsequent parameter estimation, homing focusing and other processing.
In the embodiment, the echo domain signal after the non-stationary clutter suppression is shown in fig. 4, the image domain signal is shown in fig. 5, and fig. 6 and fig. 7 show the comparison result before and after the clutter suppression, so that the bistatic forward-looking SAR clutter is sufficiently suppressed, only the moving target signal is retained in the echo domain and the image domain, and the high-reliability moving target detection can be realized.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.

Claims (4)

1. The space-time matching-based bistatic forward-looking SAR clutter suppression method is characterized by comprising the following steps:
s1, establishing a BFSAR space geometric model and initializing system parameters;
s2, performing range pulse compression on the echo signal, and performing preprocessing and migration correction on the echo signal after the range pulse compression;
s3, establishing a space-time clutter model according to the BFSAR space geometric model, acquiring space-time frequency information of the clutter of the unit to be detected, and acquiring space-time distribution information of the clutter spectrum according to the coupling relation of the space-time frequency of the clutter; the specific implementation method comprises the following steps: the space-time frequency information of the clutter of the unit to be detected is as follows:
Figure FDA0003569826890000011
Figure FDA0003569826890000012
wherein f isdAnd fsNormalized Doppler frequency and normalized spatial frequency, f, of ground clutter scattering points, respectivelyrIs the pulse repetition frequency, VTAnd VRThe flight speeds of the transmitter and receiver, respectively,. psiTAnd psiRThe spatial cone angle, theta, of the ground clutter scattering point with respect to the transmitter and receiver, respectivelyRAnd
Figure FDA0003569826890000013
the azimuth angle and the elevation angle theta of the ground clutter scattering point relative to the receiver, respectivelypThe included angle between the receiver array and the flight direction is shown, and d and lambda respectively represent the channel spacing and the signal wavelength;
the space-time distribution of the clutter spectrum in the cells to be examined is expressed as:
Figure FDA0003569826890000014
θTand
Figure FDA0003569826890000015
azimuth and elevation angles, delta, respectively, of clutter scattering sites relative to the transmitterTAnd deltaRRespectively representing the included angles of the flight directions of the transmitter and the receiver relative to the double-base baseline;
s4, designing an optimal matching space-time filter of the unit to be detected according to clutter space-time distribution information to obtain a constraint optimization problem; the specific implementation method comprises the following steps: for the system with N channels and K pulses, the weight coefficient of the designed filter is Wd，WdIs an NK × 1 dimensional complex vector, expressed as:
Wd＝[Wd1,Wd2,...,Wdnk,...,WdNK]T
wherein, WdnkIs the weight coefficient component of the nk dimension;
the two-dimensional space-time frequency response of this filter is expressed as:
Figure FDA0003569826890000016
wherein the content of the first and second substances,
Figure FDA0003569826890000017
is a space-time steering vector; stE.g. Kx 1 and SsE N × 1 are respectively time and space steering vectors, which are respectively expressed as:
Figure FDA0003569826890000021
Figure FDA0003569826890000022
the response of the filter to a moving object is represented as
Figure FDA0003569826890000023
Wherein, VtE.g. K x 1 and VsThe epsilon is N multiplied by 1 and is respectively the time and space guide vector of the moving target;
uniformly dividing a receive beam into Q sub-beams, Q being the synthetic aperture length LsynAnd azimuthal resolution ρaDetermining, expressed as Q ═ Lsyn/ρa(ii) a After beam division, the clutter signals in the receiver are obtained by superposing clutter echoes in the Q directions; therefore, in order to effectively suppress the non-stationary clutter of the bistatic forward-looking SAR, the space-time frequency response of the filter in the directions of the Q sub-beams is only required to be zero; according to the above thought, the following constraint optimization problem is established:
Figure FDA0003569826890000024
wherein epsilon is the error tolerance of the observation noise; { fu(Wd) | u ═ 1,2,3} is an objective function expressed as:
Figure FDA0003569826890000025
Figure FDA0003569826890000026
Figure FDA0003569826890000027
wherein f isdiAnd fsiNormalized Doppler frequency and normalized space frequency in the ith sub-beam direction; objective function f1(Wd) And f2(Wd) Respectively representing the notch mean and variance of the space-time filter arranged in the direction of the Q sub-beams; objective function f3(Wd) Will determine the width of the space-time filter notch, f3(Wd) The device consists of two parts: sumRL(Wd) And sumRR(Wd)，sumRL(Wd) And sumRR(Wd) The sum of the two-dimensional frequency responses, left and right of the filter notch, respectively, is expressed as:
Figure FDA0003569826890000028
Figure FDA0003569826890000029
wherein, Δ fdIs a constant doppler frequency;
s5, solving the constrained optimization problem by utilizing a particle swarm optimization algorithm to obtain an optimal solution of the constrained optimization problem;
s6, solving the obtained optimal solution according to the particle swarm optimization algorithm, reconstructing the optimal matching space-time filter, and filtering the unit to be detected by using the reconstructed optimal matching space-time filter to obtain a signal after non-stationary clutter suppression.
2. The space-time matching-based bistatic forward-looking SAR clutter suppression method according to claim 1, wherein the S2 is specifically realized by the following steps: preprocessing the echo signal by adopting a filtering mode, and performing migration correction on the echo signal through trapezoidal distortion correction;
filter H in preprocessingpre(t,fτ) Comprises the following steps:
Figure FDA0003569826890000031
wherein f isrefIs the Doppler centroid of the reference point, fτAnd fcRespectively distance frequency and carrier frequency;
the keystone correction function is expressed as:
t＝fct1/(fτ+fc)
wherein, t1Is the new bearing time after transformation;
after the above processing, the signal received by the nth channel is represented as:
Figure FDA0003569826890000032
wherein, P represents clutter scattering point or moving target in observation scene, σ (P) is backward scattering coefficient of P, ωa() represents an azimuthal envelope; parameter tau, t, Bτλ, c and tPRespectively representing distance time, azimuth time, distance bandwidth, wavelength, light speed and beam center time; rs(0, n; P) is the bistatic distance of the point P relative to the nth channel at the azimuth moment of 0; r iss(t1N; p) is the double base distance of point P relative to the nth receive channel;
the processing time is divided into a plurality of sub-time periods by adopting a time-sharing processing means, and in the time-sharing processing, each sub-time period satisfies the following relation:
Figure FDA0003569826890000033
wherein, Delta T is the length of the time-sharing processing sub-time interval, KaFor Doppler modulation of frequency, Δ δaFor Doppler resolution, TsynIs the synthetic aperture time;
after the migration correction and time-sharing processing are carried out on the echo signal, column vectorization is carried out on data in each sub-period, and space-time sample data in each sub-period is obtained and is represented as Svec(t)。
3. The space-time matching-based bistatic forward-looking SAR clutter suppression method according to claim 1, wherein the S5 is specifically realized by the following steps:
s51, initializing the population quantity X and the iteration times G, and setting the boundary of a decision variable in a particle swarm optimization algorithm;
s52, weighting the filter weight coefficient WdiSplitting into a real part and an imaginary part:
Wdi＝xi+jxl,i＝1,2,…,NK
therein, serial numberIndex i ═ i + NK, decision vector x ═ x1,x2,x3,…,x2NK]TIs a particle in the particle swarm optimization, namely an optimized solution, the optimized solution is solved by adopting the particle swarm optimization, and the obtained optimal solution is recorded as
Figure FDA0003569826890000041
4. The space-time matching-based bistatic forward-looking SAR clutter suppression method according to claim 3, wherein the S6 is specifically realized by the following steps: solving the optimal solution obtained according to the particle swarm optimization algorithm
Figure FDA0003569826890000042
Reconstructing optimal matching space-time filter weight coefficients
Figure FDA0003569826890000043
As follows below, the following description will be given,
Figure FDA0003569826890000044
for the unit to be detected, performing space-time filtering by using the reconstructed optimal matching space-time filter to obtain a target signal after the bistatic forward-looking SAR non-stationary clutter suppression
Figure FDA0003569826890000045
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