CN113435299A - 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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CN113435299A
CN113435299A CN202110697657.8A CN202110697657A CN113435299A CN 113435299 A CN113435299 A CN 113435299A CN 202110697657 A CN202110697657 A CN 202110697657A CN 113435299 A CN113435299 A CN 113435299A
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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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Abstract

本发明公开了一种基于空‑时匹配的双基前视SAR杂波抑制方法,包括如下步骤:S1、建立BFSAR空间几何模型,并进行系统参数初始化;S2、对回波信号进行距离脉冲压缩,并对距离脉冲压缩后的回波信号进行预处理与徙动校正;S3、根据BFSAR空间几何模型建立空‑时杂波模型,获取待检测单元杂波的空‑时频率信息;S4、对待检测单元的最优匹配空‑时滤波器进行设计,获得约束优化问题;S5、利用粒子群优化算法求解约束优化问题,得到最优解;S6、根据最优解重构最优匹配空‑时滤波器。本发明有效避免了传统STAP算法中杂波协方差矩阵估计,消除了双基前视SAR杂波非平稳性的影响,可在任意构型下建立最优匹配的空‑时滤波器,实现了双基前视SAR强非平稳杂波的抑制。

Figure 202110697657

The invention discloses a dual-base forward-looking SAR clutter suppression method based on space-time matching, comprising the following steps: S1, establishing a BFSAR spatial geometric model, and initializing system parameters; S2, performing range pulse compression on echo signals , and perform preprocessing and migration correction on the echo signal after range pulse compression; S3, establish a space-time clutter model according to the BFSAR spatial geometric model, and obtain the space-time frequency information of the clutter of the unit to be detected; S4, treat The optimal matching space-time filter of the detection unit is designed to obtain the constrained optimization problem; S5, the particle swarm optimization algorithm is used to solve the constrained optimization problem, and the optimal solution is obtained; S6, the optimal matching space-time filter is reconstructed according to the optimal solution . The invention effectively avoids the clutter covariance matrix estimation in the traditional STAP algorithm, eliminates the influence of the non-stationarity of the bi-base forward looking SAR clutter, and can establish an optimally matched space-time filter in any configuration. Suppression of strong non-stationary clutter in bistatic forward looking SAR.

Figure 202110697657

Description

基于空-时匹配的双基前视SAR杂波抑制方法A clutter suppression method for bistatic forward looking SAR based on space-time matching

技术领域technical field

本发明属于雷达技术领域,特别涉及一种基于空-时匹配的双基前视SAR杂波抑制方法。The invention belongs to the technical field of radar, in particular to a dual-base forward-looking SAR clutter suppression method based on space-time matching.

背景技术Background technique

双基前视合成孔径雷达(Bistatic forward-looking synthetic apertureradar,BFSAR)通过将收、发双站分置于不同的独立平台,可以突破传统单基SAR的固有限制,获取平台飞行正前方的二维高分辨图像。随着现代应用中对遥感系统的要求不断提高,BFSAR地面运动目标检测技术在军民领域中的需求日益迫切。然而,当BFSAR对地探测时,动目标回波通常将淹没于地面静止的强杂波背景中,因此,杂波抑制是运动目标检测的关键步骤之一。Bistatic forward-looking synthetic aperture radar (BFSAR) can break through the inherent limitations of traditional single-base SAR by placing the receiving and transmitting stations on different independent platforms, and obtain a two-dimensional view directly in front of the platform. High-resolution images. With the continuous improvement of the requirements for remote sensing systems in modern applications, the demand for BFSAR ground moving target detection technology in the military and civilian fields is increasingly urgent. However, when BFSAR detects the ground, the moving target echo will usually be submerged in the strong clutter background of stationary ground. Therefore, clutter suppression is one of the key steps in moving target detection.

在单基正侧视SAR中,其杂波回波是距离不相关的,即杂波角度-多普勒迹在不同距离单元具有相同的特性,但是在双基前视SAR情况下,地面杂波具有强非平稳性,杂波的角度-多普勒迹随距离单元变化而变化,存在距离相关性,从而为双基前视SAR杂波的有效抑制带来了极大的难度。In single-base front-looking SAR, the clutter echo is range-independent, that is, the clutter angle-Doppler trace has the same characteristics in different range units, but in the case of bi-base forward-looking SAR, the ground clutter The wave has strong non-stationarity, and the angle-Doppler trace of the clutter changes with the change of the distance unit, and there is a distance correlation, which brings great difficulty to the effective suppression of the clutter in the bistatic forward looking SAR.

目前,双基前视SAR的研究与文献主要集中于静止场景的成像算法,见文献“R.Wang,O.Loffeld,Y.Neo,et al.Focusing Bistatic SAR Data in Airborne/Stationary Configuration[J].IEEE Transactions on Geoscience and RemoteSensing,2010,48(1):452-465”和“H.Shin and J.Lim.Omega-k Algorithm for AirborneForward-Looking Bistatic Spotlight SAR Imaging[J].IEEE Geoscience and RemoteSensing Letter,2009,6(2):312-316”。对于双基前视SAR运动目标成像方面,近年来也有相关研究公开。见文献“Z.Li,J.Wu,Y.Huang,Z.Sun and J.Yang.Ground-Moving TargetImaging and Velocity Estimation Based on Mismatched Compression for BistaticForward-Looking SAR[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(6):3277-3291”和“Z.Li,J.Wu,Z.Liu,Y.Huang,H.Yang and J.Yang.An Optimal2-D Spectrum Matching Method for SAR Ground Moving Target Imaging.IEEETransactions on Geoscience and Remote Sensing,2018,56(10):5961-5974”。这两种方法可以实现运动目标的重聚焦与参数估计处理,但其在动目标信号的处理过程中均没有考虑BFSAR地面强杂波的影响。当BFSAR杂波存在时,上述方法将面临严重的性能损失。为了有效实现BFSAR运动目标指示,需首先对回波中的杂波进行抑制,主要的杂波抑制方法包括相位中心偏置天线(DPCA)方法和空时自适应处理(STAP)方法。见文献“D.Cerutti-Maori andI.Sikaneta.A Generalization of DPCA Processing for Multichannel SAR/GMTIRadars.IEEE Transactions on Geoscience and Remote Sensing,2013,51(1):560-572.”和“Ender,J.H G.Space-time processing for multichannel synthetic apertureradar[J].IEEE Electronics and Communication Engineering Journal,2002,11(1):29-38”。DPCA方法通过对消多通道回波来实现杂波抑制,但其要求SAR系统的速度、通道间距与脉冲重复频率满足严格的条件,从而可使不同通道的等效相位中心可在不同时域重合;然而,在BFSAR中,收发分置将导致上述条件难以满足,进而影响BFSAR-DPCA的处理效果。STAP方法作为DPCA的扩展,其将一维信号处理扩展到了空-时二维域进行处理;但是由于BFSAR的杂波回波具有距离徙动,多普勒频谱扩展以及非平稳特性,将导致该方法的杂波抑制性能严重恶化。At present, the research and literature of bistatic forward-looking SAR mainly focus on the imaging algorithm of stationary scene, see the literature "R. Wang, O. Loffeld, Y. Neo, et al. .IEEE Transactions on Geoscience and RemoteSensing, 2010, 48(1):452-465" and "H.Shin and J.Lim.Omega-k Algorithm for AirborneForward-Looking Bistatic Spotlight SAR Imaging[J].IEEE Geoscience and RemoteSensing Letter , 2009, 6(2):312-316”. For the imaging of moving targets with bistatic forward looking SAR, there have also been related research published in recent years. See document "Z.Li, J.Wu, Y.Huang, Z.Sun and J.Yang.Ground-Moving TargetImaging and Velocity Estimation Based on Mismatched Compression for BistaticForward-Looking SAR[J].IEEE Transactions on Geoscience and Remote Sensing , 2016, 54(6):3277-3291" and "Z.Li, J.Wu, Z.Liu, Y.Huang,H.Yang and J.Yang.An Optimal2-D Spectrum Matching Method for SAR Ground Moving Target Imaging. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10):5961-5974”. These two methods can realize the refocusing and parameter estimation of moving targets, but they do not consider the influence of BFSAR ground clutter in the processing of moving target signals. When BFSAR clutter exists, the above methods will face severe performance loss. In order to effectively realize the BFSAR moving target indication, it is necessary to suppress the clutter in the echo first. The main clutter suppression methods include the phase center offset antenna (DPCA) method and the space-time adaptive processing (STAP) method. See references "D. Cerutti-Maori and I. Sikaneta. A Generalization of DPCA Processing for Multichannel SAR/GMTIRadars. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(1):560-572." and "Ender, J.H G. Space-time processing for multichannel synthetic apertureradar [J]. IEEE Electronics and Communication Engineering Journal, 2002, 11(1): 29-38”. The DPCA method achieves clutter suppression by canceling multi-channel echoes, but it requires the speed, channel spacing and pulse repetition frequency of the SAR system to meet strict conditions, so that the equivalent phase centers of different channels can overlap in different time domains ; However, in BFSAR, the separation of transceiver will make it difficult to meet the above conditions, and then affect the processing effect of BFSAR-DPCA. As an extension of DPCA, the STAP method extends the one-dimensional signal processing to the 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, it will lead to this problem. The clutter suppression performance of the method is severely deteriorated.

发明内容SUMMARY OF THE INVENTION

本发明的目的在于克服现有技术的不足,提供一种采用对匹配于杂波谱的空-时滤波器进行直接设计与构建的处理策略,避免了传统STAP算法中杂波协方差矩阵估计,有效消除了双基前视SAR杂波非平稳性的影响,可在任意构型下建立最优匹配的空-时滤波器的双基前视SAR非平稳杂波抑制方法。The purpose of the present invention is to overcome the deficiencies of the prior art, and to provide a processing strategy for directly designing and constructing a space-time filter matched to the clutter spectrum, avoiding the clutter covariance matrix estimation in the traditional STAP algorithm, and effectively The influence of clutter non-stationarity of bistatic forward-looking SAR is eliminated, and an optimally matched space-time filter can be established in any configuration to suppress non-stationary clutter in bistatic forward-looking SAR.

本发明的目的是通过以下技术方案来实现的:基于空-时匹配的双基前视SAR杂波抑制方法,包括如下步骤:The object of the present invention is to be achieved through the following technical solutions: a dual-base forward-looking SAR clutter suppression method based on space-time matching, comprising the following steps:

S1、建立BFSAR空间几何模型,并进行系统参数初始化;S1. Establish a BFSAR spatial geometric model and initialize system parameters;

S2、对回波信号进行距离脉冲压缩,并对距离脉冲压缩后的回波信号进行预处理与徙动校正;S2, perform range pulse compression on the echo signal, and perform preprocessing and migration correction on the echo signal after range pulse compression;

S3、根据BFSAR空间几何模型建立空-时杂波模型,获取待检测单元杂波的空-时频率信息,根据杂波空-时频率的耦合关系,获得杂波谱的空-时分布信息;S3, establishing a space-time clutter model according to the BFSAR spatial geometric model, obtaining the space-time frequency information of the clutter of the unit to be detected, and obtaining the space-time distribution information of the clutter spectrum according to the coupling relationship of the space-time frequency of the clutter;

S4、根据杂波空-时分布信息,对待检测单元的最优匹配空-时滤波器进行设计,获得约束优化问题;S4. According to the clutter space-time distribution information, the optimal matching space-time filter of the unit to be detected is designed to obtain a constrained optimization problem;

S5、利用粒子群优化算法求解约束优化问题,得到约束优化问题的最优解;S5. Use the particle swarm optimization algorithm to solve the constrained optimization problem, and obtain the optimal solution of the constrained optimization problem;

S6、根据粒子群优化算法求解得到的最优解,重构最优匹配空-时滤波器,并利用重构的最优匹配空-时滤波器对待检测单元进行滤波,得到非平稳杂波抑制后的信号。S6. According to the optimal solution obtained by the particle swarm optimization algorithm, reconstruct the optimally matched space-time filter, and use the reconstructed optimally matched space-time filter to filter the unit to be detected to obtain non-stationary clutter suppression signal after.

进一步地,所述S2具体实现方法为:对于BFSAR系统,设发射信号为线性调频信号,采用滤波方式对回波信号进行预处理,并通过梯形失真校正对回波信号进行徙动校正;Further, the specific implementation method of S2 is: for the BFSAR system, set the transmitted signal to be a linear frequency modulation signal, use a filtering method to preprocess the echo signal, and perform migration correction on the echo signal through trapezoidal distortion correction;

预处理中的滤波器Hpre(t,fτ)为:The filter H pre (t,f τ ) in preprocessing is:

Figure BDA0003128483990000021
Figure BDA0003128483990000021

其中,fref为参考点的多普勒质心,fτ和fc分别为距离频率与载频;Among them, f ref is the Doppler centroid of the reference point, f τ and f c are the range frequency and the carrier frequency, respectively;

梯形失真校正函数表示为:The keystone correction function is expressed as:

t=fct1/(fτ+fc)t=f c t 1 /(f τ +f c )

其中,t1是变换后新的方位时间;Among them, t 1 is the new azimuth time after transformation;

经上述处理后,第n个通道接收到的信号表示为:After the above processing, the signal received by the nth channel is expressed as:

Figure BDA0003128483990000031
Figure BDA0003128483990000031

其中,P代表观测场景中的杂波散射点或运动目标,σ(P)为P的后向散射系数,ωa(·)表示方位向包络;参数τ,t,Bτ,λ,c和tP分别表示距离时间,方位时间,距离带宽,波长,光速和波束中心时刻;Rs(0,n;P)是方位时刻为0时点P相对于第n个通道的双基距离;Rs(t1,n;P)为点P相对于第n个接收通道的双基距离;Among them, P represents the clutter scattering point or moving target in the observation scene, σ(P) is the backscattering coefficient of P, ω a ( ) represents the azimuth envelope; parameters τ, t, B τ , λ, c and t P represent distance time, azimuth time, distance bandwidth, wavelength, speed of light and beam center time, respectively; R s (0,n; P) is the dual base distance of point P relative to the nth channel when the azimuth time is 0; R s (t 1 ,n; P) is the bi-base distance of point P relative to the nth receiving channel;

采用分时处理的手段,将处理时间划分为数个子时间段,分时处理中,每个子时间段满足以下关系:Using time-sharing processing, the processing time is divided into several sub-time segments. In time-sharing processing, each sub-time segment satisfies the following relationship:

Figure BDA0003128483990000032
Figure BDA0003128483990000032

其中,ΔT为分时处理子时间段长度,Ka为多普勒调频率,Δδa为多普勒分辨率,Tsyn为合成孔径时间;Among them, ΔT is the length of the time-sharing processing sub-segment, Ka is the Doppler modulation frequency, Δδ a is the Doppler resolution, and T syn is the synthetic aperture time;

对回波信号进行徙动校正与分时处理后,对每个子时段内的数据进行列向量化,得到每个子时段内的空时样本数据,表示为Svec(t)。After performing migration correction and time-division processing on the echo signal, column vectorization is performed on the data in each sub-period to obtain the space-time sample data in each sub-period, which is denoted as S vec (t).

进一步地,所述S3具体实现方法为:待检测单元杂波的空-时频率信息如下:Further, the specific implementation method of S3 is: the space-time frequency information of the clutter of the unit to be detected is as follows:

Figure BDA0003128483990000033
Figure BDA0003128483990000033

Figure BDA0003128483990000034
Figure BDA0003128483990000034

其中,fd和fs分别是地面杂波散射点的归一化多普勒频率和归一化空间频率,fr是脉冲重复频率,VT和VR分别是发射机和接收机的飞行速度,ψT和ψR分别是地面杂波散射点相对于发射机和接收机的空间锥角,θR

Figure BDA0003128483990000035
分别是地面杂波散射点相对于接收机的方位角和俯仰角,θp表示接收机阵列与飞行方向的夹角,d和λ分别表示通道间距和信号波长;where f d and f s are the normalized Doppler frequency and normalized spatial frequency of the ground clutter scattering point, respectively, f r is the pulse repetition frequency, and V T and VR are the flight of the transmitter and receiver, respectively Velocities, ψ T and ψ R are the spatial cone angles of the ground clutter scattering point relative to the transmitter and receiver, respectively, θ R and
Figure BDA0003128483990000035
are the azimuth angle and pitch angle of the ground clutter scattering point relative to the receiver, θ p represents the angle between the receiver array and the flight direction, and d and λ represent the channel spacing and signal wavelength, respectively;

待检测单元内杂波谱的空-时分布表示为:The space-time distribution of the clutter spectrum in the unit to be detected is expressed as:

Figure BDA0003128483990000041
Figure BDA0003128483990000041

θT

Figure BDA0003128483990000042
分别是杂波散射点相对于发射机的方位角和俯仰角,δT和δR分别表示发射机和接收机飞行方向相对于双基基线的夹角。θ T and
Figure BDA0003128483990000042
are the azimuth and pitch angles of the clutter scattering point relative to the transmitter, respectively, and δT and δR represent the included angles of the transmitter and receiver flight directions relative to the dual-base baseline, respectively.

进一步地,所述S4具体实现方法为:对于通道数为N且脉冲数为K的系统,令设计的滤波器权系数为Wd,Wd是NK×1维的复数向量,表示为:Further, the specific implementation method of S4 is: for a system with N number of channels and K number of pulses, let the designed filter weight coefficient be W d , and W d is a NK×1-dimensional complex vector, expressed as:

Wd=[Wd1,Wd2,...,Wdnk,...,WdNK]T W d =[W d1 ,W d2 ,...,W dnk ,...,W dNK ] T

其中,Wdnk为第nk维的权系数分量;Among them, W dnk is the weight coefficient component of the nkth dimension;

该滤波器的二维空-时频率响应表示为:The two-dimensional space-time frequency response of this filter is expressed as:

Figure BDA0003128483990000043
Figure BDA0003128483990000043

其中,

Figure BDA0003128483990000044
为空-时导向矢量;St∈K×1和Ss∈N×1分别为时间与空间导向矢量,分别表示为:in,
Figure BDA0003128483990000044
is the space-time steering vector; S t ∈ K×1 and S s ∈ N×1 are the time and space steering vectors, respectively, expressed as:

Figure BDA0003128483990000045
Figure BDA0003128483990000045

Figure BDA0003128483990000046
Figure BDA0003128483990000046

该滤波器对运动目标的响应表示为

Figure BDA0003128483990000047
其中,Vt∈K×1和Vs∈N×1分别为运动目标的时间与空间导向矢量;The response of this filter to a moving target is expressed as
Figure BDA0003128483990000047
Among them, V t ∈ K×1 and V s ∈ N×1 are the temporal and spatial steering vectors of the moving target, respectively;

将接收波束进行均匀划分为Q个子波束,Q由合成孔径长度Lsyn与方位分辨率ρa决定,表示为Q=Lsyna;经波束划分后,认为接收机中的杂波信号是由这Q个方向的杂波回波叠加得到的;因此,为实现双基前视SAR非平稳杂波的有效抑制,只需滤波器在Q个子波束方向的空-时频率响应为零;根据上述思路,建立如下的约束优化问题:The receiving beam is evenly divided into Q sub-beams, and Q is determined by the synthetic aperture length L syn and the azimuth resolution ρ a , and is expressed as Q=L syna ; after the beam division, it is considered that the clutter signal in the receiver is It is obtained by the superposition of the clutter echoes in the Q directions; therefore, in order to achieve effective suppression of the non-stationary clutter in the bistatic forward looking SAR, it is only necessary that the space-time frequency response of the filter in the direction of the Q sub-beams is zero; according to Based on the above ideas, the following constrained optimization problem is established:

Figure BDA0003128483990000048
Figure BDA0003128483990000048

其中,ε为观测噪声误差容限;{fu(Wd)∣u=1,2,3}为目标函数,表示为:Among them, ε is the error tolerance of observation noise; {f u (W d )∣u=1,2,3} is the objective function, which is expressed as:

Figure BDA0003128483990000051
Figure BDA0003128483990000051

Figure BDA0003128483990000052
Figure BDA0003128483990000052

Figure BDA0003128483990000053
Figure BDA0003128483990000053

其中,fdi和fsi为第i个子波束方向上的归一化多普勒频率与归一化空间频率;目标函数f1(Wd)和f2(Wd)分别代表空-时滤波器在Q个子波束方向上设置的凹口平均值和方差;目标函数f3(Wd)将决定空-时滤波器凹口的宽度,f3(Wd)由两部分组成:sumRL(Wd)和sumRR(Wd),sumRL(Wd)和sumRR(Wd)分别为滤波器凹口左侧与右侧的二维频率响应之和,表示为:Among them, f di and f si are the normalized Doppler frequency and normalized spatial frequency in the ith sub-beam direction; the objective functions f 1 (W d ) and f 2 (W d ) represent space-time filtering, respectively is the average and variance of the notches set by the filter in the Q sub-beam directions; the objective function f 3 (W d ) will determine the width of the space-time filter notch, and f 3 (W d ) consists of two parts: sumR L ( W d ) and sumR R (W d ), sumR L (W d ) and sumR R (W d ) are the sums of the two-dimensional frequency responses on the left and right sides of the filter notch, respectively, expressed as:

Figure BDA0003128483990000054
Figure BDA0003128483990000054

Figure BDA0003128483990000055
Figure BDA0003128483990000055

其中,Δfd为一个常数多普勒频率。where Δf d is a constant Doppler frequency.

进一步地,所述S5具体实现方法为:Further, the specific implementation method of S5 is:

S51、初始化种群数量X和迭代次数G,并设置粒子群优化算法中决策变量的边界;S51, initialize the population number X and the number of iterations G, and set the boundary of the decision variable in the particle swarm optimization algorithm;

S52、将滤波器权系数Wdi拆分为实数部分和虚数部分:S52. Split the filter weight coefficient W di into a real part and an imaginary part:

Wdi=xi+jxl,i=1,2,…,NKW di =x i +jx l , i=1,2,...,NK

其中,序号索引l=i+NK,决策向量x=[x1,x2,x3,…,x2NK]T为粒子群算法中的一个粒子,即一个优化解,该优化解采用粒子群算法进行求解,得到的最优解记为

Figure BDA0003128483990000056
Among them, the serial number index l=i+NK, the decision vector x=[x 1 ,x 2 ,x 3 ,...,x 2NK ] T is a particle in the particle swarm algorithm, that is, an optimal solution, and the optimal solution adopts the particle swarm The algorithm is solved, and the optimal solution obtained is recorded as
Figure BDA0003128483990000056

进一步地,所述S6具体实现方法为:根据粒子群优化算法求解得到的最优解

Figure BDA0003128483990000057
重构最优匹配空-时滤波器权系数
Figure BDA0003128483990000058
如下,Further, the specific implementation method of S6 is: according to the optimal solution obtained by the particle swarm optimization algorithm
Figure BDA0003128483990000057
Reconstructing the optimal matching space-time filter weights
Figure BDA0003128483990000058
as follows,

Figure BDA0003128483990000059
Figure BDA0003128483990000059

对于待检测单元,利用重构的最优匹配空-时滤波器进行空-时滤波,得到双基前视SAR非平稳杂波抑制后的目标信号

Figure BDA00031284839900000510
For the unit to be detected, use the reconstructed optimal matching space-time filter to perform space-time filtering, and obtain the target signal after the non-stationary clutter suppression of the dual-base forward looking SAR.
Figure BDA00031284839900000510

本发明的有益效果是:本发明采用了对匹配于杂波谱的空-时滤波器进行直接设计与构建的处理策略,有效解决了双基前视SAR因杂波非平稳性导致的抑制性能恶化问题。本发明首先通过BFSAR杂波建模获取杂波的空-时特性,并根据获得的杂波信息对杂波抑制滤波器进行设计。再将滤波器权系数求解问题转化为一个带约束的优化问题。最后利用粒子群优化算法,对待检测单元的最优匹配空-时滤波器进行直接求解与构建,进而通过空-时滤波实现了双基前视SAR非平稳杂波的抑制处理。本发明的创新点是有效避免了传统STAP算法中杂波协方差矩阵估计,有效消除了双基前视SAR杂波非平稳性的影响,可在任意构型下建立最优匹配的空-时滤波器,实现了双基前视SAR强非平稳杂波的抑制。The beneficial effects of the present invention are as follows: the present invention adopts the processing strategy of directly designing and constructing the space-time filter matched to the clutter spectrum, effectively solving the deterioration of the suppression performance caused by the non-stationary clutter of the bistatic forward looking SAR. question. The invention first obtains the space-time characteristics of clutter through BFSAR clutter modeling, and designs a clutter suppression filter according to the obtained clutter information. Then, the problem of solving the filter weight coefficients is transformed into a constrained optimization problem. Finally, using the particle swarm optimization algorithm, the optimal matching space-time filter of the unit to be detected is directly solved and constructed, and the non-stationary clutter suppression of the dual-base forward looking SAR is realized by the space-time filter. The innovation of the invention is that the estimation of the clutter covariance matrix in the traditional STAP algorithm is effectively avoided, the influence of the clutter non-stationarity of the bistatic forward looking SAR is effectively eliminated, and the optimal matching space-time can be established under any configuration. The filter realizes the suppression of strong non-stationary clutter in bistatic forward looking SAR.

附图说明Description of drawings

图1为本发明的双基前视SAR非平稳杂波抑制方法的流程图;Fig. 1 is the flow chart of the bistatic forward looking SAR non-stationary clutter suppression method of the present invention;

图2为本实施例的BFSAR空间几何模型示意图;Fig. 2 is the schematic diagram of the BFSAR space geometric model of the present embodiment;

图3为本实施例的回波信号图;FIG. 3 is an echo signal diagram of the present embodiment;

图4为本实施例非平稳杂波抑制后的回波域信号;FIG. 4 is an echo domain signal after non-stationary clutter suppression in this embodiment;

图5是本实施例非平稳杂波抑制后的图像域信号;Fig. 5 is the image domain signal after non-stationary clutter suppression in this embodiment;

图6是本实施例处理前后沿X轴的剖面对比;Fig. 6 is the cross-sectional comparison along the X-axis before and after the treatment of the present embodiment;

图7是本实施例处理前后沿Y轴的剖面对比。FIG. 7 is a cross-sectional comparison along the Y-axis before and after treatment in this embodiment.

具体实施方式Detailed ways

本发明主要采用仿真实验的方式进行验证,仿真验证平台为MATLAB2020a。下面结合附图和具体实施例进一步说明本发明的技术方案。The present invention mainly adopts the method of simulation experiment for verification, and the simulation verification platform is MATLAB2020a. The technical solutions of the present invention are further described below with reference to the accompanying drawings and specific embodiments.

如图1所示,本发明的一种基于空-时匹配的双基前视SAR杂波抑制方法,包括如下步骤:As shown in FIG. 1 , a method for suppressing clutter based on space-time matching for dual-base forward looking SAR of the present invention includes the following steps:

S1、建立BFSAR空间几何模型,并进行系统参数初始化;本实例所采用的BFSAR几何构型如图2所示,BFSAR的系统参数如表1所示,其中,零时刻发射机的位置坐标为(xT,yT,zT),发射机沿着Y轴以VT的速度飞行;接收机第n个通道在零时刻的位置坐标为(xR,yR+(n-1)d,zR),接收机沿着Y轴以VR的速度飞行;光速为c。S1, establish a BFSAR spatial geometric model, and initialize the system parameters; the BFSAR geometric configuration used in this example is shown in Figure 2, and the system parameters of BFSAR are shown in Table 1, wherein, the position coordinates of the transmitter at zero time are ( x T , y T , z T ), the transmitter flies along the Y axis at the speed of V T ; the position coordinate of the nth channel of the receiver at time zero is (x R , y R +(n-1)d, z R ), the receiver flies along the Y axis at the speed of VR ; the speed of light is c.

表1Table 1

Figure BDA0003128483990000061
Figure BDA0003128483990000061

Figure BDA0003128483990000071
Figure BDA0003128483990000071

S2、对回波信号进行距离脉冲压缩,并对距离脉冲压缩后的回波信号进行预处理与徙动校正;S2, perform range pulse compression on the echo signal, and perform preprocessing and migration correction on the echo signal after range pulse compression;

具体实现方法为:对于BFSAR系统,发射信号为线性调频信号,计算回波信号并对回波信号进行距离脉冲压缩;为消除回波跨距离单元对杂波抑制处理的影响,本发明采用滤波方式对回波信号进行预处理,消除多普勒质心模糊;回波信号在距离向脉冲压缩后会包含距离频率和方位时间的耦合项,通过梯形失真校正去掉耦合,实现回波距离徙动校正。The specific implementation method is as follows: for the BFSAR system, the transmitted signal is a linear frequency modulation signal, the echo signal is calculated and the echo signal is compressed by distance pulse; in order to eliminate the influence of the echo cross-range unit on the clutter suppression processing, the present invention adopts a filtering method The echo signal is preprocessed to eliminate Doppler centroid ambiguity; the echo signal will contain the coupling terms of range frequency and azimuth time after range-to-pulse compression, and the coupling is removed by keystone correction to realize echo range migration correction.

预处理中的滤波器Hpre(t,fτ)为:The filter H pre (t,f τ ) in preprocessing is:

Figure BDA0003128483990000072
Figure BDA0003128483990000072

其中,fref为参考点的多普勒质心,fτ和fc分别为距离频率与载频;Among them, f ref is the Doppler centroid of the reference point, f τ and f c are the range frequency and the carrier frequency, respectively;

梯形失真校正函数表示为:The keystone correction function is expressed as:

t=fct1/(fτ+fc)t=f c t 1 /(f τ +f c )

其中,t1是变换后新的方位时间;Among them, t 1 is the new azimuth time after transformation;

经上述处理后,第n个通道接收到的信号表示为:After the above processing, the signal received by the nth channel is expressed as:

Figure BDA0003128483990000073
Figure BDA0003128483990000073

其中,P代表观测场景中的杂波散射点或运动目标,σ(P)为P的后向散射系数,ωa(·)表示方位向包络;参数τ,t,Bτ,λ,c和tP分别表示距离时间,方位时间,距离带宽,波长,光速和波束中心时刻;Rs(0,n;P)是方位时刻为0时点P相对于第n个通道的双基距离,即0时刻P到发射站、接收站(通道n)的距离和;Rs(t1,n;P)为点P相对于第n个接收通道的双基距离;Among them, P represents the clutter scattering point or moving target in the observation scene, σ(P) is the backscattering coefficient of P, ω a ( ) represents the azimuth envelope; parameters τ, t, B τ , λ, c and t P represent the distance time, azimuth time, distance bandwidth, wavelength, light speed and beam center time respectively; R s (0,n; P) is the dual base distance of point P relative to the nth channel when the azimuth time is 0, That is, the sum of the distances from P to the transmitting station and the receiving station (channel n) at time 0; R s (t 1 ,n; P) is the dual-base distance of point P relative to the nth receiving channel;

考虑到BFSAR中长观测时间导致多普勒展宽从而无法直接应用传统的STAP方法,本发明采用分时处理的手段,将处理时间划分为数个子时间段,从而消除多普勒展宽的影响。分时处理中,每个子时间段满足以下关系:Considering that the long observation time in BFSAR results in Doppler broadening, the traditional STAP method cannot be directly applied, the present invention adopts the means of time-division processing to divide the processing time into several sub-time segments, thereby eliminating the influence of Doppler broadening. In time-sharing processing, each sub-time segment satisfies the following relationship:

Figure BDA0003128483990000074
Figure BDA0003128483990000074

其中,ΔT为分时处理子时间段长度,Ka为多普勒调频率,Δδa为多普勒分辨率,Tsyn为合成孔径时间;Among them, ΔT is the length of the time-sharing processing sub-segment, Ka is the Doppler modulation frequency, Δδ a is the Doppler resolution, and T syn is the synthetic aperture time;

对回波信号进行徙动校正与分时处理后,对每个子时段内的数据进行列向量化,得到每个子时段内的空时样本数据,表示为Svec(t),本实施例经过S2处理后得到的信号如图3所示。After performing migration correction and time-sharing processing on the echo signal, column vectorization is performed on the data in each sub-period to obtain the space-time sample data in each sub-period, which is expressed as S vec (t). In this embodiment, after S2 The resulting signal after processing is shown in Figure 3.

S3、为实现最优匹配空-时滤波器的设计,本发明首先根据BFSAR空间几何模型建立空-时杂波模型,获取待检测单元杂波的空-时频率信息,根据杂波空-时频率的耦合关系,获得杂波谱的空-时分布信息;S3. In order to realize the design of the optimal matching space-time filter, the present invention first establishes a space-time clutter model according to the BFSAR space geometric model, obtains the space-time frequency information of the clutter of the unit to be detected, and obtains the space-time frequency information of the clutter of the unit to be detected. The coupling relationship of frequency, obtain the space-time distribution information of clutter spectrum;

具体实现方法为:待检测单元杂波的空-时频率信息如下:The specific implementation method is as follows: the space-time frequency information of the clutter of the unit to be detected is as follows:

Figure BDA0003128483990000081
Figure BDA0003128483990000081

Figure BDA0003128483990000082
Figure BDA0003128483990000082

其中,fd和fs分别是地面杂波散射点的归一化多普勒频率和归一化空间频率,fr是脉冲重复频率,VT和VR分别是发射机和接收机的飞行速度,ψT和ψR分别是地面杂波散射点相对于发射机和接收机的空间锥角,θR

Figure BDA0003128483990000083
分别是地面杂波散射点相对于接收机的方位角和俯仰角,θp表示接收机阵列与飞行方向的夹角,d和λ分别表示通道间距和信号波长。where f d and f s are the normalized Doppler frequency and normalized spatial frequency of the ground clutter scattering point, respectively, f r is the pulse repetition frequency, and V T and VR are the flight of the transmitter and receiver, respectively Velocities, ψ T and ψ R are the spatial cone angles of the ground clutter scattering point relative to the transmitter and receiver, respectively, θ R and
Figure BDA0003128483990000083
are the azimuth and pitch angles of the ground clutter scattering point relative to the receiver, respectively, θ p represents the included angle between the receiver array and the flight direction, and d and λ represent the channel spacing and signal wavelength, respectively.

空间锥角ψT和ψR由下式获得,The spatial cone angles ψT and ψR are obtained by,

Figure BDA0003128483990000084
Figure BDA0003128483990000084

Figure BDA0003128483990000085
Figure BDA0003128483990000085

其中,θT

Figure BDA0003128483990000086
分别是杂波散射点相对于发射机的方位角和俯仰角,δT和δR分别表示发射机和接收机飞行方向相对于双基基线的夹角。因此,待检测单元内杂波谱的空-时分布表示为:where θT and
Figure BDA0003128483990000086
are the azimuth and pitch angles of the clutter scattering point relative to the transmitter, respectively, and δT and δR represent the included angles of the transmitter and receiver flight directions relative to the dual-base baseline, respectively. Therefore, the space-time distribution of the clutter spectrum in the unit to be detected is expressed as:

Figure BDA0003128483990000087
Figure BDA0003128483990000087

可见,杂波的空-时分布与角度

Figure BDA0003128483990000088
相关,将随距离环的变化而变化,双基前视SAR的杂波将具有非平稳特性。此外,杂波的空-时分布信息将由散射点相对于双平台的空间位置决定,以下将对角度θR
Figure BDA0003128483990000089
θT
Figure BDA00031284839900000810
进行求解。It can be seen that the space-time distribution and angle of clutter
Figure BDA0003128483990000088
The correlation will vary with the change of the range loop, and the clutter of the bistatic forward looking SAR will have non-stationary characteristics. In addition, the space-time distribution information of the clutter will be determined by the spatial position of the scattering point relative to the double platform, and the following will discuss the angle θ R ,
Figure BDA0003128483990000089
θ T and
Figure BDA00031284839900000810
to solve.

对于待检测单元,瞬时双基距离历史表示为:For the unit to be detected, the instantaneous bistatic distance history is expressed as:

Figure BDA00031284839900000811
Figure BDA00031284839900000811

将其上式进行展开,双基距离和改写为:Expand the above formula, and rewrite the double base distance sum as:

Figure BDA0003128483990000091
Figure BDA0003128483990000091

其中,

Figure BDA0003128483990000092
in,
Figure BDA0003128483990000092

一般椭圆的表达式为:The general ellipse expression is:

ax2+bxy+cy2+dx+ey+1=0ax 2 +bxy+cy 2 +dx+ey+1=0

对比椭圆一般表达式,得到该等距离环所对应非标准椭圆的各项系数为:Comparing the general expression of the ellipse, the coefficients of the non-standard ellipse corresponding to the equidistant ring are obtained as:

Figure BDA0003128483990000093
Figure BDA0003128483990000093

Figure BDA0003128483990000094
Figure BDA0003128483990000094

Figure BDA0003128483990000095
Figure BDA0003128483990000095

Figure BDA0003128483990000096
Figure BDA0003128483990000096

Figure BDA0003128483990000097
Figure BDA0003128483990000097

进一步,可得到该非标准椭圆的长轴倾角θ、几何中心(xc,yc)、长半轴Lma与短半轴Lmi为:Further, the inclination angle θ of the major axis, the geometric center (x c , y c ), the major semi-axis L ma and the minor semi-axis L mi of the non-standard ellipse can be obtained as:

Figure BDA0003128483990000098
Figure BDA0003128483990000098

Figure BDA0003128483990000099
Figure BDA0003128483990000099

Figure BDA00031284839900000910
Figure BDA00031284839900000910

Figure BDA00031284839900000911
Figure BDA00031284839900000911

根据上述获得的几何参数,根据非标准椭圆与标准椭圆之间的关系(旋转与平移),可求得非标准椭圆上各点的坐标信息为According to the geometric parameters obtained above, according to the relationship between the non-standard ellipse and the standard ellipse (rotation and translation), the coordinate information of each point on the non-standard ellipse can be obtained as:

Figure BDA00031284839900000912
Figure BDA00031284839900000912

其中,

Figure BDA00031284839900000913
为非标准椭圆对应的标准椭圆上的点坐标,可根据标准椭圆的参数方程获取。in,
Figure BDA00031284839900000913
is the point coordinates on the standard ellipse corresponding to the non-standard ellipse, which can be obtained according to the parametric equation of the standard ellipse.

因此,根据求解得到的等距离环上的点坐标,可获取各点与载机平台间的空间关系为(θR,T为θR和θT的统一表示,

Figure BDA00031284839900000914
Figure BDA00031284839900000915
Figure BDA00031284839900000916
的统一表示)Therefore, according to the obtained point coordinates on the equidistant ring, the spatial relationship between each point and the carrier platform can be obtained as (θ R, T is the unified representation of θ R and θ T ,
Figure BDA00031284839900000914
for
Figure BDA00031284839900000915
and
Figure BDA00031284839900000916
unified representation)

Figure BDA0003128483990000101
Figure BDA0003128483990000101

Figure BDA0003128483990000102
Figure BDA0003128483990000102

其中,||·||表示2范数,R0和T0分别表示接收机和发射机在地面上的投影。坐标xR,T为xR和xT的统一表示,yR,T为yR和yT的统一表示,zR,T为zR和zT的统一表示。上式中向量

Figure BDA0003128483990000103
LRP,TP和
Figure BDA0003128483990000104
表示如下(LRP,TP为LRP和LTP的统一表示,
Figure BDA0003128483990000105
Figure BDA0003128483990000106
Figure BDA0003128483990000107
的统一表示)where ||·|| represents the 2-norm, and R 0 and T 0 represent the projections of the receiver and transmitter on the ground, respectively. The coordinates x R, T are the unified representation of x R and x T , y R, T is the unified representation of y R and y T , and z R, T is the unified representation of z R and z T. The vector in the above formula
Figure BDA0003128483990000103
L RP , TP and
Figure BDA0003128483990000104
Represented as follows (L RP, TP is the unified representation of L RP and L TP ,
Figure BDA0003128483990000105
for
Figure BDA0003128483990000106
and
Figure BDA0003128483990000107
unified representation)

Figure BDA0003128483990000108
Figure BDA0003128483990000108

S4、根据杂波空-时分布信息,对待检测单元的最优匹配空-时滤波器进行设计,获得约束优化问题;S4. According to the clutter space-time distribution information, the optimal matching space-time filter of the unit to be detected is designed to obtain a constrained optimization problem;

具体实现方法为:对于通道数为N且脉冲数为K的系统,令设计的滤波器权系数为Wd,Wd是NK×1维的复数向量,表示为:The specific implementation method is: for a system with N channels and K pulses, let the designed filter weight coefficient be W d , and W d is a complex vector of NK×1 dimension, which is expressed as:

Wd=[Wd1,Wd2,...,Wdnk,...,WdNK]T W d =[W d1 ,W d2 ,...,W dnk ,...,W dNK ] T

其中,Wdnk为第nk维的权系数分量;Among them, W dnk is the weight coefficient component of the nkth dimension;

该滤波器的二维空-时频率响应表示为:The two-dimensional space-time frequency response of this filter is expressed as:

Figure BDA0003128483990000109
Figure BDA0003128483990000109

其中,

Figure BDA00031284839900001013
为空-时导向矢量;St∈K×1和Ss∈N×1分别为时间与空间导向矢量,分别表示为:in,
Figure BDA00031284839900001013
is the space-time steering vector; S t ∈ K×1 and S s ∈ N×1 are the time and space steering vectors, respectively, expressed as:

Figure BDA00031284839900001010
Figure BDA00031284839900001010

Figure BDA00031284839900001011
Figure BDA00031284839900001011

该滤波器对运动目标的响应表示为

Figure BDA00031284839900001012
其中,Vt∈K×1和Vs∈N×1分别为运动目标的时间与空间导向矢量;The response of this filter to a moving target is expressed as
Figure BDA00031284839900001012
Among them, V t ∈ K×1 and V s ∈ N×1 are the temporal and spatial steering vectors of the moving target, respectively;

将接收波束进行均匀划分为Q个子波束,Q由合成孔径长度Lsyn与方位分辨率ρa决定,表示为Q=Lsyna;经波束划分后,认为接收机中的杂波信号是由这Q个方向的杂波回波叠加得到的;因此,为实现双基前视SAR非平稳杂波的有效抑制,只需滤波器在Q个子波束方向的空-时频率响应为零;根据上述思路,建立如下的约束优化问题:The receiving beam is evenly divided into Q sub-beams, and Q is determined by the synthetic aperture length L syn and the azimuth resolution ρ a , and is expressed as Q=L syna ; after the beam division, it is considered that the clutter signal in the receiver is It is obtained by the superposition of the clutter echoes in the Q directions; therefore, in order to achieve effective suppression of the non-stationary clutter in the bistatic forward looking SAR, it is only necessary that the space-time frequency response of the filter in the direction of the Q sub-beams is zero; according to Based on the above ideas, the following constrained optimization problem is established:

Figure BDA0003128483990000111
Figure BDA0003128483990000111

其中,ε为观测噪声误差容限;{fu(Wd)∣u=1,2,3}为目标函数,表示为:Among them, ε is the error tolerance of observation noise; {f u (W d )∣u=1,2,3} is the objective function, which is expressed as:

Figure BDA0003128483990000112
Figure BDA0003128483990000112

Figure BDA0003128483990000113
Figure BDA0003128483990000113

Figure BDA0003128483990000114
Figure BDA0003128483990000114

其中,fdi和fsi为第i个子波束方向上的归一化多普勒频率与归一化空间频率;目标函数f1(Wd)和f2(Wd)分别代表空-时滤波器在Q个子波束方向上设置的凹口平均值和方差;当f1(Wd)和f2(Wd)的函数值越小时,滤波器将在空-时域中对应位置生成深且平滑的凹口,从而确保滤波器的二维频率响应与杂波谱分布相匹配,以及杂波在空-时滤波后能够被充分地抑制。目标函数f3(Wd)将决定空-时滤波器凹口的宽度,f3(Wd)由两部分组成:sumRL(Wd)和sumRR(Wd),sumRL(Wd)和sumRR(Wd)分别为滤波器凹口左侧与右侧的二维频率响应之和,表示为:Among them, f di and f si are the normalized Doppler frequency and normalized spatial frequency in the ith sub-beam direction; the objective functions f 1 (W d ) and f 2 (W d ) represent space-time filtering, respectively The average value and variance of the notch set by the filter in the Q sub-beam directions; when the function values of f 1 (W d ) and f 2 (W d ) are smaller, the filter will generate deep and deep corresponding positions in the space-time domain. Smooth notches, thus ensuring that the filter's two-dimensional frequency response matches the clutter spectral distribution and that clutter is adequately suppressed after space-time filtering. The objective function f 3 (W d ) will determine the width of the space-time filter notch, f 3 (W d ) consists of two parts: sumR L (W d ) and sumR R (W d ), sumR L (W d ) ) and sumR R (W d ) are the sum of the two-dimensional frequency responses on the left and right sides of the filter notch, respectively, and are expressed as:

Figure BDA0003128483990000115
Figure BDA0003128483990000115

Figure BDA0003128483990000116
Figure BDA0003128483990000116

其中,Δfd为一个常数多普勒频率。最小化目标函数f3(Wd),可使滤波器凹口左右两侧的频率响应具有尽可能大的增益,保证一定的凹口宽度,从而避免在求解过程中出现凹口展宽的结果,从而提高双基前视SAR非平稳杂波的抑制性能。where Δf d is a constant Doppler frequency. Minimizing the objective function f 3 (W d ) can make the frequency responses on the left and right sides of the filter notch have as large a gain as possible, and ensure a certain notch width, so as to avoid the result of notch widening during the solution process, Therefore, the suppression performance of non-stationary clutter in bistatic forward looking SAR is improved.

至此,空-时滤波器的求解问题已转化成一个带约束的数学优化问题,当和目标函数F(Wd)取得最小值时,可得到双基前视SAR最优匹配的空-时滤波器。So far, the problem of solving the space-time filter has been transformed into a mathematical optimization problem with constraints. When the minimum value of the objective function F(W d ) is obtained, the space-time filter for the optimal matching of the bistatic forward looking SAR can be obtained. device.

S5、利用粒子群优化算法求解约束优化问题,得到约束优化问题的最优解;S5. Use the particle swarm optimization algorithm to solve the constrained optimization problem, and obtain the optimal solution of the constrained optimization problem;

具体实现方法为:The specific implementation method is:

S51、初始化种群数量X和迭代次数G,并设置粒子群优化算法中决策变量的边界;S51, initialize the population number X and the number of iterations G, and set the boundary of the decision variable in the particle swarm optimization algorithm;

S52、将滤波器权系数Wdi拆分为实数部分和虚数部分:S52. Split the filter weight coefficient W di into a real part and an imaginary part:

Wdi=xi+jxl,i=1,2,…,NKW di =x i +jx l , i=1,2,...,NK

其中,序号索引l=i+NK,决策向量x=[x1,x2,x3,…,x2NK]T为粒子群算法中的一个粒子,即一个优化解,该优化解采用粒子群算法进行求解,得到的最优解记为

Figure BDA0003128483990000121
Among them, the serial number index l=i+NK, the decision vector x=[x 1 ,x 2 ,x 3 ,...,x 2NK ] T is a particle in the particle swarm algorithm, that is, an optimal solution, and the optimal solution adopts the particle swarm The algorithm is solved, and the optimal solution obtained is recorded as
Figure BDA0003128483990000121

粒子群算法的具体方法为:The specific method of particle swarm algorithm is:

S521、初始化粒子群算法的相关参数,包括决策空间VD、粒子维数D、粒子数量Ω,以及迭代最大次数G;S521. Initialize the relevant parameters of the particle swarm algorithm, including the decision space V D , the particle dimension D, the particle number Ω, and the maximum number of iterations G;

S522、初始化粒子群体Γ1。令迭代次数g=1,在决策空间VD中生成Ω个粒子组成粒子群体Γ1。令

Figure BDA0003128483990000122
表示第g代粒子群体中的第i个粒子,共包含D个自变量;
Figure BDA0003128483990000123
为该粒子在决策空间中的速度。在初始化过程中,第i个粒子的位置将在其自变量最大值和最小值之间按照均匀分布随机生成,所有粒子的初始速度为零。S522. Initialize the particle group Γ 1 . Let the number of iterations g=1, generate Ω particles in the decision space V D to form a particle group Γ 1 . make
Figure BDA0003128483990000122
represents the i-th particle in the g-th generation particle population, including D independent variables in total;
Figure BDA0003128483990000123
is the velocity of the particle in the decision space. During initialization, the position of the ith particle will be randomly generated according to a uniform distribution between the maximum and minimum values of its independent variables, and the initial velocity of all particles is zero.

初始化得到粒子群体Γ1后,计算群体中的每个粒子的适应值F(xi(g))。After the particle population Γ 1 is initialized, the fitness value F(x i (g)) of each particle in the population is calculated.

S523、当迭代次数满足g∈[1,G]时,继续进行步骤S524,否则结束迭代,进入步骤S526。S523. When the number of iterations satisfies g∈[1, G], proceed to step S524; otherwise, end the iteration and proceed to step S526.

S524、根据计算所得的适应值,记录每个粒子的极值,作为该粒子的个体最优解pBest。通过粒子间的信息共享,对个体最优解进行比较,寻得当前种群的群体最优解gBest。S524. According to the calculated fitness value, record the extreme value of each particle as the individual optimal solution pBest of the particle. Through the information sharing between particles, the individual optimal solutions are compared to find the group optimal solution gBest of the current population.

S525、根据个体最优解pBest和群体最优解gBest,对粒子的位置和速度进行更新。粒子速度的更新方式如下S525 , update the position and velocity 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)v i (g+1)=κv i (g)+Δv i (g)

其中,κ为非负的惯性因子,Δvi(g)为第g代粒子群体中的第i个粒子的速度更新变量,表示为Δvi(g)=C1×rand[0,1]×(pBesti-xi(g))+C2×rand[0,1]×(gBest(g)-xi(g)),C1与C2分别为粒子的个体学习因子与社会学习因子。rand[0,1]为0到1之间均匀分布的随机数。Among them, κ is a non-negative inertia factor, Δv i (g) is the velocity update variable of the i-th particle in the g-th generation particle population, expressed as Δv i (g)=C 1 ×rand[0 , 1]× (pBest i -xi (g))+C 2 ×rand[0 , 1]×(gBest(g) -xi (g)), C 1 and C 2 are the individual learning factor and social learning factor of the particle, respectively . rand[0,1] is a random number uniformly distributed between 0 and 1.

粒子位置更新的方式如下:The particle positions are updated as follows:

xi(g+1)=xi(g)+vi(g+1)x i (g+1)=x i (g)+v i (g+1)

在对粒子属性更新后,得到了下一代的粒子群体Γg+1。重新计算每个粒子的适应值,并进行更新g=g+1,随后返回步骤S523。After updating the particle attributes, the next generation particle population Γ g+1 is obtained. Recalculate the fitness value of each particle, update g=g+1, and then return to step S523.

S526、迭代结束后,得到最后一代的粒子群体ΓG,该群体中的每个粒子将聚集在全局最优解的位置,该全局最优解即为约束优化问题的解

Figure BDA0003128483990000131
S526. After the iteration, obtain the particle group Γ G of the last generation, each particle in the group will gather at the position of the global optimal solution, and the global optimal solution is the solution of the constrained optimization problem
Figure BDA0003128483990000131

S6、根据粒子群优化算法求解得到的最优解,重构最优匹配空-时滤波器,并利用重构的最优匹配空-时滤波器对待检测单元进行滤波,得到非平稳杂波抑制后的信号。S6. According to the optimal solution obtained by the particle swarm optimization algorithm, reconstruct the optimally matched space-time filter, and use the reconstructed optimally matched space-time filter to filter the unit to be detected to obtain non-stationary clutter suppression signal after.

具体实现方法为:根据粒子群优化算法求解得到的最优解

Figure BDA0003128483990000132
重构最优匹配空-时滤波器权系数
Figure BDA0003128483990000133
如下,The specific implementation method is: according to the optimal solution obtained by the particle swarm optimization algorithm
Figure BDA0003128483990000132
Reconstructing the optimal matching space-time filter weights
Figure BDA0003128483990000133
as follows,

Figure BDA0003128483990000134
Figure BDA0003128483990000134

对于待检测单元,利用重构的最优匹配空-时滤波器进行空-时滤波,得到双基前视SAR非平稳杂波抑制后的目标信号

Figure BDA0003128483990000135
最后,可以对目标信号SMSTF(t)进行后续的参数估计与归位聚焦等处理。For the unit to be detected, use the reconstructed optimal matching space-time filter to perform space-time filtering, and obtain the target signal after the non-stationary clutter suppression of the dual-base forward looking SAR.
Figure BDA0003128483990000135
Finally, subsequent processing such as parameter estimation and homing focusing can be performed on the target signal S MSTF (t).

本实施例非平稳杂波抑制后的回波域信号如图4所示,图像域信号如图5所示,图6与图7给出了杂波抑制前后的对比结果,从结果可见,双基前视SAR杂波已得到了充分地抑制,在回波域和图像域中仅保留运动目标信号,可实现高可靠的运动目标检测。Figure 4 shows the echo domain signal after non-stationary clutter suppression in this embodiment, and Figure 5 shows the image domain signal. Figure 6 and Figure 7 show the comparison results before and after clutter suppression. The base forward-looking SAR clutter has been fully suppressed, and only the moving target signal is retained in the echo domain and image domain, which can achieve highly reliable moving target detection.

本领域的普通技术人员将会意识到,这里所述的实施例是为了帮助读者理解本发明的原理,应被理解为本发明的保护范围并不局限于这样的特别陈述和实施例。本领域的普通技术人员可以根据本发明公开的这些技术启示做出各种不脱离本发明实质的其它各种具体变形和组合,这些变形和组合仍然在本发明的保护范围内。Those of ordinary skill in the art will appreciate that the embodiments described herein are intended to assist readers in understanding the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations without departing from the essence of the present invention according to the technical teaching disclosed in the present invention, and these modifications and combinations still fall within the protection scope of the present invention.

Claims (6)

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;
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.
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 FDA0003128483980000011
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 FDA0003128483980000012
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 FDA0003128483980000021
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)。
3. The space-time matching-based bistatic forward-looking SAR clutter suppression method according to claim 1, wherein the S3 is specifically realized by the following steps: the space-time frequency information of the clutter of the unit to be detected is as follows:
Figure FDA0003128483980000022
Figure FDA0003128483980000023
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 FDA0003128483980000024
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 FDA0003128483980000025
θTand
Figure FDA0003128483980000026
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.
4. The space-time matching-based bistatic forward-looking SAR clutter suppression method according to claim 3, wherein the S4 is specifically realized by the following steps: for the system with N channels and K pulses, the filter is designedThe filter weight coefficient 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 FDA00031284839800000311
wherein,
Figure FDA0003128483980000031
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 FDA0003128483980000032
Figure FDA0003128483980000033
the response of the filter to a moving object is represented as
Figure FDA0003128483980000034
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 ═ Lsyna(ii) a After beam division, the clutter signals in the receiver are obtained by superposing clutter echoes in the Q directions; therefore, only filtering is needed to realize effective suppression of bistatic forward-looking SAR non-stationary clutterThe space-time frequency response of the wave filter in the Q sub-wave beam directions is zero; according to the above thought, the following constraint optimization problem is established:
Figure FDA0003128483980000035
wherein epsilon is the error tolerance of the observation noise; { fu(Wd) | u ═ 1,2,3} is an objective function expressed as:
Figure FDA0003128483980000036
Figure FDA0003128483980000037
Figure FDA0003128483980000038
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 FDA0003128483980000039
Figure FDA00031284839800000310
wherein, Δ fdIs a constant doppler frequency.
5. The space-time matching-based bistatic forward-looking SAR clutter suppression method according to claim 4, 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
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 FDA0003128483980000041
6. The space-time matching-based bistatic forward-looking SAR clutter suppression method according to claim 5, wherein the S6 is specifically realized by the following steps: solving the optimal solution obtained according to the particle swarm optimization algorithm
Figure FDA0003128483980000042
Reconstructing optimal matching space-time filter weight coefficients
Figure FDA0003128483980000043
As follows below, the following description will be given,
Figure FDA0003128483980000044
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 FDA0003128483980000045
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