A clutter-suppression method for airborne bistatic polarization radar based on polarization-space-time adaptive processing

Clutter suppression poses serious problems for airborne, bistatic radar systems. Suppression may be increased using space-time adaptive processing (STAP), but suppression of slow targets is poor and target detectability is compromised. Furthermore, sufficient independent and identically (IID) training samples cannot be obtained through the use of practical applications, and the STAP performance degrades significantly due to the inaccuracy of the estimated clutter-plus-noise covariance matrix, especially in nonstationary and heterogeneous environments. Here, we present a new airborne, bistatic radar system. We transform the array from a single polarized channel to two channels, each with two orthogonally polarized antennae, and combine polarization-dimensional information with that of the space-time domain; we term our algorithm “polarization-space-time adaptive processing”. This algorithm further suppresses clutter and enhances the detection of slow targets. Sparse recovery space-time adaptive processing (SR-STAP) can reduce the need for clutter samples and suppress clutter effectively using limited training samples for airborne radar. The algorithm first uses the clutter sparse recovery function of STAP to suppress clutter in the H and V channels. Then, polarization processing is employed to further restrict mainlobe clutter. We present numerical examples to demonstrate the effectiveness of the new technique.

     

Range dependent clutter rejection using range-recursive space-time adaptive processing (STAP) algorithms

We here address the issue of ground clutter rejection for the detection of slowly moving targets in a non-side looking (NSL) array configuration airborne radar. The optimum space-time adaptive processing (STAP) filter needs the knowledge of the inverse of the space-time covariance matrix. In practice, it is unknown and has to be estimated. The most popular approximated method is the sample matrix inversion (SMI) method which consists in inverting the covariance matrix estimated by an average of the sample matrix over the secondary range cells. This estimator is unbiased in case of i.i.d. data. In an NSL configuration, the clutter power spectrum is range dependent and the data are consequently not i.i.d. We here present a solution to mitigate this range dependency of the data: the range recursive subspace-based algorithms. They are used in two architectures: a fully and a partially adaptive ones. Then a new range-recursive algorithm using Taylor series expansion is investigated. The performance of these algorithms are compared with that of the conventional STAP algorithms in term of SINR loss.     

Clutter suppression for airborne phased radar with conformal arrays by least squares estimation

The radar clutter statistics for airborne conformal arrays varies by range, i.e., the clutter distributions are nonstationary, which causes performance degradation for the conventional space-time adaptive processing (STAP), which estimates the clutter covariance matrix (CCM) from data at adjacent range cells. In this paper, a novel clutter suppression method for airborne phased radar with conformal arrays is proposed that takes a form of corrected sample matrix inversion (SMI) through the CCM estimated by the least squares (LS) estimation. The estimated CCM can provide partial information about the real CCM in the novel method, which results in improved detection performance for targets in conformal array applications. Simulation results relative to several typical conformal arrays verify the effectivity of the presented method.     

机载共形阵雷达杂波抑制方法研究

 共形阵机载相控阵雷达由于其特殊的几何配置导致杂波统计特性随距离的变化而变化,即杂波呈现严重的非均匀性,从而使得传统空时自适应处理(STAP)方法的性能严重下降.本文首先分析了共形阵机载相控阵雷达的杂波特性,通过对共形阵杂波多普勒频率的数学变换,从理论上说明了共形阵配置导致机载雷达杂波非均匀性的机理,并给出了一种衡量杂波非均匀性强弱的定量准则,然后提出了一种借助最小二乘(LS)参数估计修正传统SMI的共形阵机载相控阵雷达杂波抑制方法,最后针对几种典型共形阵列,通过仿真实验验证了本文方法的有效性.     

A subspace method for space time adaptive processing

The problem of space-time adaptive processing (STAP) using a nonlinear array is considered. A key part of STAP is the estimation of the space-time covariance matrix of the received data. The conventional method of doing this causes significant performance degradation at short ranges because of the nonstationarity of the data. We present an alternative algorithm which circumvents this problem by projecting the data on the subspace orthogonal to the clutter and jammer subspaces. The clutter subspace is computed from the known array manifold, while the jammer subspace is estimated from clutter-free measurements. Numerical examples illustrate the performance improvement achieved at short ranges.     

改进的非均匀频率采样配准双基杂波抑制方法

机载双基地雷达杂波的距离依赖性降低空时自适应处理(STAP)杂波抑制能力,频率非均匀采样配准(RB-NFS)技术通过数据域信号处理可缓解杂波的距离依赖性.对该技术在机载双基地杂波抑制中面临的问题进行深入分析并给出改进方法,针对频率非均匀采样配准中杂波空时导向矩阵构造复杂,且存在杂波抑制不充分现象,提出引入广义逆矩阵或采...     

基于多帧观测联合感知的空时自适应处理

在机载雷达体制中,空时自适应处理STAP(Space-Time Adaptive Processing)可有效抑制杂波并显著提高雷达对慢动目标的检测性能.但是在非均匀环境中,缺乏独立同分布的训练样本会使STAP性能严重下降.针对这个问题,本文提出一种基于多帧观测联合感知的空时自适应处理方法.该方法交替发射正交信号和普通的相控阵信号.检测前,通过当前及先前的环境回波感知观测场景获取杂波信息;检测时,先利用杂波信息结合平台参数及系统参数估计杂波协方差矩阵,再将估计的协方差矩阵与样本协方差矩阵进行组合以构造空时滤波器,抑制杂波,提高输出信杂比.仿真结果表明,与现有的知识辅助类STAP算法和降维算法相比,该方法在缺乏准确先验知识的情况下,可以有效地抑制非均匀杂波.     

机载非正侧视阵知识辅助STAP方法

机载非正侧视阵的近程杂波具有严重的距离依赖性,在距离模糊条件下,现有的空时自适应处理(Space-Time Adap-tive Processing,STAP)算法难以对其进行有效抑制,为此提出了知识辅助的STAP处理方法。通过使用新的样本选择策略,以及改进的针对近程杂波的知识辅助协方差矩阵模型,该方法在处理非正侧视阵近程杂波时的性能接近最优,远高于一般的基于样本估计协方差矩阵的方法,并克服了缺少训练样本的问题。仿真结果证明了该方法的有效性。     

基于稀疏重构的机载雷达KA-STAP杂波抑制算法

机载雷达非均匀杂波环境下的空时自适应处理（STAP）算法会因杂波协方差矩阵估计不准导致其杂波抑制性能下降。传统知识辅助 STAP (KA-STAP)算法性能依赖于先验知识的准确程度以及配准精度,先验信息的失配可能会导致算法性能恶化。本文提出一种基于稀疏恢复技术构造杂波加噪声协方差矩阵的KA-STAP算法。该算法不依赖于先验信息,首先利用稀疏贝叶斯学习技术通过少量回波样本估计出稳健的辅助协方差矩阵,然后结合采样协方差矩阵进行空时处理。在小样本非均匀杂波场景下,该算法的输出性能优于传统KA-STAP算法。仿真结果表明了本文方法的有效性。     

天基稀疏阵杂波自由度分析

天基稀疏孔径实现地面运动目标指示具有很大吸引力,如何简单准确地估计空时自适应处理器的杂波自由度是一个重要的课题.针对天基稀疏孔径的两种常用类型——稀疏子阵列和非全向性阵元稀疏阵,基于空时等效理论,从等效的虚拟空间阵列角度分析了阵列的杂波自由度,得到了预测杂波自由度的简单方法,仿真结果验证了方法的有效性.最后从杂波自由度得到分布式小卫星雷达实现地面运动目标指示的基本设计约束条件.     

双基地机载预警雷达空时自适应处理方法

本文在文献[1] 所建杂波模型的基础上,进一步研究空时自适应处理对双基地机载预警雷达杂波的抑制问题.深入分析了双基地机载预警雷达杂波距离多普勒特性,进而提出了一种基于距离多普勒补偿的空时二维自适应处理方法.理论分析与计算机仿真表明,该算法能有效实现工作于低重频时双基地机载预警雷达杂波抑制.     

通道失配对空时自适应处理性能的影响分析

通道失配严重影响着空时自适应处理(STAP)的性能,定量地分析通道失配对STAP性能的影响对于STAP算法的工程应用具有重要意义。文中根据通道失配是否依赖于到达角、信号带宽是否为窄带等方面,从理论上推导了存在通道失配时的杂波协方差矩阵,并仿真分析了通道失配对STAP性能的影响,为STAP算法理论建模、STAP系统设计提供理论依据。     

稳健的基于参数化协方差矩阵估计的空时自适应处理方法

参数化协方差矩阵估计（Parametric Covariance Matrix Estimation,PCE）方法利用雷达系统参数估计杂波协方差矩阵（Clutter Covariance Matrix,CCM）,显著提升非均匀环境下空时自适应处理（Space-Time Adaptive Processing,STAP）的性能;但是在系统参数和杂波分布存在误差情况下,性能下降严重.本文提出一种稳健的基于PCE方法的STAP杂波抑制方法.首先利用稀疏恢复方法与Radon变换估计杂波分布,然后提出一种归一化广义内积统计量修正杂波的分布,最后利用PCE方法估计CCM并进行STAP杂波抑制.通过分析舰载高频地波雷达仿真和实测数据处理结果表明：所提方法的稳健性大幅提升,相比稀疏恢复STAP方法和前后向空时平滑STAP方法滤波器凹口更加准确且更深,在有效抑制杂波的同时更利于慢速目标的检测.     

Algorithms for mitigating terrain-scattered interference

This paper summarises a study of terrain-scattered interference (TSI) mitigation techniques, focusing on joint TSI mitigation and monostatic clutter nulling for airborne radars. It begins with an overview of TSI phenomenology and two-dimensional space-time adaptive processing (STAP) techniques for cancelling TSI. Two approaches for mitigating combined TSI and clutter are then examined. In the first approach, a sequence of two-dimensional adaptive filters is used. The first of these filters cancels TSI, while the second filter cancels monostatic clutter. In the second approach, a single, three-dimensional pre-Doppler STAP algorithm is used to cancel clutter and TSI simultaneously. Performance results for a representative system are shown to illustrate the trade-offs inherent in designing a mitigation approach     

基于杂波谱稀疏恢复的空时自适应处理

在机载雷达体制中,空时自适应处理(STAP)可有效抑制杂波并完成动目标检测.但在实际杂波环境中,由于缺乏独立同分布的训练样本,传统STAP算法性能下降严重.针对这一问题,我们利用STAP体制下杂波在角度-多普勒域上的稀疏性,提出基于稀疏恢复的SR-STAP方法,可在少量训练样本下实现高分辨空时杂波谱及相应杂波协方差矩阵...     

MIMO Radar Space–Time Adaptive Processing Using Prolate Spheroidal Wave Functions

In the traditional transmitting beamforming radar system, the transmitting antennas send coherent waveforms which form a highly focused beam. In the multiple-input multiple-output (MIMO) radar system, the transmitter sends noncoherent (possibly orthogonal) broad (possibly omnidirectional) waveforms. These waveforms can be extracted at the receiver by a matched filterbank. The extracted signals can be used to obtain more diversity or to improve the spatial resolution for clutter. This paper focuses on space-time adaptive processing (STAP) for MIMO radar systems which improves the spatial resolution for clutter. With a slight modification, STAP methods developed originally for the single-input multiple-output (SIMO) radar (conventional radar) can also be used in MIMO radar. However, in the MIMO radar, the rank of the jammer-and-clutter subspace becomes very large, especially the jammer subspace. It affects both the complexity and the convergence of the STAP algorithm. In this paper, the clutter space and its rank in the MIMO radar are explored. By using the geometry of the problem rather than data, the clutter subspace can be represented using prolate spheroidal wave functions (PSWF). A new STAP algorithm is also proposed. It computes the clutter space using the PSWF and utilizes the block-diagonal property of the jammer covariance matrix. Because of fully utilizing the geometry and the structure of the covariance matrix, the method has very good SINR performance and low computational complexity.     

基于环境动态感知的空时自适应处理

在非均匀环境中,缺乏独立同分布的训练样本会使空时自适应处理(Space-Time Adaptive Processing, STAP)算法性能严重下降。针对这个问题,该文提出一种基于环境动态感知的空时自适应处理方法。该方法首先通过发射一组正交信号感知观测场景获取杂波信息;然后利用杂波信息结合平台参数及系统参数预测未来一段时间内杂波的协方差矩阵;最后将预测的协方差矩阵与样本协方差矩阵进行组合以构造空时滤波器。仿真结果表明,与传统的知识辅助类STAP算法相比,该方法在缺乏准确先验知识的情况下依然可以有效地抑制非均匀环境中的杂波。     

基于SA-MUSIC理论的联合稀疏恢复STAP算法

基于子空间扩展多重信号分类(SA-MUSIC)理论对杂波空时二维谱进行联合稀疏恢复,实现小样本情况下空时自适应处理(STAP)性能的显著提升.首先,提出空时导向矢量相关性模型,利用该模型分析杂波在空时二维平面上的稀疏本质,解释用部分空时导向矢量近似整个杂波子空间的合理性.其次,提出基于SA-MUSIC理论的联合稀疏恢复STAP算法(SA-MUSIC-STAP),该算法仅需极少训练样本便可实现对杂波协方差矩阵的准确估计,并实现有效的杂波抑制.仿真实验验证了SA-MUSIC-STAP算法的有效性.     

Array pattern distortion and remedies in space-time adaptiveprocessing for airborne radar

Space-time adaptive processing (STAP) for airborne early warning radar has been a very active area of research since the late 1980's. An airborne rectangular planar array antenna is usually configured into subarrays and then partial adaptive processing is applied to the outputs of these subarrays. In practice, three kinds of errors are often encountered: the array gain and phase errors existing in each element, the channel gain and phase errors, and the clutter covariance matrix estimation errors due to insufficient secondary data samples. These errors not only degrade the clutter suppression performance, but also cause the adapted array patterns to suffer much distortion (high sidelobes and distorted mainbeams), which may result in the rise of false-alarm probability and make the adaptive monopulse tracking and sidelobe blanketing more difficult. In this paper, the causes of the above three kinds of errors to array pattern distortion are discussed and a novel quadratic soft constraint factored approach is proposed to precisely control the peak sidelobe level of adapted patterns. The soft constraint factor can be determined explicitly according to the peak sidelobe level desired and the known or desired tolerant error standard deviations. Numerical results obtained by using high-fidelity simulated airborne radar clutter data are provided to illustrate the performance of the proposed approach. Although the method is presented for STAP, it can be directly applied to the conventional adaptive beamforming for rectangular planar arrays used to suppress jammers     

Forward-backward averaging in the presence of array manifold errors

We investigate the use of forward-backward (f/b) averaging for estimating the covariance matrix used for adaptive beamforming and space-time adaptive processing (STAP). We demonstrate that the estimation loss is reduced by the use of f/b averaging and, for some STAP cases, f/b averaging can even quadruple the available sample support. We also show that unknown array manifold errors have little effect on the effectiveness of f/b averaging. The gain from f/b averaging is demonstrated on data from the mountaintop database