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。     

Joint space-time interpolation for distorted linear and bistatic array geometries

This paper presents a new joint space-time interpolation technique (STINT) to improve the small sample support performance of space-time adaptive processing (STAP) with distorted linear monostatic arrays and linear bistatic array configurations. Brennan's rule for the space-time clutter covariance matrix rank is extended to monostatic linear arrays with arbitrary intersensor spacing, distorted linear arrays and bistatic geometries. It is shown that both distortion in the array geometry and bistatic operation increase the clutter rank and cause the space-time clutter covariance matrix to become range dependent. This results in lower output signal-to-interference-plus-noise ratio (SINR) for the same number of adaptive degrees of freedom and reduced available sample support. This motivates the development of the STINT technique aimed at compensating for the clutter rank inflation, while also making the clutter statistics appear more stationary across range. More specifically, a linear transformation is designed that maps the received clutter across space and time to that which would be received by a "virtual" monostatic side-looking ULA. By mapping the data to form a reduced rank clutter covariance matrix, fewer snapshots are needed for a statistically stable matrix inversion as required in STAP, thereby improving the short observation time performance. Simulation results for a typical airborne radar scenario indicate up to 10-dB SINR improvement can be obtained using STINT with limited sample support.     

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.     

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.     

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。     

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.     

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...     

机载双基地雷达杂波距离依赖补偿方法

针对杂波距离依赖造成空时自适应处理器性能下降问题,提出一种基于最小二乘估计的聚焦矩阵补偿方法。首先,利用最小二乘方法估计杂波幅度,选取幅度较大的杂波点;其次,根据杂波点的空时导向矢量求解聚焦矩阵,通过聚焦矩阵直接对训练数据进行线性变换;最后,根据变换后的数据估计出待检测单元的杂波噪声协方差矩阵,进而求出自适应权值矢量。该方法采用非均匀采样求解聚焦矩阵,杂波抑制性能更好,仿真实验验证了所提方法的有效性。      

基于传播算子的机载前视阵雷达杂波谱补偿方法

 针对机载前视阵中杂波多普勒频率随距离发生变化,导致杂波谱在距离上呈现严重的非平稳性,因此根据杂波模型提出了一种新的杂波谱补偿方法,该方法首先对接收数据进行分块处理来构造传播算子以获得杂波子空间,再将训练单元的杂波子空间向待检单元的杂波子空间进行投影得到一个变换矩阵,然后用这个变换矩阵对数据进行处理,使杂波谱得到补偿,最后利用空时自适应算法对补偿后的数据进行杂波抑制.仿真结果表明此方法有效地抑制杂波的非平稳性,提高了雷达抑制杂波的性能.     

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.     

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.

     

基于加权SPICE的MIMO-STAP稀疏恢复算法

针对机载多输入多输出(MIMO)雷达空时自适应处理(STAP)技术在非均匀杂波条件下动目标检测性能严重下降的问题,引入了加权SPICE算法用于杂波谱的稀疏恢复。加权SPICE算法可以将一大类稀疏恢复算法纳入到统一框架下,根据加权矢量不同可得LIKES,SLIM和IAA算法。这些算法不需要设置任何超参数,基于杂波样本协方差矩阵通过迭代求解未知稀疏参数。仿真实验表明,使用这些稀疏算法恢复杂波谱,可有效提升所恢复杂波谱的准确性,能够更好地实现动目标检测。      

对运动目标约束的机载前视阵雷达杂波谱补偿方法

在机载前视阵雷达中,由于地面杂波存在距离依赖性,使得杂波协方差矩阵估计不准确,进而导致空时自适应处理(STAP)的杂波抑制性能严重下降。基于配准的杂波补偿方法(RBC)能够有效地对地面杂波的距离依赖性进行补偿,但当杂波数据中含有运动目标时,使用该方法对杂波补偿后,包含在杂波中的运动目标信息会在空时平面散开,导致其无法被检测。针对上述问题,提出一种运动目标约束的杂波补偿方法,即在基于配准的方法中,根据运动目标与杂波的多普勒频率不同来添加运动目标约束保护。计算机仿真结果表明:该方法不仅能够降低杂波距离依赖性,而且可以有效地对运动目标信息进行保护。     

Fast reduced-rank STAP algorithm based on Gram–Schmidt orthogonalisation for airborne radar

In the reduced-rank space-time adaptive processing (STAP) methods, especially the principal component (PC) analysis STAP method, a set of dominant eigenvectors must be obtained by singular value decomposition of the space-time covariance matrix. Therefore, it is very difficult to be applied in practical system due to the intense computational complexity. In order to reduce the computational burden, a fast reduced-rank STAP algorithm based on Gram–Schmidt (GS) orthogonalisation is proposed in this article. In the proposed GS-PC STAP method, the clutter subspace is reconstructed by the GS orthogonalisation of training samples. Then, the STAP adaptive weight vector is calculated by orthogonally projecting the quiescent weight vector into clutter subspace, which can hold fast convergence measure of effectiveness (MOE) and require less computational complexity by compared with the conventional PC method. Based on the simulated data and multichannel airborne radar measurements data, the corresponding convergence MOE and the clutter suppression performances are verified in the article.     

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.     

Improved Multistage Wiener Filters in Nonhomogeneous Clutter Environments

A new method combining space-time preprocessing with multistage Wiener filters（STPMWF）is proposed to improve the performance of space-time adaptive processing（STAP）in nonhomogeneous clutter scenario.The new scheme only requires the data from the primary range bin,thus it can suppress discrete interferers efficiently,without calculating the inverse of covariance matrix.Comparing to the original MWF approach,the proposed scheme can be regarded as practical solutions for robust and effective STAP of nonhomogeneous radar data.The theoretical analysis shows that our STPMWF is simple in implementation and fast in convergence.The numeric results by using simulated data exhibit a good agreement with the proposed theory.     

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。     

Analysis of STAP algorithms for cases with mismatched steering andclutter statistics

In the majority of adaptive radar detection algorithms, the covariance matrix for the clutter-plus-noise is estimated using samples taken from range cells surrounding the test cell. In a nonhomogeneous environment, this can lead to a mismatch between the mean of the estimated covariance matrix and the true covariance matrix for the test cell. Further, an inaccurate target steering vector may also be employed. Closed-form expressions are provided, which give the performance for such cases when any of a set of popular space-time adaptive processing (STAP) algorithms are used. The expressions are exact for some interesting cases. For some other cases, it is demonstrated that the expressions provide good approximations to the exact performance. To simplify the analysis, the samples from the surrounding range cells are assumed to be independent and identically distributed, and these samples are assumed to be independent from the sample taken from the test cell. A small number of important parameters describe which types of mismatches are important and which are not. Monte Carlo simulations, which closely match the predictions of our equations, are included     

基于原子范数的无网格稀疏恢复非正侧视阵空时自适应处理算法

基于杂波谱稀疏恢复的空时自适应处理(STAP)方法可以显著降低对杂波样本数的要求,十分适合缺少样本情况下的机载雷达杂波抑制。然而,现有稀疏恢复STAP方法利用离散化空时导向矢量字典进行重构,在非正侧视阵情况下,由于杂波脊不在字典网格点上,字典失配问题严重影响杂波抑制性能。针对上述问题,该文提出了一种基于原子范数的无网格稀疏恢复空时自适应处理方法(ANM-STAP),利用低秩矩阵恢复理论实现连续空时平面的稀疏恢复,克服了稀疏恢复中的字典失配问题,获得了非正侧视阵情况下的高分辨率杂波空时谱,有效提高了STAP杂波抑制性能。Monte Carlo实验证明,该文方法STAP处理性能在非正侧视阵情况下优于已有字典离散化处理的稀疏恢复STAP方法。