一种简便的MIMO阵列雷达CFAR检测器

本文提出了一种适用于MIMO阵列雷达的简便的CFAR检测器,它利用了MIMO阵列雷达观测空间维数高的特点,通过直接滤除杂波干扰子空间的方式抑制杂波和干扰。该检测器的简便性在于杂波子空间可以离线估计与存储,而干扰子空间的估计也只需在低维空间上进行,而其原因是在估计杂波和干扰子空间时没有利用距离参考单元观测样本,而是利用了已知的系统参数、杂波子空间结构以及干扰协方差矩阵的模块对角性质。仿真结果表明,在杂波的理想模型条件下,选择适当的估计方法可以获得较高的杂波子空间估计精度,由此得到的CFAR检测器的性能也非常接近于已知杂波干扰子空间条件下的检测性能。      

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.     

MIMO Radar Ambiguity Properties and Optimization Using Frequency-Hopping Waveforms

The concept of multiple-input multiple-output (MIMO) radars has drawn considerable attention recently. Unlike the traditional single-input multiple-output (SIMO) radar which emits coherent waveforms to form a focused beam, the MIMO radar can transmit orthogonal (or incoherent) waveforms. These waveforms can be used to increase the system spatial resolution. The waveforms also affect the range and Doppler resolution. In traditional (SIMO) radars, the ambiguity function of the transmitted pulse characterizes the compromise between range and Doppler resolutions. It is a major tool for studying and analyzing radar signals. Recently, the idea of ambiguity function has been extended to the case of MIMO radar. In this paper, some mathematical properties of the MIMO radar ambiguity function are first derived. These properties provide some insights into the MIMO radar waveform design. Then a new algorithm for designing the orthogonal frequency-hopping waveforms is proposed. This algorithm reduces the sidelobes in the corresponding MIMO radar ambiguity function and makes the energy of the ambiguity function spread evenly in the range and angular dimensions.      

Improved design of unimodular waveforms for MIMO radar

This paper proposes a novel method of unimodular transmitting waveforms design for multiple-input multiple-output (MIMO) radar to strengthen the detection performance in the presence of clutter and white Gaussian noise. An improved iterative algorithm is put forward to maximize the signal-to-clutter-plus-noise ratio (SCNR) under the constant modulus constraint. During iterations, the optimization of unimodular waveforms with filters fixed is a nonconvex fractional quadratically constrained quadratic program problem, which is NP-hard and not able to be solved in polynomial time. An algorithm based on semidefinite programming relaxation combined with bisection and Gaussian randomization is introduced to provide the high-quality suboptimal solutions with a polynomial time computational complexity. The analysis on the approximation bound is given to prove the tightness of the semidefinite programming relaxation and so the correctness of the proposed algorithm. The simulation results show that the improved method is efficient in designing unimodular waveforms for MIMO radar to achieve a better SCNR performance.     

一种改进的知识辅助MIMO雷达空时自适应处理方法

针对机载多输入多输出(MIMO)雷达杂波抑制问题,该文提出一种改进的基于知识辅助的空时自适应信号处理算法(KA-STAP)。根据杂波在空时2维平面的先验分布离线构造杂波子空间,以此替代基于扁长椭球波函数(PSWF)估计的杂波子空间,避免了复杂运算。仿真结果表明,所提方法不仅能减小运算量,还能获得更深的零陷以及更优的旁瓣性能。     

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.     

一种两级机载MIMO雷达空时自适应处理方法

机载MIMO雷达可联合利用时间自由度、发射和接收空间自由度抑制杂波,但只能利用接收空间自由度抑制有源干扰。基于此特点,该文提出一种机载MIMO雷达的两级空时自适应处理方法抑制杂波和干扰。第1级处理中只利用部分空间接收自由度进行干扰抑制,同时实现降维处理;第2级通过匹配滤波获得发射空间自由度,并联合剩余接收空间自由度和时域自由度,进行空时联合自适应处理抑制杂波。该方法通过分级处理既有效利用了MIMO雷达的发射自由度进行杂波抑制,又同时大大减低了计算量和样本需求。理论分析表明存在强干扰时,两级处理的理论性能可以逼近全维处理的最优性能。仿真实验表明了该算法的有效性。     

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.     

有源干扰条件下机载MIMO雷达STAP协方差矩阵秩的分析

该文分析了正侧阵机载MIMO雷达杂波加有源干扰协方差矩阵的结构,得到杂波加有源干扰协方差矩阵秩的上界为杂波的秩与有源干扰的秩之和减去有源干扰个数。并由此得到MIMO雷达杂波加有源干扰协方差矩阵非满秩而SIMO雷达杂波加有源干扰协方差矩阵满秩时有源干扰个数的范围。当有源干扰的数目在此范围时,SIMO雷达的理论性能严重下降,而MIMO雷达在理论上仍然有足够的自由度来抑制杂波和有源干扰,从而保证有较好的性能。通过仿真实验验证了上述结论。     

不需要训练数据的分布式MIMO雷达距离扩展目标检测器

在非均匀杂波环境中,研究了高分辨率分布式多输入多输出(MIMO)雷达的距离扩展目标检测问题。由于分布式MIMO雷达观测到的杂波是非均匀的,无法获得足够的独立同分布的均匀训练数据来估计检测单元的杂波协方差矩阵。采用复逆Wishart分布对杂波协方差矩阵建模,基于该杂波模型设计了一种不需要训练数据的分布式MIMO雷达距离扩展目标广义似然比检测器。数值仿真结果表明:在非均匀杂波环境中,所设计的检测器的性能比用训练数据的协方差矩阵类检测器有明显的改进。     

MIMO Radar Waveform Optimization With Prior Information of the Extended Target and Clutter

The concept of multiple-input multiple-output (MIMO) radar allows each transmitting antenna element to transmit an arbitrary waveform. This provides extra degrees of freedom compared to the traditional transmit beamforming approach. It has been shown in the recent literature that MIMO radar systems have many advantages. In this paper, we consider the joint optimization of waveforms and receiving filters in the MIMO radar for the case of extended target in clutter. A novel iterative algorithm is proposed to optimize the waveforms and receiving filters such that the detection performance can be maximized. The corresponding iterative algorithms are also developed for the case where only the statistics or the uncertainty set of the target impulse response is available. These algorithms guarantee that the SINR performance improves in each iteration step. Numerical results show that the proposed methods have better SINR performance than existing design methods.      

On parameter identifiability of MIMO radar with waveform diversity

Parameter identifiability is one of the most interesting issues in multiple-input multiple-output (MIMO) radar systems. Previous works have presented conditions for parameter identifiability of the systems with uncorrelated waveforms. In this paper, under general waveform assumptions, we investigate conditions for parameter identifiability of MIMO radar systems with more flexible waveform diversity, where the transmitted waveforms are not restricted to be uncorrelated. The conditions specify the maximum number of targets that can be uniquely localized, which are obtained based on the rule of time-bandwidth product. Moreover, it is found that the conditions are in terms of the rank and structure of the waveform covariance matrix (WCM) and the geometry of MIMO radar systems. Numerical examples are provided to verify the effectiveness of the conditions.     

Space-time adaptive processing for manoeuvring airborne radar

Space-time adaptive processing (STAP) techniques provide simultaneous rejection of jamming and clutter in airborne radar. The greatest benefits over conventional MTI (moving target indication) approaches are in terms of a capability to detect slow-moving targets which possess the same Doppler frequency as mainlobe clutter returns. This paper examines the effects of platform manoeuvre on STAP clutter and jamming rejection performance for a forward-facing array (i.e. where the array is orientated transversally to the direction of travel). It is shown that STAP slow-target detection performance is not sensitive to the radar platform orientation. It is also demonstrated that, under conditions of manoeuvre, STAP can provide better jammer rejection performance than architectures which cascade conventional clutter filtering and spatial adaptive beamforming     

双基地MIMO雷达杂波建模与分析

双基地多输入多输出(MIMO)雷达发射正交波形,不同地面杂波单元的回波信号形式存在明显差异,传统的杂波建模方法已不再适用。文中提出一种双基地MIMO雷达杂波建模方法,将地面划分为满足发射角度分辨率、接收角度分辨率、距离分辨率三重约束的杂波散射单元,并计算各单元的杂波回波信号,然后将各单元回波信号采用矢量相加求得总的杂波视频信号。最后通过对传统雷达杂波建模与本文建模方法的仿真与分析,增强了对双基地MIMO雷达杂波谱结构的认识,有助于开展对双基地MIMO雷达杂波抑制的研究。     

MIMO Radar Waveform Constraints for GMTI

Ground moving-target indication (GMTI) provides both an opportunity and challenge for coherent multiple-input multiple-output (MIMO) radar. MIMO techniques can improve a radar's angle estimation and the minimum detectable velocity (MDV) for a target. However, the challenge of clutter mitigation places significant constraints on MIMO radar waveforms. In this paper, the loss of target return because of clutter mitigation (signal-to-noise ratio (SNR) loss) is the driving performance metric. The ideal, orthogonal repeated-pulse waveform is shown not to exist. Pulse-to-pulse time-varying waveforms, such as Doppler-division multiple access (DDMA), are shown to offer SNR loss performance approaching ideal MIMO systems.      

机载MIMO雷达空时杂波块对消器

针对机载MIMO雷达杂波空时耦合的特性,提出了一种应用于机载MIMO雷达的空时杂波块对消器。利用MIMO雷达接收杂波的多普勒相位和空域接收相位之间的关系,设计系数矩阵来对消杂波,后续级联空时自适应处理方法能进一步抑制杂波和提高动目标检测性能。仿真实验表明,该杂波块对消器能对正侧视和非正侧视机载MIMO雷达均有较好的杂波抑制能力,从而进一步提高了后续空时自适应处理算法的动目标检测性能。     

机载MIMO雷达杂波特征结构的空时自回归算法

非均匀环境下,机载MIMO雷达杂波不再满足独立同分布(independent identically distributed, IID)条件,由于没有足够多的IID样本来估计杂波协方差矩阵,从而导致传统的STAP方法性能急剧下降。本文研究了非均匀环境下机载MIMO雷达的杂波抑制问题,将空时自回归算法(Space time autoaggressive , STAR)引入机载MIMO雷达,降低训练样本数目,减小运算量。针对STAR算法中参数确定复杂、易受训练样本数目不足影响的缺点,提出了一种基于杂波特征结构的模型参数确定方法,仿真结果表明,该方法能在极小训练样本条件下,有效确定模型参数,实现杂波抑制,适用于非均匀杂波环境。      

The Rao and Wald Tests Designed for Distributed Targets with Polarization MIMO Radar in Compound-Gaussian Clutter

In high resolution radars, the distributed target is usually modeled as a few isolated points referred to multiple dominant scattering centers, while the clutter is a compound-Gaussian model. Additionally, the polarimetric diversity can be exploited to enhance detection performance. Motivated by extending the detection problem of multiple-input multiple-output (MIMO) radar to such cases, this paper mainly addresses distributed targets detection problem with polarization MIMO radar against a compound-Gaussian clutter dominated scenario with unknown covariance matrix. The adaptive detectors based on Rao and Wald criteria are studied, and a two-step design procedure is adopted. Specifically, the Rao and Wald tests are derived by assuming a known covariance matrix, and then a suitable estimation of the covariance matrix based on the secondary data is inserted into the derived detectors to make them fully adaptive. Some numerical results are presented together with a polarization generalized likelihood ratio test (GLRT), showing that the derived detectors provide excellent detection performance in spiky clutter for distributed targets, and that the polarimetric diversity can be exploited to improve detection performance. Overall, the Wald test performs the best.     

Iterative Adaptive Approaches to MIMO Radar Imaging

Multiple-input multiple-output (MIMO) radar can achieve superior performance through waveform diversity over conventional phased-array radar systems. When a MIMO radar transmits orthogonal waveforms, the reflected signals from scatterers are linearly independent of each other. Therefore, adaptive receive filters, such as Capon and amplitude and phase estimation (APES) filters, can be directly employed in MIMO radar applications. High levels of noise and strong clutter, however, significantly worsen detection performance of the data-dependent beamformers due to a shortage of snapshots. The iterative adaptive approach (IAA), a nonparametric and user parameter-free weighted least-squares algorithm, was recently shown to offer improved resolution and interference rejection performance in several passive and active sensing applications. In this paper, we show how IAA can be extended to MIMO radar imaging, in both the negligible and nonnegligible intrapulse Doppler cases, and we also establish some theoretical convergence properties of IAA. In addition, we propose a regularized IAA algorithm, referred to as IAA-R, which can perform better than IAA by accounting for unrepresented additive noise terms in the signal model. Numerical examples are presented to demonstrate the superior performance of MIMO radar over single-input multiple-output (SIMO) radar, and further highlight the improved performance achieved with the proposed IAA-R method for target imaging.      

前视阵FDA-STAP雷达距离模糊杂波抑制方法

高重复频率前视阵机载雷达地杂波不仅存在严重的距离依赖性,而且存在距离模糊问题,传统空时自适应处理(STAP)方法在距离依赖和距离模糊同时存在时难以有效实现杂波补偿和杂波抑制。针对距离模糊下的机载雷达杂波抑制问题,该文提出一种基于频率分集阵列STAP雷达的距离模糊杂波分离与抑制方法。该方法利用频率分集阵列发射导向矢量的距离角度2维依赖性,通过空间频率域子空间投影实现距离模糊杂波的分离,然后对分离后的杂波分别进行距离依赖补偿,最终实现无模糊区域和模糊区域的杂波抑制和运动目标检测。仿真实验验证了该文方法的有效性。