一种简化的机载MIMO雷达杂波特征相消器

针对机载多输入多输出(MIMO)雷达杂波抑制问题,提出一种简化的杂波特征相消器(EC).根据杂波在空时二维平面的先验分布离线构造杂波子空间.以此替代由协方差矩阵特征值分解(EVD)得到的杂波子空间,从而将最优权简化为一个确知投影矩阵与目标信号空时二维导向矢量的乘积,避免了传统EC方法中复杂的协方差矩阵估计和EVD运算,...     

Theory of partially adaptive radar

This work extends the recently introduced cross-spectral metric for subspace selection and dimensionality reduction to partially adaptive space-time sensor array processing. A general methodology is developed for the analysis of reduced-dimension detection tests with known and unknown covariance. It is demonstrated that the cross-spectral metric results in a low-dimensional detector which provides nearly optimal performance when the noise covariance is known. It is also shown that this metric allows the dimensionality of the detector to be reduced below the dimension of the noise subspace eigenstructure without significant loss. This attribute provides robustness in the subspace selection process to achieve reduced-dimensional target detection. Finally, it is demonstrated that the cross-spectral subspace reduced-dimension detector can outperform the full-dimension detector when the noise covariance is unknown, closely approximating the performance of the matched filter.     

Asymptotic distribution of the conditional signal-to-noise ratio in an eigenanalysis-based adaptive array

The statistical characterization of the conditioned signal-to-noise ratio (SNR) of the sample matrix inversion (SMI) method has been known for some time. An eigenanalysis-based detection method, referred to as the eigencanceler, has been shown to be a useful alternative to SMI, when the interference has low rank. In this work, the density function of the conditioned SNR is developed for the eigencanceler. The development is based on the asymptotic expansion of the distribution of the principal components of the covariance matrix. It is shown that, unlike the SMI method, the eigencanceler yields a conditional SNR distribution that is dependent on the covariance matrix, It is further shown that simpler, covariance matrix-independent approximations can be found for the large interference-to-noise case. The new distribution is shown to be in good agreement with the numerical data obtained from simulations.     

Principal components, covariance matrix tapers, and the subspaceleakage problem

A new class of robust space-time adaptive beamforming techniques is introduced to address a broad range of subspace leakage phenomena that arise in many sensor array applications. When present, these leakage phenomena can significantly increase the effective rank of the dominant colored noise interference spectrum, thereby reducing the appeal of techniques that exploit low-rank dominant interference (such as principal components (PC) or diagonal loading) to reduce sample support (training) requirements. By combining the covariance matrix taper (CMT) approach with either PC or diagonal loading, the minimal sample support properties of these techniques can be preserved     

Noise subspace techniques in non-gaussian noise using cumulants

We consider noise subspace methods for narrowband direction-of-arrival or harmonic retrieval in colored linear non-gaussian noise of unknown covariance and unknown distribution. The non-gaussian noise covariance is estimated via higher order cumulants and combined with correlation information to solve a generalized eigenvalue problem. The estimated eigenvectors are used in a variety of noise subspace methods such as multiple signal classification (MUSIC), MVDR and eigenvector. The noise covariance estimates are obtained in the presence of the harmonic signals, obviating the need for noise-only training records. The covariance estimates may be obtained nonparametrically via cumulant projections, or parametrically using autoregressive moving average (ARMA) models. An information theoretic criterion using higher order cumulants is presented which may be used to simultaneously estimate the ARMA model order and parameters. Third- and fourth-order cumulants are employed for asymmetric and symmetric probability density function (pdf) cases, respectively. Simulation results show considerable improvement over conventional methods with no prewhitening. The effects of prewhitening are particularly evident in the dominant eigenvalues, as revealed by singular value decomposition (SVD) analysis     

直接扩频通信系统中最小误码率准则下的特征相消器

首先研究最小误码率准则下的特征相消器,再由最小化误码率（MBER）准则和约束条件的最小均值输出能量（MMOE）准则之间存在的关系,将MBER准则下特征相消器的设计转化为约束条件的MMOE下的干扰抑制器的设计,并利用子空间跟踪算法实现MMOE准则下的干扰抑制器。仿真表明,采用子空间跟踪算法比同准则下的RLS算法有更低的计算量和更好的抗干扰效果。     

Eigenanalysis for interference cancellation with minimum redundancy array structure

In a previous work, the principles of the eigenanalysis method for interference cancellation was studied for uniform regular array (URA) structure, wherein the array elements are equally spaced. The main objective of this paper is to extend the results of that work to adaptive arrays with different geometry termed "minimum redundancy array" (MRA). An efficient method is presented to solve the minimum variance optimization problem and explicit analytical solutions are derived for the optimal weight vector and output noise variance of the eigencanceler. Performance analysis of the general N-element array was first derived and used to extract the performance of MRA. URA is also considered as a special case. The closed-form equation depicts the minimum variance noise residue for the single interference case, while an interactive procedure is suggested for the two interferences case. If there are more than two interferences, we propose to use direct numerical calculation.     

Space-time autoregressive filtering for matched subspace STAP

Practical space-time adaptive processing (STAP) implementations rely on reduced-dimension processing, using techniques such as principle components or partially adaptive filters. The dimension reduction not only decreases the computational load, it also reduces the sample support required for estimating the interference statistics. This results because the clutter covariance is implicitly assumed to possess a certain (nonparametric) structure. We demonstrate how imposing a parametric structure on the clutter and jamming can lead to a further reduction in both computation and secondary sample support. Our approach, referred to as space-time autoregressive (STAR) filtering, is applied in two steps: first, a structured subspace orthogonal to that in which the clutter and interference reside is found, and second, a detector matched to this subspace is used to determine whether or not a target is present. Using a realistic simulated data set for circular array STAP, we demonstrate that this approach achieves significantly lower signal-to-interference plus noise ratio (SINR) loss with a computational load that is less than that required by other popular approaches. The STAR algorithm also yields excellent performance with very small secondary sample support, a feature that is particularly attractive for applications involving nonstationary clutter.     

Reduced-rank STAP for high PRF radar

Due to the range ambiguity of high pulse-repetition frequency (HPRF) radars, echoes from far-range fold over near-range returns. This effect may cause low Doppler targets to compete with near-range strong clutter. Another consequence of the range ambiguity is that the sample support for estimating the array covariance matrix is reduced, leading to degraded performance. It is shown that space-time adaptive processing (STAP) techniques are required to reject the clutter in HPRF radar. Four STAP methods are studied in the context of the HPRF radar problem: low rank approximation sample matrix inversion (SMI), diagonally loaded SMI, eigencanceler, and element-space post-Doppler. These three methods are evaluated in typical HPRF radar scenarios and for various training conditions, including when the target is present in the training data     

New maximum entropy spectrum using uncertain eigenstructureconstraints

A number of modern spectral estimators are shown to have a common generic formulation. These include minimum variance, MUSIC, and maximum entropy. A new maximum entropy spectral estimator is derived using constraints on the modal powers or the expected-square projections of the data onto the eigenvectors of the data covariance matrix. The formulation incorporates uncertainty in the modal power constraints and the signal-versus-noise subspace separation. The resulting estimators have forms which incorporate all other modern estimators, including maximum entropy and minimum norm. The new estimators allow further development when a priori information is used in the constraints. Comparison of one version of the estimator with the minimum norm verifies the greater probability of resolution of the minimum norm but indicates in some instances the value of the incorporated uncertainties. Another version uses complex constraints and reduces to conventional maximum entropy or minimum norm under certain conditions     

Some limitations on the effectiveness of airborne adaptive radar

It is shown that internal clutter motion, aircraft crabbing, scattering from near-field obstacles, and channel mismatch can limit the effectiveness of space-time processing in eliminating airborne clutter. An analytical expression is developed to show how each of these effects produces a deterioration in the signal-to-clutter-plus-noise ratio achievable. By studying the spectral decomposition of the covariance matrix, it is found that the effects of both internal clutter motion and crabbing can either be compensated by artificially adding noise or by processing more pulses. A near-field obstacle produces a spread of the clutter into all of sine azimuth-Doppler space. It is shown that the space-time processor attempts to compensate for this effect by placing a near-field null on the obstacle. Thus, adding more elements is much more effective in eliminating this effect than is processing more pulses. Channel mismatch can be alleviated by controlling the dispersive errors more tightly and by increasing the number of receive elements     

DOA Estimation for Uniform Linear Array with Mutual Coupling

An algorithm is presented for direction-of-arrival (DOA) estimation in the presence of unknown mutual coupling based on the generalized eigenvalues utilizing signal subspace eigenvectors (GEESE) algorithm for uniform linear array (ULA). It is not an iterative algorithm, and a spectral peak search is not required. The DOA can be accurately estimated without any calibration sources since the effects of mutual coupling can be eliminated by the inherent mechanism of the proposed algorithm. An algorithm for estimating the mutual coupling coefficients is also proposed. Simulation results demonstrate the effectiveness of the proposed algorithms.     

利用分数低阶空时矩阵进行冲击噪声环境下的DOA估计

研究冲击噪声环境下的信号DOA估计问题。在对称α稳定(SαS: Symmetric α-stable)分布冲击噪声假设下,定义了一个阵列接收数据的广义分数低阶空时矩阵。理论分析表明,对广义分数低阶空时矩阵进行奇异值分解可获得噪声子空间估计。与信号空间DOA估计技术相结合,提出一种新的基于信号空间分解的DOA估计算法。该算法在低信噪比下对强冲击噪声具有更好的抑制作用。计算机仿真证明了算法的有效性。     

Optimal and adaptive reduced-rank STAP

This paper is concerned with issues and techniques associated with the development of both optimal and adaptive (data dependent) reduced-rank signal processing architectures. Adaptive algorithms for 1D beamforming, 2D space-time adaptive processing (STAP), and 3D STAP for joint hot and cold clutter mitigation are surveyed. The following concepts are then introduced for the first time (other than workshop and conference records) and evaluated in a signal-dependent versus signal independent context: (1) the adaptive processing “region-of-convergence” as a function of sample support and rank, (2) a new variant of the cross-spectral metric (CSM) that retains dominant mode estimation in the direct-form processor (DFP) structure, and (3) the robustness of the proposed methods to the subspace “leakage” problem arising in many real-world applications. A comprehensive performance comparison is conducted both analytically and via Monte Carlo simulation which clearly demonstrates the superior theoretical compression performance of signal-dependent rank-reduction, its broader region-of-convergence, and its inherent robustness to subspace leakage     

基于迭代QR分解的DOA估计技术

提出了一种基于迭代QR分解的信源到达角(DOA)估计技术.DOA估计的子空间方法主要是通过估计信号协方差矩阵的信号子空间或者噪声子空间来求出信号的DOA参数.估计这些子空间通常需要大量的计算,采用ASIC实现时其成本会非常昂贵.本文采用迭代QR分解方法进行子空间分解,可以利用较少量的计算资源完成处理任务.仿真实验结果达到0.23毫弧度,说明该算法比较可靠有效.     

Reduced-rank STAP performance analysis

The space-time radar problem is well suited to the application of techniques that take advantage of the low-rank property of the space-time covariance matrix. It is shown that reduced-rank (RR) methods outperform full-rank space-time adaptive processing (STAP) when the space-time covariance matrix is estimated from a data set with limited support. The utility of RR methods is demonstrated by theoretical analysis, simulations and analysis of real data. It is shown that RR processing has two opposite effects on the performance: increased statistical stability which tends to improve performance, and introduction of a bias which lowers the signal-to-noise ratio (SNR). A method for evaluating the theoretical conditioned SNR for fixed RR transforms is also presented. It Is shown that while best performance is obtained using data-dependent transforms, the loss incurred by the application of fixed transforms (such as the discrete cosine transform) may be relatively small. The main advantage of fixed transforms is the availability of efficient computational procedures for their implementation. These findings suggest that RR methods could facilitate the development of practical, real-time STAP technology     

On Optimizing Array Reception of Multipath

This paper reviews system configuration requirements and analyzes detectability performance characteristics for maximum likelihood array reception of multipath. Performance is analyzed to determine the effects of channel multipath structure (multipath delay and signal power division among the paths), space-time correlation properties of the incident processes, and the array spacing. It is shown by a series of case studies, that for single element coupling, as well as array coupling, an increased multipath delay factor results in decreased system detectability for fixed signal and noise intensity levels. The performance capacity is degraded as the available signal power tends to distribute more uniformly between the paths. These effects are attributed to the loss of effective signal energy concentration, resulting in a lower effective pre-detection signal-to-noise ratio. An investigation of the effects upon system performance, due to array element spacing, shows that performance is enhanced by increasing the spacing relative to the multipath delay factor and the reciprocal signal bandwidth. The former is the result of a more directive detectability (beam) pattern arising from the increased spacing. In effect, with increased spacing, the main lobe of the pattern is narrowed, while the side lobes are optimally suppressed by the required noise related array element link, frequency filters (weights).     

Median cascaded canceller for robust adaptive array processing

A median cascaded canceller (MCC) is introduced as a robust multichannel adaptive array processor. Compared with sample matrix inversion (SMI) methods, it is shown to significantly reduce the deleterious effects of impulsive noise spikes (outliers) on convergence performance of metrics; such as (normalized) output residue power and signal to interference-plus-noise ratio (SINR). For the case of no outliers, the MCC convergence performance remains commensurate with SMI methods for several practical interference scenarios. It is shown that the MCC offers natural protection against desired signal (target) cancellation when weight training data contains strong target components. In addition, results are shown for a high-fidelity, simulated, barrage jamming and nonhomogenous clutter environment. Here the MCC is used in a space-time adaptive processing (STAP) configuration for airborne radar interference mitigation. Results indicate the MCC produces a marked SINR performance improvement over SMI methods.     

基于恒模特性的QPSK-DSSS信号扩频序列估计

 针对非合作低信噪比条件下的QPSK-DSSS信号,提出一种基于恒模特性的扩频序列估计算法。该算法首先将单用户QPSK-DSSS信号等效为两用户的BPSK-DSSS信号,其次通过对信号协方差矩阵进行特征分解,估计出由同相与正交两路扩频序列张成的二维信号子空间,最后利用扩频序列的恒模特性消除特征分解带来的酉阵模糊问题,实现扩频序列的精确估计。本文提出算法实现简单,相对目前现有传统算法具有较低的计算复杂度,而且计算机仿真表明:本文提出算法在低信噪比条件下具有优良的估计性能。     

回溯降维相干分布式非圆信号DOA快速估计

在相干分布式非圆(CDNC)信号波达方向(DOA)估计中,针对阵列输出矩阵扩展后维数增加带来的较大运算量问题,基于降维的多级维纳滤波(MSWF)技术,引入回溯优化思想,提出了一种快速估计算法。该算法首先利用信号非圆特性扩展阵列输出矩阵,然后通过MSWF递推分解快速求出信号子空间,避免了计算阵列协方差矩阵及特征分解,并且在递推过程中引入回溯优化机制提高了各级匹配滤波器的估计性能,最后由最小二乘(LS)或者总体最小二乘(TLS)得到DOA估计。仿真分析表明,所提算法与相干分布式非圆信号旋转不变子空间算法(CDNC-ESPRIT)性能相当,但复杂度得到了大幅度降低,相比于基于MSWF的非圆信号快速子空间(NC-MSWF-FS)算法,在较小的复杂度代价下大幅度提升了低信噪比时的估计性能,并且对初始参考信号的选取具有了较强的鲁棒性。