基于协方差矩阵双层重构的稳健自适应单脉冲测角

常规自适应单脉冲方法在主瓣干扰下会引起自适应波束形成性能恶化并导致其单脉冲比曲线严重失真,影响被动雷达的测角精度与跟踪性能。针对此问题,提出了一种适用于平面阵的基于协方差矩阵双层重构的稳健自适应单脉冲测角方法。首先,利用Capon功率谱通过稀疏重构法初步估计出干扰加噪声协方差矩阵,通过干扰导向矢量估计完成对协方差矩阵的优化校正以提高自适应波束形成性能;然后,基于线性约束最小方差(Linearly Constrained Minimum Variance, LCMV)准则对方位角和俯仰角进行联合线性约束以避免单脉冲比曲线在主瓣干扰下严重失真;最后,根据自适应单脉冲比值求出目标与波束指向之间的偏转角以实现目标测角。与常规方法相比,所提方法在干扰抑制能力与测角精度上都有显著提升。     

Recursive least squares estimation for run-to-run control with metrology delay and its application to STI etch process

Run-to-run (RtR) control technology has received tremendous interest in semiconductor manufacturing. Exponentially weighted moving average (EWMA), double-EWMA, and internal model control (IMC) filters are recognized methods for online RtR estimation. In this paper, we consider recursive least squares (RLS) as an alternative for online estimation and RtR control. The relationship between EWMA-type and RLS-type estimates is analyzed and verified with simulations. Because measurement delay is almost inevitable in semiconductor manufacturing, we discuss and compare the performance of EWMA, RtR-IMC, and RLS controllers in handling measurement delay and measurement noise for processes with a deterministic drift. An ad hoc solution is proposed to handle measurement delay for processes with time-varying drifts. The results are illustrated through several simulations and a shallow trench isolation (STI) etch process as an industrial example.     

基于协方差矩阵重构和导向矢量估计的稳健自适应波束形成

实际应用中, 当假定的与真实的期望信号导向矢量之间存在一定误差时, 波束形成器的性能会急剧下降, 特别是当期望信号功率很强的时候.为解决这个问题, 提出了一种新的算法.当信源数小于阵元数时, 干扰加噪声协方差矩阵具有稀疏性.新方法首先利用该特性重构干扰加噪声协方差矩阵并由此得到与干扰导向矢量正交的子空间, 使接收的数据通过该子空间得到只含有期望信号和噪声的混合信号, 然后,对该混合信号基于最大化输出功率原理估计期望信号导向矢量, 最后,把得到的导向矢量和正交子空间来构造阵列加权值.仿真结果表明:该算法分别在假定的期望信号导向矢量存在误差、期望信号很强和低快拍数时仍然具有良好的性能.     

Application of Toeplitz covariance estimation to adaptivebeamforming and detection

Array processing algorithms for adaptive beamforming and the adaptive detection of radar targets in unknown interference are proposed and simulated. These algorithms rely on estimates of the interference covariance which are constrained to be Toeplitz. It is shown that the incorporation of this constraint into the covariance estimation has a significant impact on the adaptive beamformer and adaptive detector performance     

The Empirical Likelihood method applied to covariance matrix estimation

This paper presents a new estimation scheme for signal processing problems in unknown noise field. The Empirical Likelihood has been introduced in the mathematical community, but, surprisingly, it is still unknown in the signal processing community. This estimation method is an alternative to estimate unknown parameters without using a model for the probability density function.The aim of this paper is twofold: first, the Empirical Likelihood theory is presented and revisited thanks to the moment method. Its properties are derived. Second, to emphasize all the potentiality of this method, we address the problem of Toeplitz matrix estimation: this leads us to obtain improved estimates in comparison to conventional ones, as shown in simulations.     

四阶累积量的递推估计及其应用

本文推导一种复数或实数零均值平稳随机过程的高阶累积量估计的递推算法,随机信号的“峰度”递推估计公式,在递推估计公式中引入了随时间变化的“修正”项,使得该递推算法还可适合非平稳过程高阶累积量的估计计算。并且,本文大量计算了多类型。多航速、多舰船辐射噪声的“峰度”值,分析了舰船辐射噪声特性。     

Minimax CFAR detection in additive Gaussian noise of unknown covariance (Corresp.)

By employing invariance theory and the Hunt-Stein theorem in particular, CFAR and minimax detectors are derived for a signal of unknown phase in the presence of additive Gaussian noise of unknown covariance.     

Recursive estimation of two-dimensional processes with application to image processing

A low order quarter-plane-causal recursive model is presented to represent the class of 2-D stationary Gaussian processes with power spectra matrices factorable into a quarter-plane-causal and anti-causal parts. This model is used to develop a technique for obtaining optimal 2-D recursive estimators. The approach taken here is similar to Attasi's [8], with no commutability condition imposed on the model. Circumventing this condition allows the modeling of the 2-D processes to be achieved with fewer parameters, and enables one to find the solutions to the problems of blur and color noise which are inherent in most image degraded images. Some simulated examples illustrate these points.This research was supported in part by the U.S. Army Research Grant DAAG29-79-C-0054 and the National Science Foundation Grant ECS-8011911.     

Convergence analysis of adaptive filtering algorithms with singulardata covariance matrix

The paper provides a rigorous analysis of the behavior of adaptive filtering algorithms when the covariance matrix of the filter input is singular. The analysis is done in the context of adaptive plant identification. The considered algorithms are LMS, RLS, sign (SA), and signed regressor (SRA) algorithms. Both the signal and weight behavior of the algorithms are considered. The signal behavior is evaluated in terms of the moments of the excess output error of the filter. The weight behavior is evaluated in terms of the moments of the filter weight misalignment vector. It is found that the RLS and SRA diverge when the input covariance matrix is singular. The steady-state signal behavior of the LMS and SA can be made arbitrarily fine by using sufficiently small step sizes of the algorithms. Indeed, the long-term average of the mean square excess error of the LMS is proportional to the algorithm step size. The long-term average of the mean absolute excess error of the SA is proportional to the square root of the algorithm step size. On the other hand, the steady-state weight behavior of both the LMS and SA have biases that depend on the weight initialization. The analytical results of the paper are supported by simulations     

Recursive computation of the stochastic matrix for digitized Rayleigh process

A first-order Markov chain model for digitized Rayleigh process is assumed. Elements of the stochastic matrix are computed by recursive method.     

Theory and application of covariance matrix tapers for robustadaptive beamforming

We unify several seemingly disparate approaches to robust adaptive beamforming through the introduction of the concept of a “covariance matrix taper (CMT)”. This is accomplished by recognizing that an important class of adapted pattern modification techniques are realized by the application of a conformal matrix “taper” to the original sample covariance matrix. From the Schur product theorem for positive (semi) definite matrices and Kolmogorov's existence theorem, we further establish that CMTs are, in fact, the solution to a minimum variance optimum beamformer associated with an auxiliary stochastic process that is related to the original by a Hadamard (Schur) product. This allows us to gain deeper insight into the design of both existing pattern modification techniques and new CMTs that can, for example, simultaneously address several different design constraints such as pattern distortion due to insufficient sample support and weights mismatch due to nonstationary interference. A new two-dimensional (2-D) CMT for space-time adaptive radar applications designed to provide more robust clutter cancellation is also introduced. Since the CMT approach only involves a single matrix Haddamard product, it is also inherently low complexity. The practical utility of the CMT approach is illustrated through its application to both spatial and spatio-temporal adaptive beamforming examples     

Robust spatial time-frequency distribution matrix estimation with application to direction-of-arrival estimation

We consider the problem of estimating spatial time-frequency distribution (STFD) matrices in the presence of impulsive noise. STFD matrices are widely used in sensor array processing for direction-of-arrival estimation and blind source separation of non-stationary sources. Conventional methods fail when the noise is non-Gaussian or impulsive. We propose robust techniques for STFD estimation which are based on pre-processing, robust position based estimation and robust covariance based estimation. The proposed methods are compared in terms of direction-of-arrival estimation performance.     

基于稀疏迭代协方差矩阵的谐波参数快速估计方法

针对稀疏迭代协方差估计(sparse iterative covariance-based estimation,SPICE)方法功率谱估计精度较低和计算复杂度较高的局限性,提出了一种基于稀疏迭代协方差矩阵的谐波信号功率谱和频率参数的快速估计方法.该方法主要结合渐近最小方差准则和快速傅里叶变换,对功率谱参数进行快速迭代...     

A maximal invariant framework for adaptive detection withstructured and unstructured covariance matrices

We introduce a framework for exploring array detection problems in a reduced dimensional space by exploiting the theory of invariance in hypothesis testing. This involves calculating a low-dimensional basis set of functions called the maximal invariant, the statistics of which are often tractable to obtain, thereby making analysis feasible and facilitating the search for tests with some optimality property. Using this approach, we obtain a locally most powerful invariant test for the unstructured covariance case and show that all invariant tests can be expressed in terms of the previously published Kelly's generalized likelihood ratio (GLRT) and Robey's adaptive matched filter (AMF) test statistics. Applying this framework to structured covariance matrices, corresponding to stochastic interferers in a known subspace, for which the GLRT is unavailable, we obtain the maximal invariant and propose several new invariant detectors that are shown to perform as well or better than existing ad-hoc detectors. These invariant tests are unaffected by most nuisance parameters, hence the variation in the level of performance is sharply reduced. This framework facilitates the search for such tests even when the usual GLRT is unavailable     

Performance of multiuser detection with adaptive channel estimation

An adaptive multipath decorrelating multiuser receiver is considered for application in Rayleigh fading multipath channels with significant Doppler spread. Coherent diversity combining is performed using adaptively obtained channel estimates in a manner that minimizes the impact of estimation errors on data detection. The bit-error rate of the receiver is evaluated analytically, showing dependence on the fading rate of the channel and tracking capabilities of adaptive least mean square and recursive least square algorithms, in addition to the order of multipath diversity and the number of active code-division multiple-access users     

Downlink channel covariance matrix (DCCM) estimation and itsapplications in wireless DS-CDMA systems

The downlink channel covariance matrix (DCCM) is of vital importance in determining downlink beamforming weights for base station (BS) antenna array systems. For the frequency-division-duplex (FDD) mode, DCCM is difficult to obtain due to a lack of direct measurement of downlink channel responses. In this paper, a novel technique is proposed for estimating DCCM using uplink channel responses only, which does not need direction-of-arrival (DOA) estimation and its association. The downlink beamforming scheme is then proposed for wireless DS-CDMA systems, using the obtained DCCM information together with the so-called virtual uplink beamforming and power control technique. Computer simulations show that using the BS antenna array together with this new beamforming technique can provide larger system capacity than traditional DOA-based approaches, which just direct the main beam toward the desired user     

On-line estimation of signal and noise parameters with application to adaptive Kalman filtering

This paper presents two algorithms for on-line estimation of the optimal gain of the Kalman filter applied to sensor signals when the signal-to-noise ratio is unknown. First-order spectra of a pure signal and colored measurement noise have been assumed. The proposed adaptive Kalman filtering algorithms have been tested for various spectra of the pure signal and noise, and for various signal-to-noise ratios. The effect of the length of an adaptation step and a sampling frequency on the mean square errors of the pure signal estimation has also been examined. Although the test have been performed for stationary signals, the algorithms presented can also be used successfully for time-varying sensor signals when the signal-to-noise ratios vary very slowly in comparison with the length of the adaptation step.The results are helpful for designers who synthesize optimal linear digital filters for sensor signals with first-order spectra and colored measurement noise. The estimation error curves presented enable designers to determine the noise reduction attainable for particular applications of the adaptive Kalman filtering algorithms.     

Recursive nonlinear estimation of a diffusion acting as the rate of an observed Poisson process

A conditional Poisson process is observed, whose rate is a diffusion process of known structure. The problem is to estimate the rate from the observed point process. Recursive equations are given for the conditional moment generating function and for an unnormalized conditional probability density of the rate. By studying these equations separately in between jumps and at the jumps, series expansions are obtained for these generating functions and densities in a number of examples that arise in applications to optical modulation and communications networks.     

Space-time coding ambiguities in joint adaptive channel estimation and detection

This paper studies the error propagation effect that is caused by certain ambiguities in joint data detection-channel tracking algorithms for transmission diversity schemes. Here, we use a space-time (ST) receiver based on the maximum a posteriori (MAP) method that takes into account the channel estimation error assuming the unknown channel to have a given complex multivariate Gaussian probability density function (pdf) (i.e., a Ricean channel). The decision criterion that is expressed in quadratic form represents either a linear detector or a noncoherent-nonlinear detector in extreme cases. Then, the channel pdf for the next iteration is updated by estimates of the second-order statistics of the channel coefficients, and a very simple decision-directed adaptive algorithm is derived for adaptive channel estimation. The adaptive algorithm can efficiently track a fast Rayleigh fading channel and, as a result, achieves robust performance. However, the occurrence of two types of ambiguities initiated in deep fades result in error propagation. Some remedies called space-time ambiguity remedies (STARs) are proposed to prevent error propagation. A new time-varying space-time coding (TVST) scheme is suggested as a bandwidth-efficient method to combat the permutation ambiguity impairment. This coding scheme, in conjunction with a differential detector, can resolve the ambiguity problem.