Decentralized array processing using the MODE algorithm

Centralized methods for source location using sensor arrays have computational and communication burdens that increase significantly with the number of sensors in the array. Therefore, these methods may not be usable in the applications involving very large arrays. In such applications, the data processing may need to be decentralized. This paper introduces two methods for decentralized array processing, based on the recently proposed MODE algorithm. For prescribed nonoverlapping subarrays, both methods are shown to be statistically optimal in the sense that asymptotically they provide the most accurate decentralized estimates of source location parameters. The problem of subarray selection to further optimize the estimation accuracy is only briefly addressed. The two methods are intended for different types of applications: the first should be preferred when there exist significant possibilities for local processing or for parallel computation in the central processor; otherwise the second method should be preferred. The accuracy of the two decentralized methods is compared to the centralized Cramér-Rao bound, both analytically and numerically, in order to provide indications about the loss of accuracy associated with decentralized processing.The work of P. Stoica was supported by a grant from the Royal Swedish Academy of Engineering Sciences and by the Swedish Research Council for Engineering Sciences under contract 91-676. The work of A. Nehorai was supported by the Air Force Office of Scientific Research under grant no. AFOSR-90-0164, and by the Office of Naval Research under grant no. N00014-91-J-1298.on leave at the Department of Applied Electronics, Chalmens University of Technology, 2-41296 Göthensberg, Sweeden.     

Study of Capon method for array signal processing

The mean-square error (MSE) of Capon estimate of the directions-of-arrival (DOA) is established in the narrowband array processing case. An improved Capon-like DOA estimator is proposed and its MSE is studied as well. Performance comparisons between the standard and improved Capon DOA estimates, and between these two estimates and the linear prediction DOA estimate, are performed. It is concluded that the improved Capon-like method introduced in this paper provides more accurate DOA estimates in most cases.This work has been supported by the Swedish Research Council for Engineering Sciences under contract 91–676.     

MODE-type algorithm for estimating damped, undamped, or explosive modes

We propose a new algorithm for estimating the parameters of damped, undamped, or explosive sinusoidal signals. The algorithm resembles the MODE algorithm, which is commonly used for direction of arrival estimation in the array signal processing field. It is derived as a natural approximation to an asymptotically (high-SNR) optimal parameter estimator and has excellent statistical accuracy. Nevertheless, it is computationally simple and easy to implement. Numerical examples are included to illustrate the performance of the proposed method.This work has been supported by the Swedish Research Council for Engineering Sciences (TFR).     

Model validation and model structure determination

This paper gives a tutorial overview of basic approaches for model validation and model structure determination.Work partially supported by the Swedish Research Council for Engineering Sciences under contract 98-654.     

Large-sample analysis of MUSIC and Min-Norm direction estimators in the presence of model errors

We analyze the large-sample mean square error (MSE) of MUSIC and Min-Norm direction-of-arrival (DOA) estimators under fairly general conditions, including mismodelling of the array response and the noise covariance. We separate the contributions to the MSE into a bias part caused by modelling errors and a variance part caused by finite (yet large) sample effects. The bias is simply evaluated by comparing the limiting estimate (corresponding to an infinite number of snapshots) with the true DOAs (which are known to the analyzer). To simplify the variance derivation we assume that the snapshots are complex i.i.d. Gaussian vectors and that the largest eigenvalues of their covariance matrix are distinct, but,otherwise, make none of the assumptions commonly used in previous analyses; in particular we do not constrain the snapshots to satisfy any model equation. The theoretical results obtained are illustrated by means of numerical examples using various modelling errors.The work of Petre Stoica was supported by the Swedish Research Council of Engineering Sciences under contract 91-676.     

Instrumental variable methods for system identification

This paper gives a tutorial overview of instrumental variable methods. Comparisons are made to the least-squares method. An analysis including consistency and asymptotic distribution of the parameter estimates is included.Work supported in part by Swedish Research Council for Engineering Sciences under contract 95-800, and the Foundation for Strategic Research.     

Blind DOA and polarization estimation for polarization-sensitive array using dimension reduction MUSIC

A novel blind direction-of-arrival (DOA) and polarization estimation algorithm for polarization-sensitive uniform linear array using dimension reduction multiple signal classification (MUSIC) is proposed in this paper. The proposed algorithm utilizes the signal subspace to obtain an initial estimation of DOA, then estimates more accurate DOA through a one-dimensional (1-D) local searching according to the initial estimation of DOA, and finally obtains polarization parameter estimation via the estimated polarization steering vectors. The proposed algorithm, which only requires a one-dimension local searching, can avoid the high computational cost within multi-dimensional MUSIC algorithm. The simulation results reveal that the proposed algorithm has better DOA and polarization estimation performance than both estimation of signal parameters via rotational invariance technique algorithm and trilinear decomposition algorithm. Furthermore, the proposed algorithm can be suitable for irregular array geometry, obtain automatically paired multi-dimensional parameter estimation, and avoid multi-dimensional searching. Simulation results verify the effectiveness of the proposed algorithm.     

A comprehensive approach to DOA estimation using higher-order statistics

The algorithms developed in this paper rely on cumulants, or higher-order statistics, to eliminate Gaussian noise. A simple relation between the signal model parameters and the cumulant matrices computed from the received signals forms the basis for the development of these algorithms. The block Hankel structure of matrices formed from the cumulant matrices enables the directions of arrival (DOAs) to be estimated using the well-known MUSIC and ESPRIT approaches to DOA estimation. The effectiveness of the methods proposed here is demonstrated by the results of extensive computer simulation studies.This work was supported by the National Sciences and Engineering Research Council of Canada.     

A Decentralized Architecture for Simultaneous Localization and Mapping

In this paper, a decentralized data fusion algorithm is presented for simultaneous position estimation of a land vehicle and building the map of the environment. Two independent loops, one incorporating inertial sensor and GPS data, and one fusing the laser data and the readings of the wheel and steering encoders, are considered. The information obtained from the sensors is first synchronized and then communicated to the other loop to enhance the quality of local loop estimates. The real data obtained from an experiment are used in implementing the algorithm and the information form of the Kalman filter is used as the main tool for the decentralized data fusion. It is shown that the algorithm leads to more accurate estimates as compared to the local loop estimates, and can perform properly even in the case of GPS masking.      

基于Screened Ratio原理的冲击噪声环境下DOA估计算法

该文提出了一种冲击噪声环境中DOA估计的算法。算法首先根据Screened ratio原理构造阵列信号的相关矩阵,然后利用MUSIC算法实现DOA估计。与基于分数低阶矩(FLOM)的算法相比,该文算法不需要选择FLOM参数p。计算机仿真表明该文算法在冲击噪声环境下具有更佳的稳定性和估计精度。     

Direction finding using data-supported optimization

A novel data-supported optimization technique for maximum likelihood (ML) direction-of-arrival estimation is proposed. The essence of our approach is to optimize the likelihood function at certaindata-supported points obtained by a resampled root-MUSIC procedure. These points are shown to comprise a small but representative subset of all possible searching points and contain enough information for solving the ML problem.This work was supported in part by grants from the National Sciences and Engineering Research Council of Canada (NSERC), Ministry of Energy, Science and Technology (MEST) of Ontario, Communications and Information Technology Ontario (CITO), and by the Senior Individual Grant Program of the Swedish Foundation for Strategic Research.     

遗传算法多极值函数优化及其在MUSIC算法中的应用

本文就遗传算法解决多极值函数优化问题的方法进行了分析总结,提出了一种基于多生境排挤机制的改进算法,并将其应用到ＭＵＳＩＣ谱的谱峰搜索过程中。实验证明,改进后的方法较原算法计算效率更高,能够在一次搜索过程中同时确定目标函数多个极值点的位置。对于ＭＵＳＩＣ谱谱峰搜索问题,计算量较等步长遍历搜索节省在两个数量级以上．     

基于稀疏互质电磁矢量阵列的MUSIC算法

为增加传统MUSIC算法可分辨的信号源数,该文构造了一对具有互质关系的2维稀疏电磁矢量阵列,并基于此阵列提出了一种基于3维平滑的MUSIC算法。该算法利用两个阵列间的互质关系形成具有更多自由度的互质差合成阵列,并基于3维(2维空域加极化域)平滑算法恢复其自相关矩阵的秩,达到应用于传统MUSIC算法的目的。该算法的最大优势是仅使用二阶统计量即可系统地增加了原阵列的自由度。计算机仿真结果表明所提算法能估计多于物理阵元数的信号且分辨率高。     

On the concentrated stochastic likelihood function in array signal processing

The stochastic likelihood function [(STO)LF] associated with the narrowband signal processing problem can be concentrated with respect to the signal covariance matrix elements and the noise power. Although this is a known fact, no clear-cut derivation of the concentrated (STO)LF appears to be available in the literature. In this short paper we provide a simple, complete proof of the concentrated (STO)LF formula.The work of A. Nehorai was supported by the Air Force Office of Scientific Research under Grant no. F49620-93-1-0096, the Office of Naval Research under Grant no. N00014-91-J-1298, and the National Science Foundation under grant no. MIP-9122753. The work of P. Stoica was supported by the Swedish Research Council for Engineering Sciences under contract no. 93-669.     

A study of password security

We prove relationships between the security of a function generator when used in an encryption scheme and the security of a function generator when used in a UNIX-like password scheme.On leave of absence from the Computer Science Department, University of Toronto. Research partially supported by the Canadian Natural Sciences and Engineering Research Council Operating Grant A8092 and by a University of Toronto research grant.Research partially supported by the Canadian Natural Sciences and Engineering Research Council Operating Grant A3611.     

Capon estimation of covariance sequences

Estimating the covariance sequence of a wide-sense stationary process is of fundamental importance in digital signal processing (DSP). A new method, which makes use of Fourier inversion of the Capon spectral estimates and is referred to as theCapon method, is presented in this paper. It is shown that the Capon power spectral density (PSD) estimator yields an equivalent autoregressive (AR) or autoregressive moving-average (ARMA) process; hence, theexact covariance sequence corresponsing to the Capon spectrum can be computed in a rather convenient way. Also, without much accuracy loss, the computation can be significantly reduced via an approximate Capon method that utilizes the fast Fourier transform (FFT). Using a variety of ARMA signals, we show that Capon covariance estimates are generally better than standard sample covariance estimates and can be used to improve performances in DSP applications that are critically dependent on the accuracy of the covariance sequence estimates.This work was supported in part by National Science Foundation Grant MIP-9308302, Advanced Research Project Agency Grant MDA-972-93-1-0015, the Senior Individual Grant Program of the Swedish Foundation for Strategic Research and the Swedish Research Council for Engineering Sciences (TFR).     

L型阵列天线的测频测角实现研究

基于构建的FPGA协同DSP(FPGA+DSP)异构系统提出了一种L型阵列天线的测频和测角实现方案,研究了谱峰搜索多重信号分类(MUSIC)算法和酉矩阵求根MUSIC算法的实现。2种算法的实测数据试验结果表明,谱峰搜索MUSIC算法实现简单,但处理精度受限于频域和空域的搜索精度。在同一精度量级时,酉矩阵求根MUSIC算法的处理时间只有谱峰搜索MUSIC算法的62.6%,且可以避免多维搜索,还对宽带信号具有一定的分辨率,具有较好的工程鲁棒性。     

Novel angle estimation for bistatic MIMO radar using an improved MUSIC

In this article, we study the problem of angle estimation for bistatic multiple-input multiple-output (MIMO) radar and propose an improved multiple signal classification (MUSIC) algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation. The proposed algorithm obtains initial estimations of angles obtained from the signal subspace and uses the local one-dimensional peak searches to achieve the joint estimations of DOD and DOA. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, and is almost the same as that of two-dimensional MUSIC. Furthermore, the proposed algorithm can be suitable for irregular array geometry, obtain automatically paired DOD and DOA estimations, and avoid two-dimensional peak searching. The simulation results verify the effectiveness and improvement of the algorithm.     

声矢量阵张量分解MUSIC算法

矢量水听器同时、共点测量声场中的声压和振速分量,因此相对于声压水听器能够获取更多的声场信息,多重信号分类算法(MUSIC)是一种具有高分辩能力的方位估计算法,本文对声矢量阵接收信号三阶张量建模,并通过高阶奇异值分解得到信号张量子空间,从而结合MUSIC算法对声源进行方位估计。基于三阶张量奇异值分解得到的信号子空间相比于传统的矩阵奇异值分解得到的信号子空间能够更好地抑制噪声,并且体现了多维数据之间的关联关系,因此方位估计精度更高。计算机仿真结果表明:矢量阵张量分解MUSIC算法性能优于传统矢量阵MUSIC方法。      

基于NC-MUSIC算法的均匀圆阵DOA估计及性能分析

近年来基于非圆信号的DOA估计算法由于其优良的估计性能,受到越来越多的关注。在接收阵列为均匀圆阵的情况下,对入射信号进行方位角和俯仰角的联合估计。依据非圆信号的DOA估计数学模型及阵列模型,采用NC-MUSIC算法完成对均匀圆阵方位角和俯仰角的联合估计。通过计算机仿真,得出该算法对均匀圆阵方位角和俯仰角的估计是比较准确的,并且通过NC-MUSIC与MUSIC算法仿真性能的分析比较,得出在接收阵列为均匀圆阵的情况下,NC-MUSIC算法也优于MUSIC。