Showing papers by "Mats Viberg published in 1993"

Exact and Large Sample ML Techniques for Parameter Estimation and Detection in Array Processing

Abstract: Sensor array signal processing deals with the problem of extracting information from a collection of measurements obtained from sensors distributed in space. The number of signals present is assumed to be finite, and each signal is parameterized by a finite number of parameters. Based on measurements of the array output, the objective is to estimate the signals and their parameters. This research area has attracted considerable interest for several years. A vast number of algorithms has appeared in the literature for estimating unknown signal parameters from the measured output of a sensor array.

Subspace fitting with diversely polarized antenna arrays

Abstract: Diversely polarized antenna arrays are widely used in RF applications The diversity of response provided by diversely polarized antenna arrays can greatly improve direction-finding performance over arrays sensitive to only one polarization component For d emitters, directly implementing a multidimensional estimation algorithm would require a search for 3d parameters: d directions of arrival (DOAs), and 2d polarization parameters A more efficient solution is presented based on the noise subspace fitting (NSF) algorithm It is shown how to decouple the NSF search into a two-step procedure, where the DOAs are estimated separately The polarization parameters are then obtained by solving a linear system of equations The advantage of this approach is that the search dimension is reduced by a factor of three, and no initial polarization estimate is required The algorithm can be shown to yield asymptotically minimum variance estimates: provided no perfectly coherent signals are present Simulation examples are included >

Blind estimation of multiple co-channel digital signals arriving at an antenna array

Abstract: We propose a novel approach for separating and estimating multiple co-channel digital signals arriving at an antenna array. The spatial response of the antenna array is known imprecisely or unknown. We exploit the discrete-alphabet property of digital signals to simultaneously determine the array response and the bit sequence for each signal. Uniqueness of the estimates is established for signals with BPSK modulation format. This new approach as applicable to an unknown array geometry and propagation environment, which is particularly useful in digital mobile communications. Simulation results demonstrate its promising performance. >

Analysis of the combined effects of finite samples and model errors on array processing performance

Abstract: The authors consider the performance of the class of signal subspace fitting algorithms for signal parameter estimation using narrowband sensor array data. The principle sources of estimation error in such applications are the finite sample effects of additive noise and imprecise models for the antenna array and spatial noise statistics. The covariance matrix of the estimation error when all of these error sources are present is found to be the sum of the individual contributions from each component separately. This simplifying fact allows for the derivation of an overall optimal subspace weighting for a particular array and noise covariance error model. In fact, the resulting algorithm yields the lowest possible asymptotic estimation error variance of any method for the model in question. >

Performance of Subspace-Based System Identification Methods

Abstract: Traditional prediction-error techniques for multivariable system identification require canonical descriptions using a large number of parameters. This problem can be avoided using subspace based m ...

An efficient non-iterative method for estimating the angles of arrival of known signals

Abstract: The vast majority of existing high resolution angle of arrival (AOA) estimators are designed for the case of completely unknown signal waveforms. However, in many applications, such as mobile communications, the receiver has access to the structure of the incoming signals. By exploiting this extra information, a considerable improvement in estimation accuracy and/or computational complexity can be achieved. Herein, we propose a simple two-step procedure for the case of perfectly known waveforms (up to gain and phase). Despite its low complexity, the method can operate in the presence of arbitrary noise fields including interfering signals. Furthermore, if the signals of interest are uncorrelated, the proposed technique yields statistically efficient AOA estimates. >

Efficient subspace fitting algorithms for diversely polarized arrays

Abstract: The authors present a statistically efficient algorithm for direction of arrival (DOA) estimation using diversely polarized arrays. The proposed technique requires a multidimensional search over the DOA parameters only, after which the polarization parameters can be calculated explicitly. A comparison with a multiple signal classification (MUSIC) based approach revealed that the method may give significantly improved performance, particularly in scenarios involving highly correlated signals. >