Showing papers by "Mats Viberg published in 1989"

Analysis of subspace fitting based methods for sensor array processing

Abstract: The problem of estimating signal parameters from sensor array measurements is addressed. A general multidimensional signal subspace method, called the weighted subspace fitting (WSF) method, is proposed. The relationship of WSF to other signal subspace methods as well as the relation to the deterministic maximum-likelihood (ML) method is discussed. The asymptotic properties of WSF are presented for a general weighting. This result includes the properties of ML as a special case. The weighting that minimizes the estimation error covariance is given, resulting in a method that always outperforms ML. A numerical example is presented, demonstrating that the optimally weighted WSF method can give notably lower variance for highly correlated signals. Simulations are included to substantiate the analysis. >

Stochastic maximum likelihood estimation in sensor arrays by weighted subspace fitting

Abstract: The problem of estimating parameters of multiple narrowband emitter signals from sensor array data is considered. Under the assumption of Gaussian distributed emitter signals, the stochastic maximu ...

Sensor array processing using gated signals

Abstract: The author considers the problem of using an array of sensors for separating a desired signal from unwanted disturbance signals. The desired signal is assumed to be gated, either in time or in frequency. He derives an eigenstructure based method which requires no knowledge of the array manifold and which allows for coherent signals. >

Direction-of-arrival estimation and detection using weighted subspace fitting

Abstract: AbstmclThe problem of estimating tlie dircetioes of arrival of n&ow band signals, impinging on an array of sensors is addressed. An estimation procedure. which nw plies to arbitrary nrray structures and signnl correlation i? ptopo*cecl.. The method ia bared on thc rrcvntly iutrodnccd Weighted Subspace Fitti+ (WSF) crit.crios. A Gauss-Newlon type algorithm is suggested for minimizing the WSF criterion. A new doteelion scheme ia "1.0 formtiInted, bmod on tho nsymptntic distribution of tho WSF cost function: Siniulntioiis are presented, demonstrating the improv&inent of tho proposed WSF bnsed approaches, over similar tcchniqii6s hnscd on deterministic Mnximoni Likelihood (ML) .

ESPRIT And Uniform Linear Arrays

Abstract: A¢â‚¬â€?ESPRIT is a recently developed and patented technique for high-resolution estimation of signal parameters. It exploits an invariance structure designed into the sensor array to achieve a reduction in computational requirements of many orders of magnitude over previous techniques such as MUSIC, Burg's MEM, and Capon's ML, and in addition achieves performance improvement as measured by parameter estimate error variance. It is also manifestly more robust with respect to sensor errors (e.g. gain, phase, and location errors) than other methods as well. Whereas ESPRIT only requires that the sensor array possess a single invariance best visualized by considering two identical but other-wise arbitrary arrays of sensors displaced (but not rotated) with respect to each other, many arrays currently in use in various applications are uniform linear arrays of identical sensor elements. Phased array radars are commonplace in high-resolution direction finding systems, and uniform tapped delay lines (i.e., constant rate A/D converters) are the rule rather than the exception in digital signal processing systems. Such arrays possess many invariances, and are amenable to other types of analysis, which is one of the main reasons such structures are so prevalent. Recent developments in high-resolution algorithms of the signal/noise subspace genre including total least squares (TLS) ESPRIT applied to uniform linear arrays are summarized. ESPRIT is also shown to be a generalization of the root-MUSIC algorithm (applicable only to the case of uniform linear arrays of omni-directional sensors and unimodular cisoids). Comparisons with various estimator bounds, including CramerRao bounds, are presented.

Asymptotic Analysis Of The Total Least Squares ESPRIT Algorithm

Abstract: This paper considers the problem of estimating the parameters of multiple narrowband signals arriving at an array of sensors. Modern approaches to this problem often involve costly procedures for calculating the estimates. The ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) algorithm was recently proposed as a means for obtaining accurate estimates without requiring a costly search of the parameter space. This method utilizes an array invariance to arrive at a computationally efficient multidimensional estimation procedure. Herein, the asymptotic distribution of the estimation error is derived for the Total Least Squares (TLS) version of ESPRIT. The Cramer-Rao Bound (CRB) for the ESPRIT problem formulation is also derived and found to coincide with the variance of the asymptotic distribution through numerical examples. The method is also compared to least squares ESPRIT and MUSIC as well as to the CRB for a calibrated array. Simulations indicate that the theoretic expressions can be used to accurately predict the performance of the algorithm.