Showing papers by "Mats Viberg published in 1992"
TL;DR: It is shown that the multidimensional signal subspace method, termed weighted subspace fitting (WSF), is asymptotically efficient, which results in a novel, compact matrix expression for the Cramer-Rao bound (CRB) on the estimation error variance.
Abstract: It is shown that the multidimensional signal subspace method, termed weighted subspace fitting (WSF), is asymptotically efficient. This results in a novel, compact matrix expression for the Cramer-Rao bound (CRB) on the estimation error variance. The asymptotic analysis of the maximum likelihood (ML) and WSF methods is extended to deterministic emitter signals. The asymptotic properties of the estimates for this case are shown to be identical to the Gaussian emitter signal case, i.e. independent of the actual signal waveforms. Conclusions concerning the modeling aspect of the sensor array problem are drawn. >
211 citations
23 Mar 1992
TL;DR: A novel instrumental variable (IV) approach to the sensor array problem is proposed, by exploiting temporal correlatedness of the source signals, and knowledge of the spatial noise covariance is not required.
Abstract: Signal parameter estimation from sensor array data is of great interest in a variety of applications, including radar, sonar, and radio communication. A large number of high-resolution (i.e., model-based) techniques have been suggested in the literature. The vast majority of these require knowledge of the spatial noise correlation matrix, which constitutes a significant drawback. A novel instrumental variable (IV) approach to the sensor array problem is proposed. By exploiting temporal correlatedness of the source signals, knowledge of the spatial noise covariance is not required. The asymptotic properties of the IV estimator are examined, and an optimal IV method is derived. Simulations are presented examining the properties of the IV estimators in data segments of realistic lengths. >
11 citations
23 Mar 1992
TL;DR: A paradigm for generating an array model from noise corrupted calibration vectors is developed and the key idea is to use a local parametric model of the sensor responses.
Abstract: Many practical applications of signal processing require accurate determination of signal parameters from sensor array measurements. Most estimation techniques are sensitive to errors in the array response model. Thus, reliable array calibration schemes are of great importance. A paradigm for generating an array model from noise corrupted calibration vectors is developed. The key idea is to use a local parametric model of the sensor responses. The potential improvement using the suggested scheme is demonstrated on real data collected from a full-scale hydroacoustic array. >
5 citations
TL;DR: In this article, the effect of such model errors on parametric methods is examined and the spatial correlation structure of the background noise (i.e., the correlation from sensor to sensor) is known to within a multiplicative scalar.
4 citations
26 Oct 1992
TL;DR: An improved technique for direction-of-arrival estimation of temporally correlated signals in the presence of spatially colored, but temporally uncorrelated, noise is presented and nearly achieves the deterministic Cramer-Rao bound.
Abstract: An improved technique for direction-of-arrival estimation of temporally correlated signals in the presence of spatially colored, but temporally uncorrelated, noise is presented. The method is particularly suited to applications in which the receiver bandwidth exceeds that of the emitter signals. A statistical performance analysis shows that the method nearly achieves the deterministic Cramer-Rao bound if the signals are sufficiently predictable. A Monte Carlo experiment suggests that the theoretical estimation error variance well predicts the empirical mean square error down to the threshold region. >
2 citations
01 Jan 1992
TL;DR: A paradigm for generating an array model from noise corrupted calibration vectors is developed and the key idea is to use a local parametric model of the sensor responses to generate the socalled array manifold from a finite collection of calibration vectors.
Abstract: In many signal processing applications high accuracy signal parameter estimation from sensor array data is a significant problem Much of the recent work in array processing has focussed on methods for high-resolution location estimation Model based estimation techniques require accurate knowledge of the so-called array manifold In practice, the array response is often determined by measuring the array response when only one emitter is radiating and the signal parameters of which are allowed to vary in a known way This paper addresses some of the practical issues that arise in generating the socalled array manifold from a finite collection of calibration vectors For high-resolution signal parameter estimation techniques to be successful, the interpolated array manifold has to satisfy certain smoothness conditions A paradigm for generating an array model from noise corrupted calibration vectors is developed The key idea is to use a local parametric model of the sensor responses The potential improvement using the suggested scheme rather than an ideal array model is demonstrated on real data collected from a full-scale hydro-acoustic array