Optimum localization of multiple sources by passive arrays
01 Oct 1983-IEEE Transactions on Acoustics, Speech, and Signal Processing (IEEE)-Vol. 31, Iss: 5, pp 1210-1217
TL;DR: The maximum likelihood (ML) estimator of the location of multiple sources and the corresponding Cramer-Rao lower bound on the error covariance matrix are derived and Iterative algorithms for the actual computation of the ML estimator are presented.
Abstract: The maximum likelihood (ML) estimator of the location of multiple sources and the corresponding Cramer-Rao lower bound on the error covariance matrix are derived. The derivation is carried out for the general case of correlated sources so that multipath propagation is included as a special case. It is shown that the ML processor consists of a bank of beam-formers, each focused to a different source, followed by a variable matrix-filter that is controlled by the assumed location of the sources. In the special case of uncorrelated sources and very low signal-to-noise ratio this processor reduces to an aggregate of ML processors for a single source with each processor matched to a different source. Iterative algorithms for the actual computation of the ML estimator are also presented.
Citations
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01 Aug 1997
TL;DR: This paper provides a comprehensive and detailed treatment of different beam-forming schemes, adaptive algorithms to adjust the required weighting on antennas, direction-of-arrival estimation methods-including their performance comparison-and effects of errors on the performance of an array system, as well as schemes to alleviate them.
Abstract: Array processing involves manipulation of signals induced on various antenna elements. Its capabilities of steering nulls to reduce cochannel interferences and pointing independent beams toward various mobiles, as well as its ability to provide estimates of directions of radiating sources, make it attractive to a mobile communications system designer. Array processing is expected to play an important role in fulfilling the increased demands of various mobile communications services. Part I of this paper showed how an array could be utilized in different configurations to improve the performance of mobile communications systems, with references to various studies where feasibility of apt array system for mobile communications is considered. This paper provides a comprehensive and detailed treatment of different beam-forming schemes, adaptive algorithms to adjust the required weighting on antennas, direction-of-arrival estimation methods-including their performance comparison-and effects of errors on the performance of an array system, as well as schemes to alleviate them. This paper brings together almost all aspects of array signal processing.
2,169Â citations
TL;DR: In this paper, a method of constructing a single signal subspace for high-resolution estimation of the angles of arrival of multiple wide-band plane waves is presented, which relies on an approximately coherent combination of the spatial signal spaces of the temporally narrow-band decomposition of the received signal vector from an array of sensors.
Abstract: This paper presents a method of constructing a single signal subspace for high-resolution estimation of the angles of arrival of multiple wide-band plane waves. The technique relies on an approximately coherent combination of the spatial signal spaces of the temporally narrow-band decomposition of the received signal vector from an array of sensors. The algorithm is presented, and followed by statistical simulation examples. The performance of the technique is contrasted with other suggested methods and statistical bounds in terms of the determination of the correct number of sources (detection), bias, and variance of estimates of the angles.
1,067Â citations
TL;DR: Three noniterative techniques are presented for localizing a single source given a set of noisy range-difference measurements, and in one case the maximum likelihood bearing estimate is approached.
Abstract: Three noniterative techniques are presented for localizing a single source given a set of noisy range-difference measurements. The localization formulas are derived from linear least-squares "equation error" minimization, and in one case the maximum likelihood bearing estimate is approached. Geometric interpretations of the equation error norms minimized by the three methods are given, and the statistical performances of the three methods are compared via computer simulation.
724Â citations
01 Jan 2001
TL;DR: This chapter summarizes the current field and comments on the general merits and shortcomings of each genre, and presents a new localization method that is significantly more robust to acoustical conditions, particularly reverberation effects, than the traditional localization techniques in use today.
Abstract: Talker localization with microphone arrays has received significant attention lately as a means for the automated tracking of individuals in an enclosure and as a necessary component of any general purpose speech capture system. Several algorithmic approaches are available for speech source localization with multi-channel data. This chapter summarizes the current field and comments on the general merits and shortcomings of each genre. A new localization method is then presented in detail. By utilizing key features of existing methods, this new algorithm is shown to be significantly more robust to acoustical conditions, particularly reverberation effects, than the traditional localization techniques in use today.
649Â citations
TL;DR: In this paper, the eigenstructure of the covariance and spectral density matrices of the received signals is used for estimating the spatio-temporal spectrum of the signals received by a passive array.
Abstract: This paper presents new algorithms for estimating the spatio-temporal spectrum of the signals received by a passive array. The algorithms are based on the eigenstructure of the covariance and spectral density matrices of the received signals. They allow partial correlation between the sources and thus are applicable to certain kinds of multipath problems. Simulation results that illustrate the performance of the new algorithms are presented.
508Â citations
Cites methods from "Optimum localization of multiple so..."
...In the somewhat simpler 1-D problem, where the spectral densities of the sources are known, the maximum likelihood estimator has been studied by Good [13], Hahn and Tretter [14] Schweppe [38], and Wax and Kailath [ 43 ]....
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References
More filters
TL;DR: In this paper, a maximum likelihood estimator is developed for determining time delay between signals received at two spatially separated sensors in the presence of uncorrelated noise, where the role of the prefilters is to accentuate the signal passed to the correlator at frequencies for which the signal-to-noise (S/N) ratio is highest and suppress the noise power.
Abstract: A maximum likelihood (ML) estimator is developed for determining time delay between signals received at two spatially separated sensors in the presence of uncorrelated noise. This ML estimator can be realized as a pair of receiver prefilters followed by a cross correlator. The time argument at which the correlator achieves a maximum is the delay estimate. The ML estimator is compared with several other proposed processors of similar form. Under certain conditions the ML estimator is shown to be identical to one proposed by Hannan and Thomson [10] and MacDonald and Schultheiss [21]. Qualitatively, the role of the prefilters is to accentuate the signal passed to the correlator at frequencies for which the signal-to-noise (S/N) ratio is highest and, simultaneously, to suppress the noise power. The same type of prefiltering is provided by the generalized Eckart filter, which maximizes the S/N ratio of the correlator output. For low S/N ratio, the ML estimator is shown to be equivalent to Eckart prefiltering.
4,317Â citations
Book•
01 Jan 1971
TL;DR: In underwater sonar systems, external acoustic noise is generated by waves and wind on the water surface, by biological agents (fish, prawns, etc.), and by man-made sources such as engine noise.
Abstract: Probability. Random Processes. Narrowband Signals. Gaussian Derived Processes. Hypothesis Testing. Detection of Known Signals. Detection of Signals with Random Parameters. Multiple Pulse Detection of Signals. Detection of Signals in Colored Gaussian Noise. Estimation of Signal Parameters. Extensions. References. Bibliography. Index.
1,421Â citations
Book•
01 Jan 1981
1,133Â citations
TL;DR: The Cramer-Rao matrix bound for the vector delay estimate is derived, and used to show that either properly filtered beamformers or properly filtered systems of multiplier-correlators can be used to provide efficient estimates.
Abstract: For the purpose of localizing a distant noisy target, or, conversely, calibrating a receiving array, the time delays defined by the propagation across the array of the target-generated signal wavefronts are estimated in the presence of sensor-to-sensor-independent array self-noise. The Cramer-Rao matrix bound for the vector delay estimate is derived, and used to show that either properly filtered beamformers or properly filtered systems of multiplier-correlators can be used to provide efficient estimates. The effect of suboptimally filtering the array outputs is discussed.
261Â citations
09 Apr 1980
TL;DR: It is shown in this paper that if the spatial coherence of the background noise is not exactly known but can be modelized by a function which depends on some parameters, these parameters can be estimated and the high resolution method carried out.
Abstract: Researchs directed towards improvement of underwater passive listening techniques have led to high resolution methods which are based on the eigenvalues eigen vectors decomposition of the spectral density matrix of the signals received on the sensors of the array. These methods require the knowledge of the spatial coherence of sources and background noise. It is shown in this paper that if the spatial coherence of the background noise is not exactly known but can be modelized by a function which depends on some parameters, these parameters can be estimated and the high resolution method carried out.
243Â citations