Maximum-likelihood direction-of-arrival estimation in the presence of unknown nonuniform noise

We consider the problem of estimating directions of arrival (DOAs) of multiple sources observed on the background of nonuniform white noise with an arbitrary diagonal covariance matrix. A new deterministic maximum likelihood (ML) DOA estimator is derived. Its implementation is based on an iterative...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 49; no. 7; pp. 1310 - 1324
Main Authors: Pesavento, M., Gershman, A.B.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.07.2001
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Analytical models
Applied sciences
Arrays
Circuit noise
Closed-form solution
Covariance matrix
Detection, estimation, filtering, equalization, prediction
Direction of arrival estimation
Exact sciences and technology
Information, signal and communications theory
Iterative methods
Mathematical models
Maximum likelihood estimation
Noise
Nonuniform
Performance analysis
Seismic phenomena
Signal and communications theory
Signal processing
Signal, noise
Simulation
Stochastic resonance
Studies
Telecommunications and information theory
White noise
Performance evaluation
Simulation
Arrival angle
Signal estimation
White noise
Signal processing
Maximum likelihood
Antenna array
Cramer Rao inequality
ISSN:1053-587X, 1941-0476
Online Access:Get full text
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Summary:We consider the problem of estimating directions of arrival (DOAs) of multiple sources observed on the background of nonuniform white noise with an arbitrary diagonal covariance matrix. A new deterministic maximum likelihood (ML) DOA estimator is derived. Its implementation is based on an iterative procedure which includes a stepwise concentration of the log-likelihood (LL) function with respect to the signal and noise nuisance parameters and requires only a few iterations to converge. New closed-form expressions for the deterministic and stochastic direction estimation Cramer-Rao bounds (CRBs) are derived for the considered nonuniform model. Our expressions can be viewed as an extension of the well-known results by Stoica and Nehorai (1989, 1990) and Weiss and Friedlander (1993) to a more general noise model than the commonly used uniform one. In addition, these expressions extend the results obtained by Matveyev et al. (see Circuits, Syst., Signal Process., vol.18, p.479-87, 1999) to the multiple source case. Comparisons with the above-mentioned earlier results help to discover several interesting properties of DOA estimation in the nonuniform noise case. To compare the estimation performance of the proposed ML technique with the results of our CRB analysis and with the performance of conventional "uniform" ML, simulation results are presented. Additionally, we test our technique using experimental seismic array data. Our simulations and experimental results both validate essential performance improvements achieved by means of the approach proposed.
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ISSN:1053-587X
1941-0476
DOI:10.1109/78.928686