Stochastic Maximum-Likelihood DOA Estimation in the Presence of Unknown Nonuniform Noise

This correspondence investigates the direction-of-arrival (DOA) estimation of multiple narrowband sources in the presence of nonuniform white noise with an arbitrary diagonal covariance matrix. While both the deterministic and stochastic Cramer-Rao bound (CRB) and the deterministic maximum-likelihoo...

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Vydáno v:IEEE transactions on signal processing Ročník 56; číslo 7; s. 3038 - 3044
Hlavní autoři: Chiao En Chen, Lorenzelli, F., Hudson, R.E., Kung Yao
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.07.2008
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Applied sciences
Cities and towns
Detection, estimation, filtering, equalization, prediction
Direction of arrival estimation
Direction-of-arrival (DOA) estimation
Equations
Estimators
Exact sciences and technology
Information, signal and communications theory
Iterative methods
Mathematical models
Maximum likelihood estimation
Miscellaneous
Narrowband
Noise
Nonuniform
nonuniform noise
Position measurement
sensor array processing
Sensor arrays
Signal and communications theory
Signal processing
Signal processing algorithms
Signal, noise
Speech processing
stochastic maximum likelihood (ML) algorithm
Stochastic resonance
Stochasticity
Studies
Telecommunications and information theory
Performance evaluation
Direction-of-arrival estimation
Parameter estimation
Nuisance parameter
Array signal processing
sensor array processing
Signal estimation
Iterative method
Background noise
Implementation
White noise
Antenna array
Deterministic approach
Likelihood function
Cramer Rao inequality
Logarithmic function
stochastic maximum likelihood (ML) algorithm
Direction-of-arrival (DOA) estimation
Covariance matrix
Algorithm
Narrow band
Simulation
Diagonal matrix
Signal processing
nonuniform noise
Maximum likelihood
Sensor array
ISSN:1053-587X, 1941-0476
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Shrnutí:This correspondence investigates the direction-of-arrival (DOA) estimation of multiple narrowband sources in the presence of nonuniform white noise with an arbitrary diagonal covariance matrix. While both the deterministic and stochastic Cramer-Rao bound (CRB) and the deterministic maximum-likelihood (ML) DOA estimator under this model have been derived by Pesavento and Gershman, the stochastic ML DOA estimator under the same setting is still not available in the literature. In this correspondence, a new stochastic ML DOA estimator is derived. Its implementation is based on an iterative procedure which concentrates the log-likelihood function with respect to the signal and noise nuisance parameters in a stepwise fashion. A modified inverse iteration algorithm is also presented for the estimation of the noise parameters. Simulation results have shown that the proposed algorithm is able to provide significant performance improvement over the conventional uniform ML estimator in nonuniform noise environments and require only a few iterations to converge to the nonuniform stochastic CRB.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2008.917364