Signal Processing - Special section: Hans Wilhelm Schüßler celebrates his 75th birthday
Robust downlink power control in wireless cellular systems
EURASIP Journal on Wireless Communications and Networking - Special issue on multiuser MIMO networks
Asymptotic bounds for frequency estimation in the presence of multiplicative noise
EURASIP Journal on Applied Signal Processing
Gaussian channel model for mobile multipath environment
EURASIP Journal on Applied Signal Processing
Distributed fusion filter for systems with multiplicative noise
ISCGAV'09 Proceedings of the 9th WSEAS international conference on Signal processing, computational geometry and artificial vision
Journal of Computational and Applied Mathematics
Derivation of centralized and distributed filters using covariance information
Computational Statistics & Data Analysis
Fast DOA estimation of incoherently distributed sources by novel propagator
Multidimensional Systems and Signal Processing
Low-Complexity Estimation of the Nominal Azimuth and Elevation for Incoherently Distributed Sources
Wireless Personal Communications: An International Journal
Low-Complexity Estimation of DOA and Angular Spread for an Incoherently Distributed Source
Wireless Personal Communications: An International Journal
A Simplified Estimator for Tridimensional Localization of Single Incoherently Distributed Source
Wireless Personal Communications: An International Journal
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We consider the problem of localizing a source by means of a sensor array when the received signal is corrupted by multiplicative noise. This scenario is encountered, for example, in communications, owing to the presence of local scatterers in the vicinity of the mobile or due to wavefronts that propagate through random inhomogeneous media. Since the exact maximum likelihood (ML) estimator is computationally intensive, two approximate solutions are proposed, originating from the analysis of the high and low signal to-noise ratio (SNR) cases, respectively. First, starting with the no additive noise case, a very simple approximate ML (AML1) estimator is derived. The performance of the AML1 estimator in the presence of additive noise is studied, and a theoretical expression for its asymptotic variance is derived. Its performance is shown to be close to the Cramer-Rao bound (CRB) for moderate to high SNR. Next, the low SNR case is considered, and the corresponding AML2 solution is derived. It is shown that the approximate ML criterion can be concentrated with respect to both the multiplicative and additive noise powers, leaving out a two-dimensional (2-D) minimization problem instead of a four-dimensional (4-D) problem required by the exact ML. Numerical results illustrate the performance of the estimators and confirm the validity of the theoretical analysis