Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/112316
Title: Bayesian array signal processing in additive generalized Gaussian noise
Authors: KANNAN BALAKRISHNAN 
Keywords: Bayesian estimation
M-H algorithm
Non-Gaussian signal processing
Sensor array processing
Issue Date: 2001
Citation: KANNAN BALAKRISHNAN (2001). Bayesian array signal processing in additive generalized Gaussian noise. IEEE Workshop on Statistical Signal Processing Proceedings : 86-89. ScholarBank@NUS Repository.
Abstract: In this paper, we present a Bayesian approach for DOA and frequency estimation of narrow band signals in additive generalized Gaussian noise. Using Bayesian techniques, the posterior probability densities for DOA (Direction Of Arrival) and frequency parameters are derived from the signal and noise models. These posterior probabilities are then used in the Metropolis-Hastings (M-H) algorithm to derive the samples for the DOA and frequency parameters. The performances of our algorithms are studied by plotting the MSEs (Mean Square Errors) of the parameters for various SNRs. The MSEs of the parameters are compared with the CRLBs (Cramer Rao Lower Bound) for the generalized Gaussian models.
Source Title: IEEE Workshop on Statistical Signal Processing Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/112316
Appears in Collections:Staff Publications

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