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Bayesian multi-class covariance matrix filtering for adaptive environment learning

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Abstract
Covariance matrix estimation is a crucial task in adaptive signal processing applied to several surveillance systems, including radar and sonar. In this paper we propose a dynamic environment learning strategy to track both the covariance matrix and its class; the class represents a set of structured covariance matrices. We assume that the posterior distribution of the covariance given the class, is basically a mixture of inverse Wishart, while the class posterior distribution evolves according to a Markov chain. The proposed multi-class inverse Wishart mixture filter is shown to outperform the class-clairvoyant maximum likelihood estimator in terms of covariance estimate accuracy, as well as the Bayesian information criterion rule in terms of classification performance.

URI
http://hdl.handle.net/20.500.12489/774

Report Number
CMRE-PR-2019-033

Source
In: 2018 26th European Signal Processing Conference (EUSIPCO), Rome, Italy, 3-7 September 2018, pp. 266-270, doi: 10.23919/EUSIPCO.2018.8553440

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Date
2019/05

Author(s)
Braca, Paolo
; 
Aubry, Augusto
; 
Millefiori, Leonardo
; 
De Maio, Antonio
; 
Marano, Stefano

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CMRE-PR-2019-033.pdf (602.6Kb)

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