Bayesian filtering for nonlinear state-space models in symmetric alpha-stable measurment noise
Document typeConference report
Rights accessOpen Access
Bayesian ltering appears in many signal processing problems,reason why it attracted the attention of many researchers to develop efficient algorithms, yet computationally a ordable. In many real systems, it is appropriate to consider α-stable noise distributions to model possible outliers or impulsive behavior in the measurements. In this paper, we consider a nonlinear state-space model with Gaussian process noise and symmetric α-stable measurement noise. To obtain a robust estimation framework we consider that both process and measurement noise statistics are unknown. Using the product property of α-stable distributions we rewrite the measurement noise in a conditionally Gaussian form. Within this framework, we propose an hybrid sigma-point/Monte Carlo approach to solve the Bayesian ltering problem, what leads to a robust method against both outliers and a weak knowledge of the system.
CitationVila, J. [et al.]. Bayesian filtering for nonlinear state-space models in symmetric alpha-stable measurment noise. A: European Signal Processing Conference. "2011 EUSIPCO - 19th European Signal Processing Conferenc". 2011, p. 674-678.
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