Bayesian filtering for nonlinear state-space models in symmetric alpha-stable measurment noise
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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.
CitacióVila, 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.