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Bayesian filtering for nonlinear state-space models in symmetric alpha-stable measurment noise
Vila Valls, Jordi; Fernández Prades, Carlos; Closas Gómez, Pau; Fernández Rubio, Juan Antonio
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions
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.
Peer Reviewed
Àrees temàtiques de la UPC::Enginyeria electrònica i telecomunicacions::Processament del senyal
Bayesian filtering problem
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