Robust particle filter by dynamic averaging of multiple noise models

State filtering is a key problem in many signal processing applications. From a series of noisy measurement, one would like to estimate the state of some dynamic system. Existing techniques usually adopt a Gaussian noise assumption which may result in a major degradation in performance when the measurements are with the presence of outliers. A robust algorithm immune to the presence of outliers is desirable. To this end, a robust particle filter (PF) algorithm is proposed, in which the heavier tailed Student's t distributions are employed together with the Gaussian distribution to model the measurement noise. The effect of each model is automatically and dynamically adjusted via a Bayesian model averaging mechanism. The validity of the proposed algorithm is evaluated by illustrative simulations.