  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.
