Progressive correction for regularized particle filters

Particle methods have been recently proposed to deal with the nonlinear filtering problem. These are Monte Carlo methods that can provide a nonparametric approximation to the signal conditional distribution even in nonlinear and non Gaussian cases, without depending on the state space dimension. We present a new version of regularized particle filter using a progressive correction (PC) principle which improves the approximation, in introducing a decreasing sequence of (fictitious) matrices for the observation noise. This method is applied to the multisensor tracking problem (radar and IR sensor) and compared to the classical regularized particle filter and the EKF.