RMSE Based Performance Analysis of Marginalized Particle Filter and Rao Blackwellised Particle filter for Linear/Nonlinear State Space Models

Particle filters and Rao Blackwellised particle filter have been widely used in solving nonlinear filtering problems. The particle filter is fairly easy to implement and tune, its main drawback is that it is quite computer intensive, with the computational complexity increasing quickly with the state dimension. One solution to this problem is to marginalize out the states appearing linearly in the dynamics. The result is that one Kalman filter is associated with each particle. The main contribution in this paper is to analyse the performance of the marginalized particle filter and Rao Blackwellised Particle filter for a general nonlinear state-space model. In an extensive Monte Carlo simulation different computational aspects are studied and compared with the derived theoretical results.