Likelihood particle filter based on support vector machines resampling

To cope with state estimation problems of nonlinear/non-Gaussian dynamic systems with weak measurement noise, an improved likelihood particle filter(LPF) algorithm is proposed based on support vector machines(SVM) resampling. Firstly, the algorithm employs the likelihood as proposal distribution and takes account of the most recent observation, so it is comparably closer to the posterior than the transition prior used as proposal. Then, the posterior probability density model of the states is estimated by SVM with current particles and their importance weights during iteration. Finally, after resampling the new particles from the given density model, degeneration problem is solved effectively by these diversiform particles. The simulation results show the feasibility and effectiveness of the algorithm.