Combined Parameter and State Estimation in Particle Filtering

In this paper, an adaptive estimation algorithm is proposed for non-linear dynamic systems with unknown static parameters based on combination of particle filtering. The estimates of static parameters are obtained by state samples and maximum-likelihood estimation in particle filtering, and the stochastic approximation is used to approximate the gradient of cost function. The proposed algorithm achieves combined state and parameters estimation. Simulation result demonstrates the efficiency of the algorithm.