Joint parameter and state estimation based on particle filtering and stochastic approximation

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