Abstract: The choose of important function is a critical issue in particle filter algorithm,in the paper we propose a extended H_∞particle filter (EHPF) algorithm with a important function generated by the extended H_∞ filter(EHF) . Because the extended H_∞filter al-gorithm has very high accuracy and strong robustness, and the filter algorithm integrates the new observations, then the important function which it generates can approximate the real posterior probability distribution of the system state reasonable well. The theoretical analysis and experimental results show that the extended  H_∞ particle filter algorithm is superior to the standard particle filter algorithm and others  filters algorithm such as the extended kalman filter algorithm and extended kalman particle filter algorithm, provides performance compara-ble to that of the unscented kalman particle filter algorithm but with lower computational cost, so it's a effective particle filter algorithm.