Abstract: Aiming at the particle filter’s inefficacy problem caused by narrow likelihood of accurate measurement system, a new particle filter with resampling step based on clustering and determination quasi-Monte Carlo is proposed. When importance samples are lost, the algorithm extracts key samples using clustering method, and then determines the number of spring-offs of them and sampling space according to the new support set composed of key samples. New particles are generated by QMC, in order to obtain more efficient samples and avoid the inefficacy phenomena. Simulations show that, compared to the general resampling step of particle filter, this algorithm obtains more accurate and steady state estimation.