Particle filtering is one of the most important algorithms for solving state estimation of nonlinear systems and has been widely studied in many fields. However, due to the unknown complex noise in the actual system, its estimation performance is degraded. Moreover, when the number of particles increase, the real-time performance of the algorithm is poor. For these two problems above, this paper proposed a parallel acceleration CRPF (cost-reference particle filter) algorithm based on CUDA (Compute Unified Device Architecture). CRPF does not need known noise statistics in nonlinear system state estimation, which can reduce the influence of unknown noise on state estimation accuracy. Combined with GPU's (Graphics Processing Unit) multi-thread parallel computing capability, CRPF parallel acceleration can be realized. Since the data association can't be parallel resampled, all the particles are evenly distributed to multiple blocks, and resampling process can be parallelized by block parallel computing, so as to improve the speed of the algorithm. At the same time, in order to reduce the global particle performance degradation caused by block resampling, the particles with low probability mass in each block are optimized by using a portion of global high-quality particles. Through two sets of simulation experiments, it is proved that the proposed method has improved in estimation accuracy and the real-time performance has been improved significantly, which can provide a new idea for the practical application of nonlinear filtering method.