Aiming at the problem of low statute efficiency of prefix sum execution during the execution of the parallel differential evolutionary particle filtering algorithm, a filtering algorithm based on the CUDA unfolding cyclic prefix sum is proposed to remove the thread differentiation and thread idleness existing in the parallel prefix sum by unfolding the cyclic method and unfolding the thread bundle method, optimize the cycle, and improve the prefix sum execution efficiency. By introducing the parallel strategy, the differential evolutionary particle filtering algorithm is implemented in parallel and executed on the GPU side using the improved prefix sum computation during the algorithm update. Through big data analysis, the results show that this parallel differential evolutionary particle filtering algorithm with the improved prefix sum statute can effectively improve differential evolutionary particle filtering for nonlinear system states and real-time performance in heterogeneous parallel processing systems.

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