Parallel Differential Evolutionary Particle Filtering Algorithm Based on the CUDA Unfolding Cycle

Abstract

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.

Implementation and analysis of a parallel kalman filter algorithm for lidar localization based on CUDA technology

“…1. Large Data Volumes (Huang and Cao, 2021): Autonomous vehicles accumulate substantial data from diverse sensors like lidars, radars, and cameras. Swift processing of this data is crucial for appropriate responses to varied situations.…”

“…6. Adaptability to Various Computing Resources (Huang and Cao, 2021): Parallel computing is applicable across different computing platforms, including CPUs, GPUs, and specialized accelerators like FPGAs and ASICs. This adaptability allows navigation algorithms to be tailored to the available resources, optimizing their overall efficiency.…”