Research on Parallel Computing Method of Kalman Filter Based on CUDA

The Kalman filter is an important tool for state estimation of linear dynamic systems and is widely used in signal processing, control systems, navigation and other fields. The computational efficiency of the traditional Kalman filter is limited when processing large-scale data. This article proposes a Kalman filter parallel computing method based on CUDA (Compute Unified Device Architecture), which improves processing speed by executing filter steps in parallel on the GPU. Experimental results show that compared with traditional CPU implementation, the Kalman filter implemented by CUDA shows significant acceleration effect on large data sets.