Research on Kalman Filter Parallel Computing Method Based on OpenMP

Kalman filtering is an algorithm widely used in state estimation of dynamic systems, and its wide applications include navigation, tracking and control systems. However, the computational complexity of Kalman filtering makes it inefficient when processing large-scale data. This paper studies the parallel processing method of Kalman filtering, and improves the execution efficiency of the algorithm by dividing the filtering process into blocks and parallelizing them. Through experiments on simulated data of sinusoidal signals plus Gaussian white noise, the significant advantages of parallel processing methods in computing time are verified, and the influence of block processing on filtering results is discussed.