Abstract
AbstractWhen dealing with the real track, the environment is often an unpredictable factor, so filtering is very important. We can use the filter to eliminate the influence of noise as much as possible. Kalman filter is one of them. In this work, we proposed a new Particle Swarm Optimization algorithm, called the Sheep Herding Optimization algorithm, which can obtain higher quality solutions with faster convergence speed and better stability. Besides, in order to improve the performance of Kalman filter, we apply the Sheep Herding Optimization algorithm to the filter. The improved Kalman filter can fuse and predict the track, and has higher computational performance and smaller error.KeywordsKalman filterPSOKSPFilteringTrack fusion
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