Maneuvering Target Tracking Based on Unscented Particle Filter Aided by Neutral Network
A filtering method aided by neural network to improve the maneuvering target tracking performance is proposed in this paper. Based on unscented Kalman filter, the unscented particle filter (UPF) has more accurate proposal distribution and better approximation to non-linear tracking problem than other Sequential Monte-Carlo methods. The neural network is constructed and trained by the maneuvering features, and the outputs of NN are used as acceleration control parameters to correct model parameters. Simulation results show the performance of UPF aided by NN is much improved than extensive Kalman filter.
KeywordsExtensive Kalman Filter Target Tracking Unscented Kalman Filter Neutral Network Maneuvering Target
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