As to solve the problem of dim small target tracking in low signal-to-noise ratio (SNR<3 dB) scenes, an improved particle filter tracking method is proposed. This paper firstly obtains the gray feature by spatial position weighting method, and combines the neighborhood motion model and the gray probability graph to get the motion features of dim small target. Then construct the joint observation model of gray and motion features to calculate the particle weights. At the same time, in the process of tracking, the gray distribution of the target is not stable, and the strategy of adaptively updating the gray template of reference target is added. Finally, the sequence image is used to prove the tracking effect of dim small target. Experiments show that compared with the traditional particle filter algorithm, the proposed method greatly enhanced the tracking ability of dim small target in low SNR (SNR<3 dB) scenes.
Dim small target tracking based on improved particle filter
First published at:Aug 01, 2018
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National Natural Science Foundation of China (61571096)
Get Citation: Fan Xiangsuo, Xu Zhiyong, Zhang Jianlin. Dim small target tracking based on improved particle filter[J]. Opto-Electronic Engineering, 2018, 45(8): 170569.
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