交互式多模型集自适应协同滤波目标跟踪算法研究

doi:10.3969/j.issn.1000-1158.2021.05.15

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Abstract In order to solve the limitation of the static model set of the traditional interactive multiple model(IMM)algorithm, a interactive multi-model set adaptation collaborative filtering(SAC-IMM)algorithm is proposed. By calculating the model matching probability between the target and different models in the current model set, the best model and the worst model in the current model match are automatically determined. The adaptive process of the model set is achieved by changing the structure of the static model set by using activation, retention and elimination strategies. Compared with the traditional IMM algorithm, the SAC-IMM algorithm proposed has a certain degree of improvement in positioning accuracy. By comparing with other IMM algorithms that have been proposed, the experimented results show that the SAC-IMM algorithm proposed has been optimized for state estimation of speed, acceleration, and turning rate. The proposed method can improve the accuracy of target tracking and positioning to a certain extent.

Key words： metrology target tracking interactive multi-model set adaptation collaborative filtering maneuver discrimination

Received: 31 March 2020 Published: 24 May 2021

PACS:	TB973
	TB934

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	Articles by authors
	KONG De-ming
	YANG Dan
	WANG Shu-tao

Cite this article:

KONG De-ming,YANG Dan,WANG Shu-tao. Research on Interactive Multi-model Set Adaptive Collaborative Filtering Target Tracking Algorithm[J]. Acta Metrologica Sinica, 2021, 42(5): 638-644.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.05.15 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I5/638

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