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Adaptive filtering and smoothing algorithm based on variable structure interactive multiple model.
Hu, Kai-Yu; Wang, Jiaming; Cheng, Yuqing; Yang, Chunxia.
Afiliación
  • Hu KY; The 304 Institute, China Aerospace Science and Industry Corporation, Beijing, 100074, People's Republic of China. hkywuyue@163.com.
  • Wang J; Center for Applied Mathematics, China Aerospace Science and Industry Corporation, Beijing, 100074, People's Republic of China. hkywuyue@163.com.
  • Cheng Y; The 304 Institute, China Aerospace Science and Industry Corporation, Beijing, 100074, People's Republic of China.
  • Yang C; The 304 Institute, China Aerospace Science and Industry Corporation, Beijing, 100074, People's Republic of China.
Sci Rep ; 13(1): 12993, 2023 Aug 10.
Article en En | MEDLINE | ID: mdl-37563137
For maneuvering target tracking, a novel adaptive variable structure interactive multiple model filtering and smoothing (AVSIMMFS) algorithm is proposed in this paper. Firstly, an accurate model of the variable structure interactive multiple model algorithm is established. Secondly, by constructing a new model subset based on the original model subsets, the matching accuracy between the model subset and the actual maneuvering mode of the target is improved. Then, the AVSIMMFS algorithm is obtained by smoothing the filtered data of the new model subset. Because of the combination of forward filtering and backward smoothing, the target tracking accuracy is further improved. Finally, in order to verify the effectiveness of the algorithm, the simulation is carried out on two cases. The simulation results show that the tracking performance of AVSIMMFS algorithm is better than other methods and has lower calculation cost.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article Pais de publicación: Reino Unido