Open AccessLabeled Multi‐Bernoulli Maneuvering Target Tracking Algorithm via TSK Iterative Regression Model
Author(s) -
WANG Xiaoli,
XIE Weixin,
LI Liangqun
Publication year - 2022
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2020.00.156
Subject(s) - computer science , bernoulli's principle , feature (linguistics) , set (abstract data type) , tracking (education) , iterative method , algorithm , nonlinear system , fuzzy logic , artificial intelligence , regression , mathematics , psychology , pedagogy , philosophy , linguistics , physics , statistics , quantum mechanics , engineering , programming language , aerospace engineering
Aiming at the problem that the existing labeled multi‐Bernoulli (LMB) method has a single and fixed model set, an LMB maneuvering target tracking algorithm via Takagi‐Sugeno‐Kang (TSK) iterative regression multiple model is proposed. In the TSK iterative regression modeling, the feature information of the targets is analyzed and represented by multiple semantic fuzzy sets. Then the state is expanded to introduce model information, thereby the adaptive multi‐model idea is incorporated into the framework of the LMB method to solve the uncertain maneuverability of moving targets. Finally, the simulation results show that the proposed algorithm can effectively achieve maneuvering target tracking in the nonlinear system.