A mixed target estimation fusion algorithm based on Gibbs‐GLMB and federated filter. Issue 1 (23rd March 2022)

• Record Type: Journal Article
• Authors: Liu, Yu; Peng, Zhangming; Gao, Shibo; Li, Jiangning
• Other Names: Liu Weifeng guestEditor.
• Is Part Of: IET cyber-systems and robotics. Volume 4:Issue 1(2022)
• Page Start: 61
• Page End: 75
• Publication Date: 2022-03-23
• Subjects: mixed target -- track fusion -- tracking control; Robotics -- Periodicals; Cybernetics -- Periodicals; Cybernetics; Robotics; Periodicals; 629
• DOI: 10.1049/csy2.12044
• Languages: English
• ISSNs: 2631-6315

Abstract

Abstract: Mixed targets are composed of point targets, extended targets, and group targets. The point target can produce one measurement at most, the extended target and the group target can produce multiple measurements, but the sub‐goals of the group target have a certain dependency relationship. At this time, the estimated fusion of the group target is converted to the estimated fusion of sub‐targets with formation motion structure, and the distance among the sub‐targets is very close, which brings difficulties to the estimated fusion of mixed targets. This paper combines the adjacency matrix in graph theory to dynamically model the discernible group target and introduces the concept of deformation. Also, it uses the finite mixture model method to dynamically model the extended target. Then the Gibbs‐GLMB algorithm is used to estimate the state and number of the mixed targets. A dynamic detection federated filter fusion algorithm is proposed to fuse the mixed targets state estimates. The effectiveness of the algorithm is verified in the final simulation.