Open AccessMulti-sensor weighted fusion algorithm based on improved TOPSIS
Author(s) -
Tao Sheng,
Xia Hai-bao
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1607/1/012067
Subject(s) - sensor fusion , fusion , closeness , algorithm , computer science , topsis , tracking (education) , ideal solution , process (computing) , ideal (ethics) , artificial intelligence , mathematics , psychology , mathematical analysis , pedagogy , philosophy , linguistics , physics , epistemology , operations research , thermodynamics , operating system
In the process of track fusion of multi-sensor target tracking, multi-sensor weighted fusion is used to overcome the shortcoming of incomplete single information source. In order to change the weight of each sensor participating in fusion in real time according to the merits and demerits of sensor information adaptively, a method of sensor weight calculation based on improved TOPSIS is proposed. Firstly, the local estimation and global prediction residuals of each sensor are composed into residuals matrix, from which the ideal solution is found, and then the relative closeness degree is calculated according to the grey correlation distance between the local estimation residuals and the ideal solution, so as to screen the sensors with high weight to participate in the fusion. The simulation results show that the tracking error of the algorithm after fusion is significantly reduced compared with the local estimation error of each sensor, and the tracking error of multiple sensors for the target can be realized.