Open AccessAn improved weighted fusion algorithm of multi-sensor
Author(s) -
Haibin Liu,
Shuhua Fang,
Jianhua Ji
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 - 10.1088/1742-6596/1453/1/012009
Subject(s) - fusion , sensor fusion , algorithm , computer science , weighted arithmetic mean , data mining , selection (genetic algorithm) , artificial intelligence , machine learning , philosophy , linguistics
Multi-sensor data fusion is to take full advantage of the complementary nature of multivariate data to improve the feasibility of the statistics. The weighted fusion algorithm is commonly used due to its easiness to achieve. Among the relative factors, the weight directly impacts the results of the data fusion, therefore, the selection of weight is particularly important, as choosing an inappropriate weight will lead to the instability of algorithm performance. To find the best weight, we develop an improved weighted fusion algorithm, introducing the concept of the optimal proportion weight and using secondary weighted approach the single sensor will be weighted individually before the whole sensor system is weighted in order to achieve the optimal algorithm performance.