Research Article
High-Discrimination Multi-sensor Information Decision Algorithm Based on Distance Vector
@INPROCEEDINGS{10.1007/978-3-030-63941-9_4, author={Lingfei Zhang and Bohang Chen}, title={High-Discrimination Multi-sensor Information Decision Algorithm Based on Distance Vector}, proceedings={6GN for Future Wireless Networks. Third EAI International Conference, 6GN 2020, Tianjin, China, August 15-16, 2020, Proceedings}, proceedings_a={6GN}, year={2021}, month={1}, keywords={Information decision Distance vector Support matrix Dominance function Discrimination function}, doi={10.1007/978-3-030-63941-9_4} }- Lingfei Zhang
Bohang Chen
Year: 2021
High-Discrimination Multi-sensor Information Decision Algorithm Based on Distance Vector
6GN
Springer
DOI: 10.1007/978-3-030-63941-9_4
Abstract
In the process of sensor target recognition, attitude estimation and information decision-making, most of the current sensor information decisions require probability conversion or weight calculation of sensor data. The calculation process is complex and requires a large amount of computation. In addition, the decision result is greatly affected by the probability value. This paper proposes a multi-sensor information decision algorithm with high-discrimination based on distance vectors. At the same time, the support function, dominance function and discrimination function for the algorithm are presented. The dominance function is obtained through the normalization processing of the support matrix, and then the dominance function after normalization is sorted. The maximum value is taken as the optimal solution. The discrimination function mainly provides the basis for the evaluation of the algorithm. The simulation results show that the discrimination degree of this method in sensor information decision-making reaches more than 0.5, and the decision-making effect is good. Compared with the classic D-S evidence theory, this algorithm can effectively avoid the phenomenon that D-S evidence theory contradicts with the actual situation when making a decision. It is less affected by a single sensor and the decision result is stable. Compared with the probabilistic transformation of the initial data of the sensor in the decision-making process, it has obvious advantages.
