Multi-sensor data fusion based on soft likelihood functions and OWA aggregation and its application in target recognition system

Xiangjun Mi; Tongxuan Lv; Ye Tian; Bingyi Kang

doi:10.1016/j.isatra.2020.12.009

Multi-sensor data fusion based on soft likelihood functions and OWA aggregation and its application in target recognition system

ISA Trans. 2021 Jun:112:137-149. doi: 10.1016/j.isatra.2020.12.009. Epub 2020 Dec 8.

Authors

Xiangjun Mi¹, Tongxuan Lv¹, Ye Tian¹, Bingyi Kang²

Affiliations

¹ College of Information Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China.
² College of Information Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China. Electronic address: bingyi.kang@nwsuaf.edu.cn.

PMID: 33349453
DOI: 10.1016/j.isatra.2020.12.009

Abstract

Multi-sensor data fusion plays an irreplaceable role in actual production and application. Dempster-Shafer theory (DST) is widely used in numerous fields of information modeling and information fusion due to the flexibility and effectiveness of processing uncertain information and dealing with uncertain information without prior probabilities. However, when highly contradictory evidence is combined, it may produce results that are inconsistent with human intuition. In order to solve this problem, a hybrid method for combining belief functions based on soft likelihood functions (SLFs) and ordered weighted averaging (OWA) operators is proposed. More specifically, a soft likelihood function based on OWA operators is used to provide the possibility to fuse uncertain information compatible with each other. It can characterize the degree to which the probability information of compatible propositions in the collected evidence is affected by unknown uncertain factors. This makes the results of using the Dempster's combination rule to fuse uncertain information from multiple sources more comprehensive and credible. Experimental results manifest that this method is reliable. Example and application show that this method has obvious advantages in solving the problem of conflict evidence fusion in multi-sensor. In particular, in target recognition, when three pieces of evidence are fused, the target recognition rate is 96.92%, etc.

Keywords: Dempster–Shafer theory; Multi-sensor data fusion; Ordered weighted averaging aggregation; Soft likelihood functions; Target recognition system.