Multi-Source Interval-Typed Sensor Information Fusion Based on a New Belief Structure Generating Method Using ILWD and Jaccard Similarity Coefficient

Multi-source sensor information fusion technology plays an important role in many application fields in the era of “Industry 4.0”. Generally, in practical engineering applications, the obtained information is inevitably uncertain due to the degradation of sensor performance, environmental interference, abnormal communication transmission process, etc. Compared with the strict requirements of probability density function for data quantity, the use of interval numbers to characterize the uncertainty of information has obvious advantages. Classical Dempster-Shafer(D-S) evidence theory can effectively deal with uncertain information, whereas its evidence structure is single-valued, making it difficult to fuse interval uncertainty information. Therefore, it is necessary to adopt an interval-valued evidence structure to carry out interval uncertainty information fusion. Current researches on interval-valued belief structure mainly focus on the modification of evidence combination rules but neglect the key problem of how to generate interval-valued evidence structure from original data obtained from sensors. Aiming at the problem of classification and discrimination based on multi-source interval-valued sensor information fusion, this paper adopts the theory of interval-valued evidence and focuses on the problem of generating a reasonable belief structure based on original interval-valued sensor information by using a multistage correction framework. Firstly, an interval-valued Lance and Williams Distance(ILWD) is proposed, and an initial belief structure is generated based on the proposed ILWD using the original sensor information. Secondly, by considering the interval length effect and based on Jaccard similarity coefficient, the initial interval-valued belief structure is corrected by a two-stage process. Then, in order to alleviate the impact of evidence conflict on the combination results, the weight of each evidence is assigned by an optimization method (the third stage of the belief structure modification). Further, the evidence is combined based on the method proposed by Wang et al. (2007). Finally, a practical case study is carried out to verify the rationality and effectiveness of the proposed method.