A novel method to determine basic probability assignment in Dempster–Shafer theory and its application in multi-sensor information fusion

Liguo Fei,Yuqiang Feng,Luning Liu,Jun Xia

doi:10.1177/1550147719865876

Abstract

Multi-sensor information fusion occurs in a vast variety of applications, including medical diagnosis, automatic drive, speech recognition, and so on. Often these problems can be modeled by Dempster–Shafer theory. In Dempster–Shafer theory, the most primary processing unit is the basic probability assignment, which is a description of objective information in the real world. How to make this description more effective is a vital but open issue. A novel basic probability assignment generation model is proposed in this article whose objective is to provide perspective with respect to how basic probability assignment can be determined based on learning algorithms. First, the basic probability assignment generation model is constructed based on clustering idea using K-means method, which is employed to determine basic probability assignment with the proposed basic probability assignment generation method. Moreover, the proposed basic probability assignment generation method is extended by K–nearest neighbor (K-NN) algorithm. The detailed implementation of the proposed method is demonstrated by several numerical examples. As an extension, a classifier called KKC is constructed according to the developed approach, and its classification effect is compared with several famous classification algorithms. Experiments manifest desirable results with regard to classification accuracy, which illustrates the applicability of the proposed method to determine basic probability assignment.

Highlights

Information fusion is an information processing technology, known as data fusion.[1]
The basic probability assignment (BPA) generation models are constructed based on training sets using the method developed in Section ‘‘Construct the model to generate BPA based on K-means method.’’ For each sample in different test sets, BPAs of all the attributes are calculated based on the method presented in sections ‘‘Generate BPAs based on the constructed model’’ and ‘‘The KNN-based BPA generation method.’’ The classifier K-means and K-NN based classifier (KKC) is employed for BPAs of attributes associated samples from different test sets, all the samples to be classified are assigned class labels
We tried to establish a model for determining BPA in Dempster–Shafer evidence theory (DST)

Summary

Introduction

Information fusion is an information processing technology, known as data fusion.[1]. The BPAs of them are determined based on the proposed generation model, which are processed to obtain the final BPA for the sample to be tested. A BPA construction method using confusion matrix is proposed, which employed the accuracy and recall rate of each class to model and generate the BPA. The normalized similarity is used to obtain BPAs. An improved method to obtain basic belief assignment is proposed based on the triangular fuzzy number and k-means + + algorithm. Pessimistic strategy based on the differentiation degree between model and sample is defined to determine BPAs. This paper presents three methods to construct the BPA function. Based on the above analysis and aiming at how to effectively generate the BPA of sensor data, this article breaks the traditional statistical modeling method and proposes the BPA generation algorithm innovatively.

Ci x2Ci x

Construct the BPA generation model

Generate BPAs of test samples based on the established model

Findings

Conclusions and future research