A New Evidence Weight Combination and Probability Allocation Method in Multi-Sensor Data Fusion

Abstract

A single sensor is prone to decline recognition accuracy in the face of a complex environment, while the existing multi-sensor evidence theory fusion methods do not comprehensively consider the impact of evidence conflict and fuzziness. In this paper, a new evidence weight combination and probability allocation method is proposed, which calculated the degree of evidence fuzziness through the maximum entropy principle, and also considered the impact of evidence conflict on fusing results. The two impact factors were combined to calculate the trusted discount and reallocate the probability function. Finally, Dempster’s combination rule was used to fuse every piece of evidence. On this basis, experiments were first conducted to prove that the existing weight combination methods produce results contrary to common sense when handling high-conflicting and high-clarity evidence, and then comparative experiments were conducted to prove the effectiveness of the proposed evidence weight combination and probability allocation method. Moreover, it was verified, on the PAMAP2 data set, that the proposed method can obtain higher fusing accuracy and more reliable fusing results in all kinds of behavior recognition. Compared with the traditional methods and the existing improved methods, the weight allocation method proposed in this paper dynamically adjusts the weight of fuzziness and conflict in the fusing process and improves the fusing accuracy by about 3.3% and 1.7% respectively which solved the limitations of the existing weight combination methods.

Deep Evidential Remote Sensing Landslide Image Classification With a New Divergence, Multiscale Saliency and an Improved Three-Branched Fusion

“…w i EBetP i (39) where H represents the traditional Shannon entropy. Since when P is a n-dimensional discrete uniform distribution, the Shannon entropy can achieve its maximum value, we have…”

“…It has been exploited as a plug-and-play module in copious domains, such as machine learning [31]- [34], object detection [35] and opinion aggregation [36]. This theory is sensitive to decision uncertainty [37], and specially tailors a "Dempster's combination rule" for multiple evidence fusion [38], [39]. On the basis of its merits, the present study investigates evidence theory-coupled deep learning in remote sensing landslide image classification.…”

Multisensor Data Fusion in IoT Environments in Dempster–Shafer Theory Setting: An Improved Evidence Distance-Based Approach

“…In [89], the evidence model was revised using a novel correction coefficient based on a stochastic approach for the link-structure analysis (SALSA) algorithm combined with the Lance-Williams distance to better measure the degree of support for each piece of evidence. Ma et al [90] adopted the trusted discount method to alleviate the shortcoming in D-S theory, incorporating Jousselme distance for conflict measurement, and Wasserstain distance for uncertainty measurement, and they proposed a new adaptive weights' allocation method. Huwa and Jing [91] introduced an improved belief Hellinger divergence that considers both belief and plausibility functions to measure the discrepancy between the pieces of evidence and used belief entropy to measure the uncertainty degree.…”