Abstract
Dempster–Shafer theory (DST), which is widely used in information fusion, can process uncertain information without prior information; however, when the evidence to combine is highly conflicting, it may lead to counter-intuitive results. Moreover, the existing methods are not strong enough to process real-time and online conflicting evidence. In order to solve the above problems, a novel information fusion method is proposed in this paper. The proposed method combines the uncertainty of evidence and reinforcement learning (RL). Specifically, we consider two uncertainty degrees: the uncertainty of the original basic probability assignment (BPA) and the uncertainty of its negation. Then, Deng entropy is used to measure the uncertainty of BPAs. Two uncertainty degrees are considered as the condition of measuring information quality. Then, the adaptive conflict processing is performed by RL and the combination two uncertainty degrees. The next step is to compute Dempster’s combination rule (DCR) to achieve multi-sensor information fusion. Finally, a decision scheme based on correlation coefficient is used to make the decision. The proposed method not only realizes adaptive conflict evidence management, but also improves the accuracy of multi-sensor information fusion and reduces information loss. Numerical examples verify the effectiveness of the proposed method.
Highlights
The practical experience shows that comparing with a single-sensor system, multi-sensor systems can significantly enhance the system performance of detection, identification, and fault diagnosis [11,12]; due to various uncertainties in the real world, the information obtained by multi-sensor is affected
Due to the impact of the actual environment, the multi-sensor information fusion decision system may be of high conflict; it needs to set up a reasonable action policy to realize the effective processing of conflicting data
We have investigated the multi-sensor online fusion problem, and proposed a novel method on the basis of the uncertainty of basic probability assignment (BPA) and reinforcement learning (RL)
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. How to correctly process multi-sensor information and establish a fusion model is a widespread attention problem As for this issue, many theories and methods have been proposed, for example Z-number [16,17], D-number [18,19], fuzzy sets [20,21,22], rough sets [23,24], R-number [25], entropy-based [26,27], and Dempster–Shafer theory (DST) [28,29]. This paper proposes a new information fusion method, which combines the uncertainty of evidence and RL. In order to achieve the adaptive online information fusion, RL is combined with the uncertainty degrees to process the conflicting evidence.
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