Abstract
The Dempster-Shafer (D-S) theory is widely applied in various fields involved with multi-sensor information fusion for radar target tracking, which offers a useful tool for decision-making. However, the application of D-S evidence theory has some limitations when evidences are conflicting. This paper proposed a new method combining the Pignistic probability distance and the Deng entropy to address the problem. First, the Pignistic probability distance is applied to measure the conflict degree of evidences. Then, the uncertain information is measured by introducing the Deng entropy. Finally, the evidence correction factor is calculated for modifying the bodies of evidence, and the Dempster’s combination rule is adopted for evidence fusion. Simulation experiments illustrate the effectiveness of the proposed method dealing with conflicting evidences.
Highlights
In complex battlefield environments, modern combat systems need to identify air targets accurately and quickly
This paper introduces Pignistic probability distance and Deng entropy to compute the evidence correction factor, and the bodies of evidence are modified before using Dempster’s combination rule
The simulation results prove that the method in this paper effectively solves the problem of conflict evidence combination
Summary
Modern combat systems need to identify air targets accurately and quickly. Zhang et al (2014) applied the idea of distance-based evidence conflict analysis and proposed a new method based on the law of cosines to identify and represent conflict data It ignores the influence of evidence on the correction coefficient, so the method in this paper introduces Deng entropy (Zhang and Deng 2019; Kang and Deng 2019; Gao and Deng 2020; Gao and Deng 2019) to improve the performance of information fusion. The above improved methods based on redistribution of conflicting evidence do not fully take into account the fact that each piece of evidence has different degrees of reliability To solve this problem, a new combination method for multisensor conflicting information is proposed in this paper.
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