A Robust DS Combination Method Based on Evidence Correction and Conflict Redistribution

Abstract

To eliminate potential evidence conflicts, an effective and accurate DS combination method is addressed in this paper. DS evidence theory is an outstanding information fusion approach with valid uncertainty treatment. Nevertheless, there are some limitations of the usage of the DS evidence theory. On the one hand, due to the complexity of a combat measurement environment and the inconsistency of sensor capabilities, sensor sources have enormous uncertainty, which would inevitably cause conflicts for evidence combination. On the other hand, DS combination rule realizes the unity property of fusing results with a compulsive normalization, which unavoidably leads to conflicting situations. To solve the possible evidence conflicts in a multisensor fusion system, we raise a robust DS combination method based on evidence correction and conflict redistribution. Firstly, two corrected indexes-the reliability index and consistency index-are separately addressed with the introduction of the Matusita distance function and closeness degree function. After the evidence modification based on two correction indexes, the conflicts caused by unreliable sensor sources are solved. Then, based on the corrected evidences, we put forward a weighted assignment of conflicting mass where the weight index lies on the evidence credibility. As the normalization step is abolished, the conflict redistribution strategy avoids the conflicts caused by straightforward normalization. Through comprehensive conflict management, the proposed DS combination method can not only guarantee the rationality and availability of fusing results, but also enhance the reliability and robustness of a multisensor system. Finally, three combination experiments with different conflicting degrees illustrate the advantage and superiority of the novel combination method for conflict management. Consequently, the innovation of the novel algorithm is verified.

Paradox Elimination in Dempster–Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion

“…These counterintuitive phenomena of the DS theory are called paradoxes. According to Reference [11], there are mainly three types of paradoxes.…”

“…If methods 1 and 2 are combined, then the inherent paradoxes of DS rule are solved. Building on this idea, Lin et al [24] and Ye Fang et al [11] published several new improvements of original DS combination rule. They improved the fusion results, but the results were often too complicated and overengineered to apply for real-time use.…”

Multisensor Fusion Method Based on the Belief Entropy and DS Evidence Theory

“…The second type of algorithm focuses on preprocessing the original evidence. Algorithms of this type include Murphy [52], Han et al [53], Zhang et al [54], Yuan et al [55], Xiao [15], Radim and Prakash [56], Ye et al [57], Khan and Anwar [58], and Ma and An and Jiang et al [59,60]. Murphy [52] averaged multiple sets of evidence without considering the correlation between the sets of evidence.…”

“…The method defined the sum of an expected value of Shannon's entropy as a measure of conflict and the expected value of Hartley's entropy as a measure of nonspecificity. Ye et al [57] proposed a robust DS combination method based on evidence correction and conflict redistribution. The Matusita distance function and closeness degree function were combined to modify the evidence.…”

Time-Domain Data Fusion Using Weighted Evidence and Dempster–Shafer Combination Rule: Application in Object Classification

“…Few studies considered the influence of time factor on time-domain evidence combination. Hong and Lynch [7] showed multiple approaches of how original DS method can be applied to time-domain, but no steps to improve the limitations of the original DS method [8] is mentioned. Song et al [9,10] proposed credibility decay model based on the idea that credibility of the evidence will decay over time.…”