Research for Multi-sensor Information Fusion Algorithm of Search and Rescue Robot Based on Embedded Control Network

Wang, Peng; Jiang, Wenhao; Li, Xin; Kang, Shaochen; Xin, Jinglei

doi:10.4304/jcp.7.5.1176-1183

Cited by 1 publication

(1 citation statement)

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“…The versatility of the D-S theory has led to its successful application in various domains. It has been effectively employed in decision-making processes [9], pattern recognition tasks [14,15], risk assessment [18], target tracking [20], complex mass function analysis [21], fault diagnosis [22,23], expert systems [24,25], complex network analysis [26][27][28][29], information fusion [30], and other hybrid fields [6,31]. Its broad range of applications demonstrates the efficiency and validness of the D-S theory in solving diverse problems.…”

Section: Introductionmentioning

confidence: 99%

Leveraging a New Base Belief Function for Conflict Data Management and Decision Support

Dutta,

Shome

2023

Preprint

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The Dempster-Shafer theory (D-S theory) finds wide-ranging applications, including pattern recognition and decision making. When paradoxes exists in multi-source information system due to unfavorable environment, unification cannot be reliably and effectively performed by traditional D-S theory. In light of this, a fresh perspective is taken to construct a base belief function for conflict data fusion within the D-S theory. In this novel approach, a new base belief function is introduced to modify the original basic probability assignment (BPA) prior to applying the classical Dempster combination rule (DCR) in a closed world scenario. Instead of assigning initial belief to the entire power set of the Frame of Discernment(FOD), the proposed method assigns base belief to individual events. This targeted approach aims to reduce the existing uncertainty within the FOD. To generate more intuitive outcomes, the proposed method combines the base belief function with the original BPA, thus incorporating all the pertinent information contained in the evidence set (BOE). Compared to other alternative methods, the proposed approach demonstrates consistent and logical outcomes that align with the real world. Furthermore, it exhibits a high rate of convergence and lower computational complexity. An advantageous aspect of the proposed method is its utilization of maximum information from the evidence set, not limited to an average operation between the base belief function and the initial BPA. This comprehensive integration not only resolves conflicts within the evidence set but also generates effective outcomes. To validate the efficaciousness of the proposed method, several numerical examples are presented, along with its application to classification problems. These instances substantiate the effectiveness of the approach in practice.

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