Simple D-S Evidence Theory Fusion Detection Method MATLAB Implementation

D-S Evidence Theory (Dempster-Shafer Theory) is an effective method for handling uncertainty and incomplete information, widely applied in multi-source data fusion and decision support systems. This article demonstrates how to implement a simple D-S evidence theory fusion detection method in MATLAB.

Implementation Approach: The first step involves defining Basic Probability Assignment (BPA) functions, which quantify the degree of support each evidence provides to different hypotheses. Next, implement Dempster's combination rule to merge BPA functions from multiple evidence sources. Finally, apply decision rules (such as maximum belief or minimum uncertainty) to reach final conclusions.

Core Steps Breakdown: BPA Initialization: Assign support values for each hypothesis from every evidence source using probability mass functions. Conflict Coefficient Calculation: Quantify contradictions between different evidence sources using conflict measures. Normalization Processing: Redistribute conflict portions to non-conflicting hypotheses proportionally through Dempster's rule. Decision Output: Compare fused belief function values and select the most credible hypothesis.

Application Scenarios: This method is particularly suitable for sensor networks, fault diagnosis, and other scenarios requiring integration of multi-source uncertain information. MATLAB's matrix computation capabilities efficiently handle combinatorial calculations in evidence theory, while its visualization functions facilitate analysis of fusion results. Key MATLAB functions like matrix operations and probability distribution tools can be leveraged for implementing BPA calculations and combination rules.