Simple MATLAB Implementation of D-S Evidence Theory Fusion Detection Method

This document presents a MATLAB-based implementation of a simplified D-S evidence theory fusion detection methodology. The Dempster-Shafer (D-S) evidence theory provides a mathematical framework for combining uncertain evidence sources and calculating the resulting belief degrees for various hypotheses. Our implementation focuses on creating an accessible approach for integrating this theoretical framework with practical detection scenarios. The program structure includes three main components: evidence input normalization, belief function calculation, and Dempster's combination rule implementation. Key MATLAB functions utilized in this implementation include: - array manipulation functions for evidence matrix handling - probability distribution normalization routines - combinatorial operations for evidence fusion We begin with an overview of D-S evidence theory fundamentals and its applications in uncertainty management. The methodology section details our fusion approach, specifically describing how to structure evidence inputs as probability mass functions and implement the orthogonal sum operation using MATLAB's matrix operations. The step-by-step implementation guide covers evidence representation, basic probability assignment (BPA) initialization, conflict coefficient calculation, and normalization procedures. The code implementation demonstrates how to handle evidence conflicts through conflict factors and illustrates the combination rule using practical MATLAB syntax examples. Special attention is given to handling degenerate cases where evidence completely conflicts. We conclude by discussing computational limitations regarding evidence frame size constraints and potential applications in multi-sensor data fusion, anomaly detection systems, and pattern recognition workflows. Future enhancements could incorporate adaptive evidence weighting and dynamic frame of discernment adjustment capabilities.