Data Fusion Using D-S Evidence Theory with Algorithm Implementation

Data fusion using D-S evidence theory is applied to wireless sensor network target localization and decision-level fusion. Data fusion refers to the process of integrating and analyzing information from different sensors to improve target localization accuracy and reliability. Wireless sensor networks consist of numerous distributed sensor nodes that can perceive and collect environmental data. Target localization involves determining target positions by analyzing collected data. Decision-level fusion builds upon target localization by synthesizing data from multiple sensors along with auxiliary information to perform higher-level decision-making and inference.

In implementation, the D-S evidence theory framework typically requires defining basic probability assignments (BPAs) for each sensor's evidence, where mass functions represent uncertainty intervals for target hypotheses. The core algorithm involves Dempster's combination rule to merge evidence from multiple sources: m_1,2(A) = K^-1∑_B∩C=Am₁(B)m₂(C), where K represents the conflict coefficient. Key functions include evidence normalization, conflict resolution mechanisms, and belief/plausibility calculation functions. Practical implementation often involves MATLAB or Python code structures handling evidence matrices, combination iterations, and decision thresholds for hypothesis selection.

Therefore, applying D-S evidence theory for data fusion enhances wireless sensor network target localization performance and enables effective decision-level fusion applications through mathematical evidence combination and uncertainty management.