Implementation of Data Fusion using Dempster-Shafer Evidence Theory
A Novel Evidence Theory-Based Combination Formula for Data Fusion, proposed by researcher Sun Quan in the Journal of Electronics.
DS Evidence Theory Case Studies: Implementation Examples for Data Fusion Decision Making
Practical examples of DS evidence theory applied to data fusion decisions, complete with implementation explanations and reference materials, demonstrating basic combination rules and conflict resolution algorithms
MATLAB Simulation Program for D-S Evidence Theory in Data Fusion
MATLAB simulation implementation of D-S evidence theory for data fusion applications with algorithm validation and performance analysis
Kalman Filter for Data Fusion
Kalman filter implementation for data fusion that performs matrix-weighted fusion of filtering results from multiple sensors to obtain precise output estimates
Data Fusion Using Bayesian Inference for Enhanced Positioning
Implementation of Bayesian inference-based data fusion to process results from CHAN and Taylor algorithms, achieving improved localization accuracy and reliability through probabilistic integration.
Integrated Navigation System: Strapdown Inertial Navigation and GPS with Kalman Filter Data Fusion
Implementation of strapdown inertial navigation and GPS integrated navigation system using Kalman filtering for multi-sensor data fusion and optimized position output
Bayesian Inference-Based Data Fusion for Enhanced Positioning
Utilizes Bayesian inference to process the outputs of CHAN and Taylor algorithms, achieving superior localization results through probabilistic data fusion. Validated through comprehensive testing with implementation involving Gaussian likelihood modeling and posterior probability optimization.
Drone Altitude Control Using MATLAB Data Fusion
Drone Altitude Control Using MATLAB Data Fusion (with Kalman Filter Implementation)
AFMR Routing Algorithm in WSN
The AFMR routing algorithm in Wireless Sensor Networks addresses energy-efficient data fusion and correlated data collection through optimized routing construction and adaptive in-node decisions on data fusion operations. By evaluating transmission energy consumption, fusion energy costs, and energy-saving gains from data aggregation algorithms, AFMR dynamically adjusts whether to perform data fusion when mobile agents migrate between nodes, achieving optimized energy expenditure across diverse application scenarios.
Multi-Sensor Data Fusion Based on Extended Kalman Filter (EKF)
Multi-Sensor Data Fusion in Clutter Environments Using Extended Kalman Filter