DS Evidence Theory for Information Fusion: Implementation and Applications
DS Evidence Theory applied to information fusion with implementation insights across multiple disciplines, featuring code-related descriptions and algorithm explanations.
Kalman Filter Program for Information Fusion
Kalman filter program implementation focusing on information fusion applications with code-specific technical descriptions
Multi-Sensor Data Fusion Across Multiple Time Cycles
Integrating data from multiple sensors over several time cycles to enable comprehensive decision-making through advanced algorithmic processing
Kalman Filter Based Information Fusion
Information fusion program implementing Kalman filter algorithms, providing practical understanding of Kalman filtering principles with code implementation details
Information Fusion Based on Kalman Filter with MATLAB Implementation Examples
Implementation of Kalman Filter-based Information Fusion Using MATLAB Applications
Information Fusion Filtering Algorithm for Motion Target Tracking
Information Fusion Filtering Algorithm: Constant Velocity-Acceleration-Constant Velocity Single Target Tracking Based on IMM
Bar-Shalom-Campo Fusion Algorithm for Multi-Sensor Data Integration
Information Fusion Techniques for Track-to-Track Integration Using Dual Sensors with Bar-Shalom-Campo Algorithm Implementation
Practical Example of Information Fusion Using Dynamic Matrix Control (DMC)
A practical example demonstrating how Dynamic Matrix Control (DMC) achieves desired target values for known requirements in information fusion applications. All programs have been thoroughly debugged and validated. This example includes implementation details about DMC's predictive control algorithm, step response modeling, and optimization calculations.
Machine Learning Source Code Based on Information Fusion
GentleBoost Algorithm Implementation with Information Fusion Techniques
DS Evidence Theory for Information Fusion
DS Evidence Theory for Information Fusion with Implementation Approaches