Explore MATLAB source code curated for "多传感器数据融合" with clean implementations, documentation, and examples.
Multi-Sensor Data Fusion: Applying MATLAB programming to multi-sensor data fusion with comprehensive simulation examples demonstrating practical implementation, key algorithms, and MATLAB functions for data integration and processing.
An improved Kalman filter algorithm designed for multi-sensor data fusion, featuring enhanced inconsistency handling and computational efficiency
Multi-Sensor Data Fusion: MATLAB simulation program implementing multi-sensor data fusion algorithms including Kalman filtering, particle filtering, and Bayesian networks with practical code examples
Implementation of the target tracking and satellite positioning algorithm described on page 44 of the book "Multi-Sensor Data Fusion," suitable for simulation programs with code structure and functional explanations.
Multi-Sensor Data Fusion: Algorithms, MATLAB Implementation, and Simulation Cases
Multi-Sensor Data Fusion Technology with Code Implementation Details for Target Tracking and Satellite Positioning
Multi-sensor data fusion techniques with MATLAB implementation for applications in autonomous driving, robotics navigation, and environmental monitoring