Multi-Sensor Data Fusion Algorithm Implementation

Page 44 of the book "Multi-Sensor Data Fusion" introduces an algorithm specifically designed for target tracking and satellite positioning applications. This algorithm can be effectively implemented and demonstrated through simulation programs, which typically involve several key development phases: coding the core algorithm logic, establishing mathematical models, processing multi-sensor data streams, and analyzing performance results. A typical implementation would include: - Sensor data preprocessing functions to handle noise filtering and coordinate transformation - Fusion algorithms (such as Kalman filters or particle filters) for combining heterogeneous sensor inputs - Tracking modules with prediction and correction mechanisms for maintaining target trajectories - Visualization components for real-time monitoring and result validation The simulation development requires careful planning and systematic execution of these components. Furthermore, the algorithm's applications extend beyond the original scope to emerging fields like robotic navigation and autonomous driving systems. Mastering this algorithm's implementation methodology provides strong technical support for research and practical applications in related domains, particularly when dealing with complex multi-sensor environments requiring high-precision positioning and robust tracking capabilities.