Robot Localization via WiFi Signal Measurements Using Particle Filtering

This MATLAB program demonstrates robot tracking through particle filtering. WiFi measurements are simulated using a ray-tracing engine that accommodates up to 3 wall reflections. The particle filter algorithm is employed to correct distance estimates and trajectory calculations. During the robot tracking process, WiFi signal measurement data enables more accurate computation of the robot's position and movement trajectory, thereby improving localization precision. The program implements a ray-tracing engine to simulate WiFi signal propagation, incorporating reflections from up to 3 walls in the simulation environment. The particle filter algorithm processes measurement data through iterative weight updates and resampling techniques to correct positioning errors. Key implementation aspects include: - Ray-tracing simulation for multipath signal propagation modeling - Particle filter with systematic resampling for state estimation - Measurement correction through likelihood functions based on signal strength - Trajectory smoothing using sequential Monte Carlo methods The combination of these technologies results in more accurate and reliable robot tracking, delivering improved performance in practical applications. The implementation showcases how wireless signal processing and statistical filtering can enhance indoor localization systems.