MATLAB Implementation of Particle Filter Tracking Algorithm

This demonstration showcases a particle filter tracking implementation, an effective algorithm for monitoring moving objects. The core methodology involves distributing numerous particles around the target object and iteratively adjusting their positions based on environmental feedback measurements. Key implementation aspects include: particle initialization using Gaussian distributions around the initial target position, importance sampling with systematic resampling techniques, and weight updating through likelihood functions comparing particle states with sensor observations. During our validation process, the algorithm demonstrated excellent performance in maintaining accurate track of designated targets. The code structure incorporates modular design with separate functions for prediction, update, and resampling phases. This approach holds significant potential for practical applications such as vehicle/pedestrian tracking in autonomous driving systems, where the algorithm's probabilistic nature effectively handles sensor noise and occlusion scenarios.