Joint Probabilistic Data Association (JPDA) Algorithm for Multi-Target Tracking

This article discusses a MATLAB implementation package for multi-target tracking using the Joint Probabilistic Data Association (JPDA) algorithm. The implementation handles data association uncertainties through probabilistic weighting of measurement-to-track assignments, with core functions managing gating, validation matrix generation, and joint association probability calculations. While the current tracking performance shows acceptable accuracy, several enhancements could further improve its capabilities. For instance, expanding training datasets could refine the algorithm's statistical models and enhance tracking precision. Implementing advanced techniques like interacting multiple models (IMM) or incorporating neural networks could boost efficiency in complex scenarios. Additionally, the package's modular structure allows for adaptation to diverse applications such as healthcare monitoring systems, security surveillance, and autonomous vehicle navigation. Key functions include measurement preprocessing, Kalman filter prediction-update cycles, and hypothesis management for handling occlusions. Although the current implementation provides solid foundational performance, significant potential remains for optimization through code-level improvements like parallel processing implementation and adaptive thresholding mechanisms.