Simulation Program for Multi-Target Tracking Based on JPDAF

This paper presents a simulation program for multi-target tracking based on the Joint Probabilistic Data Association Filter (JPDAF) algorithm. The program demonstrates tracking of two targets as an example case, implementing sensor data fusion from multiple sources to accurately track and localize targets. The core algorithm handles measurement-to-track association probabilities jointly across all targets, overcoming limitations of single-target tracking methods. The implementation includes Kalman filter prediction and update steps with probabilistic data association weights. This simulation framework can be applied not only in military domains but also in civilian applications such as autonomous driving systems and intelligent security surveillance. By continuously optimizing the association algorithm and increasing the number of sensors in the simulation configuration, the tracking accuracy and real-time performance can be further enhanced through improved measurement validation and hypothesis management.