Implementation of Two Constant Velocity Targets Track-to-Track Association Using JPDA Data Association Algorithm

This document presents the implementation of Joint Probability Data Association (JPDA) algorithm for associating point measurements with tracks for two targets moving with constant velocity. JPDA is a widely used data association method that employs joint probability density functions to correlate multiple observations, thereby improving the accuracy of target position and velocity estimation. The algorithm effectively handles measurement errors and uncertainties, resulting in more robust and reliable association outcomes. In practical implementation, JPDA can be integrated with Kalman filtering techniques to enhance both the precision and efficiency of data association. The typical implementation involves calculating association probabilities for all possible measurement-to-track pairs, followed by probabilistic weighting of Kalman filter updates. Key functions would include measurement gating, probability calculation for valid associations, and combinatorial optimization for joint association events. This makes JPDA particularly valuable in applications such as target tracking systems and radar signal processing, where multiple targets need to be tracked simultaneously in cluttered environments.