Association of Plots and Tracks for Two Uniformly Moving Targets

Data association represents the core challenge in multi-target tracking systems, particularly when targets exhibit similar motion patterns. The Joint Probabilistic Data Association (JPDA) algorithm effectively addresses the plot-to-track association problem for two uniformly moving targets.

For uniformly moving targets, the motion model demonstrates linear characteristics. The algorithm initially predicts potential position regions (association gates) for the next time step based on current track states. It then calculates association probabilities between individual plots and tracks. JPDA's innovation lies in considering joint probabilities where plots may originate from different targets or clutter, updating track states through weighted fusion techniques.

Implementation requires careful attention to association gate configuration - gates must encompass legitimate target plots while minimizing clutter interference. For uniformly moving targets with constant velocity, prediction regions can be accurately computed using linear motion models. The algorithm ultimately outputs correspondences between plots and tracks, along with updated target motion states. Key implementation aspects include Kalman filter integration for state prediction and gating functions using Mahalanobis distance calculations to determine valid association candidates.