PDA Technique for Track-to-Truth Assignment in the Presence of Sensor Biases and Small Objects

When conducting performance assessment of multi-target tracking algorithms with a realistic computer simulation or real-world sensor data, good track-to-truth assignment (TTA) is a critical component of any meaningful assessment. The presence of kinematic data for small objects that may not be observed by the sensor in the truth data poses serious challenges to the TTA. The assignment process is further complicated by the presence of sensor biases. In this paper, TTA in the presence of small objects and sensor biases is considered. When the target density is high and some objects are small, correctly assigning tracks to truth objects is challenging, and with the addition of sensor biases, the problem of correctly assigning tracks to truth objects is impossible without mitigation. The high rate of false switches of tracks between true objects greatly hinders the performance assessment of the target tracking system. In this paper, these challenges are addressed by adding probability of tracking to a probabilistic data association (PDA) technique for TTA. The computational algorithms for the implementation of the PDA technique are presented along with simulation results that confirm the effectiveness of the PDA approach in accurately estimating the sensor biases, and reducing the artificial track switches and ambiguity in the TTA.