Evaluation of data association techniques in a real multitarget radar tracking environment

This paper assesses tracking performance of a number of commonly-used data association techniques, including the nearest-neighbor (NN) data association with optimal and sub-optimal assignments, the weighted-average and nearest-neighbor version of the probabilistic data association (PDA), joint probabilistic data association (JPDA), cheap JPDA, and sub-optimal JPDA. The real radar tracking data used for the performance evaluation in this paper contain multiple maneuvering and non-maneuvering air targets in various clutter conditions. The study shows that all the data association methods perform well when the targets are well separated with near straight-line trajectories. In the case of closely spaced and maneuvering targets, the NN and NN version of JPDA methods are more effective than the weighted-average PDA and JPDA methods.