Performance evaluation of a multiple-hypothesis multi-target tracking algorithm

This study is concerned with the performance evaluation of multiple-hypothesis, multi-target tracking algorithms. Target-detection/track-initiation capabilities as measures of performance were investigated. Through Monte Carlo simulations, a multiple-hypothesis tracking algorithm was evaluated in terms of: probability of establishing a track from target returns; and false track density. A radar was chosen as the sensor, and a general-purpose multiple-hypothesis, multitarget tracking algorithm, called generalized tracker/classifier, was used in the Monte Carlo simulations