Off-Line and Real-Time ML-PDA Track Validation

Abstract

We present two procedures for validating track estimates obtained using the Maximum Likelihood Probabilistic Data Association (ML-PDA) algorithm. The ML-PDA, developed for Very Low Observable (VLO) target tracking, always provides a track estimate that must then be tested for target existence by comparing the value of the Log Likelihood Ratio (LLR) at the track estimate to a threshold. Using extreme value theory, we show that in the absence of a target the LLR at the track estimate obeys approximately a Gumbel distribution rather than the Gaussian distribution previously ascribed to it in the literature. The oll-line track validation procedure relies on extensive off-line simulations to obtain a set of track validation thresholds. The real-time procedure uses the data set that produced the track estimate to also determine the track validation threshold. The performance of these two procedures is investigated through simulation of an active sonar tracking scenario by comparing the false and true track acceptance probabilities.