Developed over 15 years ago, the maximum-likelihood-probabilistic data association target tracking algorithm has been demonstrated to be effective in tracking very low observable (VLO) targets where target signal-to-noise ratios (SNRs) require very low detection processing thresholds to reliably give target detections. However, this algorithm has had limitations, which in many cases would preclude use in real-time tracking applications. In this paper, we describe three recent advances in the ML-PDA algorithm which make it suitable for real-time tracking. First we look at two recently reported techniques for finding the ML-PDA track estimate which improves computational efficiency by one order of magnitude. Next we review a method for validating ML-PDA track estimates based on the Neyman-Pearson lemma which gives improved reliability in track validation over previous methods. As our main contribution, we extend ML-PDA from a single-target tracker to a multitarget tracker and compare its performance to that of the probabilistic multihypothesis tracker (PMHT).