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A sequential probability ratio test (SPRT) is introduced as a track management (TM) mechanism for the joint probabilistic data association (JPDA) multitarget tracking algorithm. The SPRT is based on an approximate target likelihood ratio that is derived from the JPDA event and association probabilities. SPRT-based TM functions are defined and compared to the joint integrated probabilistic data association (JIPDA) algorithm, including a discussion of parameter tuning for both approaches. Simulation experiments are reported for multiple closely spaced constant-velocity targets over a range of values for detection probability and clutter density. The SPRT-based algorithm achieves performance similar to JIPDA for this data, but with fewer TM parameters and a more straightforward parameter-tuning process. [ABSTRACT FROM AUTHOR]

The Maximum Likelihood Probabilistic Multi-Hypothesis Tracker (ML-PMHT) can be used as a powerful multisensor, low-observable, multitarget active tracker. It is a non-Bayesian algorithm that uses a generalized likelihood ratio test (GLRT) to differentiate between clutter and targets. We use a new method, initially developed to obtain the probability density function (pdf) of the maximum point in theML-PMHT log-likelihood ratio (LLR) due to clutter, to now develop a pdf for the maximum value of the ML-PMHT LLR caused by a target. With expressions for the pdfs of the maximum points caused by both clutter (developed in a companion article) and a target, we can, for a given set of tracking parameters (signal-to-noise ratio, search volume, target measurement probability of detection, etc.), develop ML-PMHT "tracker operating characteristic" curves, similar to receiver operating characteristic curves for a detector. Since ML-PMHT can be thought of as an optimal algorithm in the sense that, as long as the target and the environment match the algorithm?s assumptions, all the information from all the available measurements can be used, and no approximations are necessary to get the algorithm to function, the analysis presented in this paper offers for the first time part of the answer to the fundamental question: Can a particular target be tracked? [ABSTRACT FROM AUTHOR]

This paper presents a modified version of the probabilistic data association filter (PDAF) algorithm for single target tracking which allows for measurement covariances to vary within, as well as between, time scans. This modification enhances the ability to track targets in situations where the measurement errors within a time scan are highly variable. The potential for improved tracking is demonstrated by comparing the performance of the modified and the standard algorithm in tracking simulated intensity detector measurements. [ABSTRACT FROM AUTHOR]