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In the field of target tracking, it is often assumed that as long as a target is present and detectable, it should be “trackable.” Any failure to track a target that is generating detectable measurements is assumed to be due to a sub-optimal tracking algorithm or perhaps an algorithm that is not properly “tuned.” There seems to exist the idea that “if this knob is turned slightly, or this parameter...
The maximum-likelihood probabilistic data association (ML–PDA) tracker and the maximum-likelihood probabilistic multihypothesis (ML–PMHT) tracker are tested in their capacity as algorithms for very low observable (VLO) targets (meaning 6-dB postsignal processing or even less) and are then applied to five synthetic benchmark multistatic active sonar scenarios featuring multiple targets, multiple sources,...
The Maximum Likelihood Probabilistic Data Association (ML-PDA) tracker and the Maximum Likelihood Probabilistic Multi-Hypothesis (ML-PMHT) tracker are tested in their capacity as algorithms for very low observable targets (VLO, meaning 6 dB post-signal-processing or even less) and are then applied to five synthetic benchmark multistatic active sonar scenarios featuring multiple targets, multiple sources...
This paper provides a solution for bias estimation of multiple passive sensors using common targets of opportunity. The measurements provided by these sensors are assumed time-coincident (synchronous) and perfectly associated. Since these sensors provide only line of sight (LOS) measurements, the formation of a single composite Cartesian measurement obtained from fusing the LOS measurements from different...
The Maximum Likelihood Probabilistic Data Association (ML-PDA) tracker and the Maximum Likelihood Probabilistic Multi-Hypothesis (ML-PMHT) tracker were applied to five synthetic multistatic active sonar scenarios featuring multiple targets, multiple sources, and multiple receivers. For each of the scenarios, Monte Carlo testing was performed to quantify the performance differences between the two...
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