Abstract
Tracking multiple targets in a cluttered environment is a challenging task. Probabilistic multiple hypothesis tracking (PMHT) is an efficient approach for dealing with it. Essentially PMHT is based on expectation-maximization for handling with association conflicts. Linearity in the number of targets and measurements is the main motivation for a further development and extension of this methodology. In particular, the problem of track extraction and deletion is apparently not yet satisfactorily solved within this framework. A sequential likelihood-ratio (LR) test for track extraction has been developed and integrated into the framework of traditional Bayesian multiple hypothesis tracking by Gunter van Keuk in 1998. As PMHT is a multiscan approach as well, it also has the potential for track extraction. In this paper, an analogous integration of a sequential LR test into the PMHT framework is proposed. We present an LR formula for track extraction and deletion using the PMHT update formulae. The LR is thus a by-product of the PMHT iteration process, as PMHT provides all required ingredients for a sequential LR calculation. Therefore, the resulting update formula for the sequential LR test affords the development of track-before-detect algorithms for PMHT. The approach is illustrated by a simple example.
Highlights
The problem of tracking multiple targets in a realistic environment has been an object of research for a long time
As a path in a hypothesis tree spans all time scans, from the past up to the present, Bayesian multiple hypothesis tracking (MHT) is counted among the multiscan approaches
As already pointed out in [14], the standard Probabilistic multiple hypothesis tracking (PMHT) suffers from the so-called hospitality problem: the association weights wtlns are normalized with respect to the targets
Summary
The problem of tracking multiple targets in a realistic environment has been an object of research for a long time. The traditionalapproaches to multiple hypothesis tracking (MHT) rely on the complete enumeration of all possible associationinterpretations of a series of measurements [1] These Bayesian MHT algorithms use a hard association model which (in the case of point targets) realistically implies that a target can produce at most one measurement at a time. PMHT works on a sliding data window (multiscan), and exploits the information of previous and following time scans in every of its kinematic state estimations. Working on a sliding data window, PMHT takes the information of previous and following time scans into account As it is a multiscan approach, it has the potential for track extraction. We derive an LR formula for sequential track extraction by PMHT Using this formula the LR is a by-product of the iteration process on the PMHT data window.
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