Proceedings Article10.1117/12.179066
Maximum likelihood method for probabilistic multihypothesis tracking
Roy L. Streit,Tod Luginbuhl +1 more
- 06 Jul 1994
- Vol. 2235, pp 394-405
233
TL;DR: In this paper, a strictly probabilistic approach to the measurement-to-track assignment problem is taken and the PMHT algorithm is taken, which is computationally practical because it requires neither enumeration of measurement- to-track assignments nor pruning.
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Abstract: In a multi-target multi-measurement environment, knowledge of the measurement-to-track assignments is typically unavailable to the tracking algorithm. In this paper, a strictly probabilistic approach to the measurement-to-track assignment problem is taken. Measurements are not assigned to tracks as in traditional multi-hypothesis tracking (MHT) algorithms; instead, the probability that each measurement belongs to each track is estimated using a maximum likelihood algorithm derived by the method of Expectation-Maximization. These measurement-to-track probability estimates are intrinsic to the multi-target tracker called the probabilistic multi-hypothesis tracking (PMHT) algorithm. Unlike MHT algorithms, the PMHT algorithm does not maintain explicit hypothesis lists. The PMHT algorithm is computationally practical because it requires neither enumeration of measurement-to-track assignments nor pruning.
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References
An Empirical Bayes Approach to Statistics
Herbert Robbins
- 01 Jan 1956
TL;DR: In this paper, a random variable with a priori distribution function is considered, and a probability distribution depending in a known way on an unknown real parameter A, where A is assumed to have discrete values.
Probabilistic Multi-Hypothesis Tracking
Roy L. Streit,Tod Luginbuhl +1 more
- 15 Feb 1995
TL;DR: This study is a probabilistic approach to the measurement-to-track assignment problem, where measurements are not assigned to tracks as in traditional multi-hypothesis tracking algorithms; Instead, the probability that each measurement belongs to each track is estimated using a maximum a posteriori (MAP) method.
A maximum likelihood approach to data association
TL;DR: In this paper, an approach is presented to data association (DA) problems for which measurements are independent from scan to scan, and it is demonstrated that maximum likelihood estimation of target parameters may be efficiently implemented by an EM iterative scheme, applied to multitarget trajectory estimation of constant-velocity targets from passive (bearing-only) sensors.
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