Accession Number : ADA298501
Title : Probabilistic Multi-Hypothesis Tracking
Descriptive Note : Final rept.,
Corporate Author : NAVAL UNDERWATER SYSTEMS CENTER NEWPORT RI
Personal Author(s) : Streit, Roy L. ; Luginbuhl, Tod E.
PDF Url : ADA298501
Report Date : 15 FEB 1995
Pagination or Media Count : 52
Abstract : In a multitarget, multimeasurement environment, knowledge of the measurement-to-track assignments is typically unavailable to the tracking algorithm. This study is a probabilistic approach to the measurement-to-track assignment problem. 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 a posteriori (MAP) method. These measurement-to-track probability estimates are intrinsic to the multitarget tracker called the probabilistic multi-hypothesis tracking (PMHT) algorithm. The PMHT algorithm is computationally practical because it requires neither enumeration of measurement-to-track assignments nor pruning. The PMHT algorithm is an optimal MAP multitarget tracking algorithm. (AN)
Descriptors : *MATHEMATICAL MODELS, *ALGORITHMS, *MULTIPLE TARGETS, *TRACKING, OPTIMIZATION, COMPUTATIONS, MAXIMUM LIKELIHOOD ESTIMATION, RANDOM VARIABLES, MATRICES(MATHEMATICS), STATISTICAL INFERENCE, PROBABILITY, STATISTICAL DATA, SIGNAL TO NOISE RATIO, NONPARAMETRIC STATISTICS, APPROXIMATION(MATHEMATICS), CONVERGENCE, DISCRETE DISTRIBUTION, COVARIANCE, HYPOTHESES, BAYES THEOREM, STATISTICAL PROCESSES, BACKGROUND NOISE.
Subject Categories : Operations Research
Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE