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