The ML-PMHT Multistatic Tracker for Sharply Maneuvering Targets
Abstract
The maximum likelihood probabilistic multi-hypothesistracker (ML-PMHT) is applied to a benchmark multistaticactive sonar scenario with multiple targets, multiple sources,and multiple receivers. We first compare the performance ofthe tracker on this scenario when it is applied in Cartesianmeasurement space, a typical implementation for manytrackers, against its performance in delay-bearing measurementspace, where the measurement uncertainty is more accuratelyrepresented. ML-PMHT is a batch tracker, and the motion ofa target being tracked must be given a parameterization thatdescribes the motion of the target throughout the batch. In thescenario in which we apply the tracker, the majority of targetreturns have low amplitudes (i.e., the targets are low-observable),which makes the choice of a batch tracker very appropriate. Inprior work, ML-PMHT was implemented with a straight-lineparameterization to describe target motion. However, in orderto track maneuvering targets, the tracker was implemented in asliding-batch fashion under the assumption that a maneuveringtrack could be approximated as a series of short straight lines.Here, we augment the straight-line parameterization by amaneuvera single course change within the batchthat allows ML-PMHT to follow even sharply maneuvering targets, and weapply it in both Cartesian and delay-bearing measurement space.We also implement this maneuvering-model parameterizationwith both a fixed batch-length implementation as well as avariable batch-length implementation. Finally, we developan expression for the Cramer-Rao lower bound (CRLB) forthe maneuvering-model parameterization and show that theML-PMHT tracker with the maneuvering-model parameterizationis an efficient estimator.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2013
- Accession Number
- AD1019674
Entities
People
- Peter Willett
- Steven Schoenecker
- Yaakov Bar-Shalom
Organizations
- University of Connecticut