Date of Award
3-2003
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Electrical and Computer Engineering
First Advisor
Peter S. Maybeck, PhD
Abstract
The problem of tracking multiple maneuvering targets in clutter naturally leads to a Gaussian mixture representation of the Provability Density Function (PDF) of the target state vector. State-of-the-art Multiple Hypothesis Tracking (MHT) techniques maintain the mean, covariance and probability weight corresponding to each hypothesis, yet they rely on ad hoc merging and pruning rules to control the growth of hypotheses.
AFIT Designator
AFIT-GE-ENG-03-19
DTIC Accession Number
ADA415317
Recommended Citation
Williams, Jason L., "Gaussian Mixture Reduction of Tracking Multiple Maneuvering Targets in Clutter" (2003). Theses and Dissertations. 4246.
https://scholar.afit.edu/etd/4246