A Comparison of Multiple-Model Target Tracking Algorithms

There are many multiple-model (MM) target-tracking algorithms that are available but there has yet to be a comparison that includes all of them. This work compares seven of the currently most popular MM algorithms in terms of performance, credibility, and computational complexity. The algorithms to be considered are the autonomous multiple-model algorithm, generalized pseudo- Bayesian of first order, generalized pseudo-Bayesian of second order, interacting multiple-model algorithm, B-Best algorithm, Viterbi algorithm, and reweighted interacting multiple-model algorithm. The algorithms were compared using three scenarios consisting of maneuvers that were both in and out of the model set. Based on this comparison, there is no clear-cut best algorithm but the B-best algorithm performs best in terms of tracking errors and the IMM algorithm has the best computational complexity among the algorithms that have acceptable tracking errors.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical Engineering

Year

2004

Author dc:creator

• Pitre, Ryan

Contributors dc:contributor

• Li, X. Rong
• Chen, Huimin
• Jilkov, Vesselin

• Target tracking
• Multiple model

Repository record dc:identifier

https://scholarworks.uno.edu/td/193

OAI identifier oai:identifier

oai:scholarworks.uno.edu:td-1197