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Proceedings Paper

Performance modeling for multisensor tracking and classification
Author(s): KuoChu Chang; Eswar Sivaraman; Martin E. Liggins
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Paper Abstract

Multisensor Fusion allow us to combine information from sensors with different physical characteristics to enhance the understanding of our surroundings and provide the basis for planning and decision-making. Much effort has been made toward the development of building different types of fusion methodologies and architectures. However, it would be desirable if we could estimate the performance of fusion systems before we implement them. This paper presents a performance model to evaluate the multisensor tracking systems where both kinematics and classification components are considered. Secifically, we focus our effort on classification performance prediction by defining the local confusion matrix and global confusion matrix and develop an analytical method to estimate the probability of correct classification over time. Simulation results that support the analytic approaches are also included.

Paper Details

Date Published: 9 August 2004
PDF: 8 pages
Proc. SPIE 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII, (9 August 2004); doi: 10.1117/12.544061
Show Author Affiliations
KuoChu Chang, George Mason Univ. (United States)
Eswar Sivaraman, George Mason Univ. (United States)
Martin E. Liggins, George Mason Univ. (United States)

Published in SPIE Proceedings Vol. 5429:
Signal Processing, Sensor Fusion, and Target Recognition XIII
Ivan Kadar, Editor(s)

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