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Proceedings Paper

Performance modeling for multisensor data fusion
Author(s): Kuo Chu Chang; Ying Song; Martin E. Liggins
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Paper Abstract

In the past, in multisensor fusion community, the research goal has been primarily focused on establishing a computational approach for fusion processing and algorithm. However, it would be very useful to be able to characterize the relationship between sensed information inputs available to the fusion system and the quality of fused information output. This will not only help us understand the fusion system performance but also provide high level performance bounds given sensor mix and quality for system control such as sensor resource allocation and estimate information requirements. This paper presents a fusion performance model (FPM) for a general multisensor fusion system. The model includes both kinematics and classification component and focuses on the two performance measures: positional error and classification error. The performance model is based on Bayesian theory and a combination of simulation and analytical approaches. Simulation results that validate the analytical performance predictions are also included.

Paper Details

Date Published: 25 August 2003
PDF: 10 pages
Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); doi: 10.1117/12.486868
Show Author Affiliations
Kuo Chu Chang, George Mason Univ. (United States)
Ying Song, George Mason Univ. (United States)
Martin E. Liggins, Veridian Systems (United States)

Published in SPIE Proceedings Vol. 5096:
Signal Processing, Sensor Fusion, and Target Recognition XII
Ivan Kadar, Editor(s)

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