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Proceedings Paper

Figures-of-merit to bridge fusion, long-term prediction, and dynamic sensor management
Author(s): Ivan Kadar; KuoChu Chang; Kristin O'Connor; Martin E. Liggins II
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Paper Abstract

Dynamic collection/sensor management (DSM) systems require the ability to plan in advance deployment and use of platforms/sensors to optimally locate and identify time critical and time sensitive ground targets (TST) at some future anticipated time. In order to provide long-term planning, track fusion based initial target kinematic and classification state estimates can be used to initialize long-term location prediction modeling (LPM) algorithms of target movement along lines-of-communications (LOC) networks given knowledge of target and LOC characteristics. In order to optimize the selection of the planned sensor mix for the anticipated target location, a fusion performance model (FPM) can be used to predict the best combination of available platforms/sensors. Given the outcome of long-term prediction and the best kinematic and classification state estimates from the fusion performance model, a ranked set Figures-of-Merits (FOMs) is developed for the DSM system focusing on optimizing target position and classification accuracies. The methodology, development, implementation and open/closed loop simulation concepts for FOMs evaluation are discussed.

Paper Details

Date Published: 9 August 2004
PDF: 7 pages
Proc. SPIE 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII, (9 August 2004); doi: 10.1117/12.544342
Show Author Affiliations
Ivan Kadar, Interlink Systems Sciences, Inc. (United States)
KuoChu Chang, George Mason Univ. (United States)
Kristin O'Connor, General Dynamics Advanced Information Systems (United States)
Martin E. Liggins II, General Dynamics Advanced Information Systems (United States)


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

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