9 August 2004 Figures-of-merit to bridge fusion, long-term prediction, and dynamic sensor management
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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.
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Ivan Kadar, Ivan Kadar, KuoChu Chang, KuoChu Chang, Kristin O'Connor, Kristin O'Connor, Martin E. Liggins, Martin E. Liggins, } "Figures-of-merit to bridge fusion, long-term prediction, and dynamic sensor management", Proc. SPIE 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII, (9 August 2004); doi: 10.1117/12.544342; https://doi.org/10.1117/12.544342
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