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Proceedings Paper

Dynamic optimization of ISR sensors using a risk-based reward function applied to ground and space surveillance scenarios
Author(s): J. T. DeSena; S. R. Martin; J. C. Clarke; D. A. Dutrow; A. J. Newman
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Paper Abstract

As the number and diversity of sensing assets available for intelligence, surveillance and reconnaissance (ISR) operations continues to expand, the limited ability of human operators to effectively manage, control and exploit the ISR ensemble is exceeded, leading to reduced operational effectiveness. Automated support both in the processing of voluminous sensor data and sensor asset control can relieve the burden of human operators to support operation of larger ISR ensembles. In dynamic environments it is essential to react quickly to current information to avoid stale, sub-optimal plans. Our approach is to apply the principles of feedback control to ISR operations, "closing the loop" from the sensor collections through automated processing to ISR asset control. Previous work by the authors demonstrated non-myopic multiple platform trajectory control using a receding horizon controller in a closed feedback loop with a multiple hypothesis tracker applied to multi-target search and track simulation scenarios in the ground and space domains. This paper presents extensions in both size and scope of the previous work, demonstrating closed-loop control, involving both platform routing and sensor pointing, of a multisensor, multi-platform ISR ensemble tasked with providing situational awareness and performing search, track and classification of multiple moving ground targets in irregular warfare scenarios. The closed-loop ISR system is fullyrealized using distributed, asynchronous components that communicate over a network. The closed-loop ISR system has been exercised via a networked simulation test bed against a scenario in the Afghanistan theater implemented using high-fidelity terrain and imagery data. In addition, the system has been applied to space surveillance scenarios requiring tracking of space objects where current deliberative, manually intensive processes for managing sensor assets are insufficiently responsive. Simulation experiment results are presented. The algorithm to jointly optimize sensor schedules against search, track, and classify is based on recent work by Papageorgiou and Raykin on risk-based sensor management. It uses a risk-based objective function and attempts to minimize and balance the risks of misclassifying and losing track on an object. It supports the requirement to generate tasking for metric and feature data concurrently and synergistically, and account for both tracking accuracy and object characterization, jointly, in computing reward and cost for optimizing tasking decisions.

Paper Details

Date Published: 18 May 2012
PDF: 17 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 83920B (18 May 2012); doi: 10.1117/12.919424
Show Author Affiliations
J. T. DeSena, The Johns Hopkins Univ. Applied Physics Lab. (United States)
S. R. Martin, The Johns Hopkins Univ. Applied Physics Lab. (United States)
J. C. Clarke, The Johns Hopkins Univ. Applied Physics Lab. (United States)
D. A. Dutrow, The Johns Hopkins Univ. Applied Physics Lab. (United States)
A. J. Newman, The Johns Hopkins Univ. Applied Physics Lab. (United States)


Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)

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