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Proceedings Paper

Adaptive sensing for target tracking in covert operations
Author(s): Patricia R. Barbosa; Edwin K. P. Chong
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Paper Abstract

Multisensor applications rely on effectively managing sensor resources. In particular, next-generation multifunctional agile radars demand innovative resource management techniques to achieve a common sensing goal while satisfying resource constraints. We consider an active sensing platform where multiple waveform-agile radars scan a hostile surveillance area for targets. A central controller adaptively selects which transmitters should be active and which waveforms should be transmitted. The controller's goal is to choose the sequence of (transmitter, waveform) pairs that yields the most accurate tracking estimate. We formulate this problem as a partially observable Markov decision process (POMDP), and propose a novel "two-level" scheduling scheme that uses two distinct schedulers: (1) at the lower level, a myopic waveform scheduler; and (2) at the upper level, a non-myopic transmitter scheduler. Scheduling decisions at these two levels are carried out differently. While waveforms are updated at every radar scan, a new set of transmitters only becomes active if the overall tracking accuracy falls below a given threshold, or if the "detection risk" is exceeded, given by a limit on the number of consecutive scans during which a set of transmitters is active. By simultaneously exploiting myopic and non-myopic scheduling schemes, we benefit from trading off short-term for long-term performance, while maintaining low computational costs. Moreover, in certain situations, the myopic scheduling of waveforms at each radar scan improves on non-myopic actions taken in the past. Monte Carlo simulations are used to evaluate the performance of the proposed adaptive sensing scheme in a multitarget tracking setting.

Paper Details

Date Published: 11 May 2009
PDF: 10 pages
Proc. SPIE 7336, Signal Processing, Sensor Fusion, and Target Recognition XVIII, 73360A (11 May 2009); doi: 10.1117/12.818357
Show Author Affiliations
Patricia R. Barbosa, Colorado State Univ. (United States)
Intermap Technologies Inc. (United States)
Edwin K. P. Chong, Colorado State Univ. (United States)

Published in SPIE Proceedings Vol. 7336:
Signal Processing, Sensor Fusion, and Target Recognition XVIII
Ivan Kadar, Editor(s)

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