Paper
5 January 2004 Adaptive sensor placement for target tracking in the presence of uncertainties
Author Affiliations +
Abstract
Recently a general framework for sensor resource management, which has been shown to allow efficient and effective utilization of a multisensor system was introduced in5. The basis of this technique is to use the Posterior Cramer-Rao Lower Bound (PCRLB) to quantify and control the optimal achievable accuracy of target state estimation. In the current paper we extend this framework by addressing the issues of imperfect sensor placement and uncertain sensor movement (e.g., sensor drift). In contrast the previous work considered only the case where the sensor location is known exactly. The crucial consideration is then how these two forms of uncertainty affect the sensor management strategy. If unaccounted for, these uncertainties will render the output of the resource manager useless. We adjust the PCRLB to account for sensor location uncertainty, and we also allow for measurement origin uncertainty (missed target originated detections and false alarms). The work is motivated by the problem of tracking a submarine by adaptively deploying sonobuoys from a helicopter. Simulation results are presented to show the advantages of accounting for sensor location uncertainty within this focal domain of anti-submarine warfare. The same technique can be used for tracking using unattended ground sensors (UGS) or unmanned aerial vehicles (UAV).
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kumaradevan Punithakumar, Thiagalingam Kirubarajan, and Marcel L. Hernandez "Adaptive sensor placement for target tracking in the presence of uncertainties", Proc. SPIE 5204, Signal and Data Processing of Small Targets 2003, (5 January 2004); https://doi.org/10.1117/12.504872
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Cited by 4 scholarly publications.
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KEYWORDS
Sensors

Error analysis

Target detection

Unattended ground sensors

Motion models

Unmanned aerial vehicles

Monte Carlo methods

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