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SENSOR SCHEDULING AND EFFICIENT ALGORITHM IMPLEMENTATION FOR TARGET TRACKING (2006)
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BibTeX
@MISC{Chhetri06sensorscheduling,
author = {Amit Singh Chhetri},
title = {SENSOR SCHEDULING AND EFFICIENT ALGORITHM IMPLEMENTATION FOR TARGET TRACKING},
year = {2006}
}
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Abstract
Recent advances in sensor technology coupled with embedded systems and wireless networking has made it possible to deploy sensors for numerous applications including target tracking, environmental science, defense information, and security. Sensor scheduling, a process to allocate sensing resources by optimizing a performance metric over a future time-horizon under constraints, is an effective method to improve performance for such problems. This work investigates myopic (one step ahead) and non-myopic (multiple steps ahead) sensor scheduling algorithms for target tracking applications. Two methods of predicting tracker performance are developed that can be used for tar-get tracking applications. The first is covariance-based, and it can be used with covariance-based scheduler costs. The second is unscented transform-based and it can be used with arbitrary scheduler costs. In application, both methods give a significant improvement in tracking performance over the tracking performance without sensor scheduling. The use of non-myopic sensor scheduling is often restricted due to an exponential dependency of computational and memory requirements on the length of prediction horizon. For
Keyphrases
sensor scheduling future time-horizon exponential dependency significant improvement sensor technology target tracking application recent advance wireless networking numerous application non-myopic sensor scheduling arbitrary scheduler cost environmental science tar-get tracking application tracker performance memory requirement effective method target tracking covariance-based scheduler cost tracking performance prediction horizon sensing resource embedded system multiple step defense information
