Abstract
In this paper, we propose to use information theory to automatically select the best sensors in a Ultra Wide Band (UWB) Radar Sensor Networks (RSN) to detect target in foliage environment. Information theoretic algorithms such as entropy and mutual information are proven methods that can be applied to data collected by various sensors for target detection. However, the complexity of the environment brings uncertainty in fusion center and the big data collected by sensors can have huge processing load. In this paper, we propose to use another information theoretical criterion known as Chernoff information that can provide the best error exponent of detection in Bayesian approach. We also used Chernoff Stein Lemma for fusing the data to optimize the performance. The performance of the algorithm was evaluated, based on real world data. Results show that our opportunistic sensing (OS) algorithm does efficient utilization of sensing assets and provide same performance while it is compared with the existing method without OS.
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References
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Maherin, I., Liang, Q. (2014). Information Theory Based Opportunistic Sensing in Radar Sensor Networks. In: Cai, Z., Wang, C., Cheng, S., Wang, H., Gao, H. (eds) Wireless Algorithms, Systems, and Applications. WASA 2014. Lecture Notes in Computer Science, vol 8491. Springer, Cham. https://doi.org/10.1007/978-3-319-07782-6_63
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DOI: https://doi.org/10.1007/978-3-319-07782-6_63
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07781-9
Online ISBN: 978-3-319-07782-6
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