Information Theory Based Opportunistic Sensing in Radar Sensor Networks

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• 作者：Ishrat Maherin (18)
  Qilian Liang (18)
  
• 关键词：Target detection ; opportunistic sensing ; UWB ; Radar Sensor Networks ; Chernoff information ; sense ; through ; foliage and entropy
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8491
• 期：1
• 页码：706-717
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  2. Maherin, I., Liang, Q.: A mutual information based approach for target detection through foliage using UWB radar. In: IEEE Int. Conf. Commun. (ICC), June 10-15, pp. 6406鈥?410 (2012)
  3. Fabeck, G., Mathar, R.: Chernoff information-based optimization of sensor networks for distributed detection. In: IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), December 14-17, pp. 606鈥?11 (2009)
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  8. Liang, Q.: Automatic Target Recognition Using Waveform Diversity in Radar Sensor Networks. Pattern Recognition Letters (Elsevier)聽29(2), 377鈥?81 (2008)
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  10. Carbunar, B., Grama, A., Vitek, J., Carbunar, O.: 鈥淐overage Preserving Redundancy Elimination in Sensor Networks. In: IEEE SECON 2004 (October 2004)
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  13. Liang, Q., Cheng, X., Huang, S., Chen, D.: Opportunistic Sensing in Wireless Sensor networks: Theory and Application. Accepted by IEEE Trans. on Computers
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  16. Dill, C.: Foliage Penetration (phase II) Field Test Narrow band vesus Wideband Foliage Penetration. Final report of contract number F41624-03-D-700/04 (July to February 2006)
  17. Liang, J., Liang, Q.: Sense-through-foliage target detection using uwb radar sensor networks. Pattern Recognition Letters聽31, 1412鈥?421 (2010) CrossRef
  18. Liang, Q., Samn, S.W., Cheng, X.: UWB radar sensor networks for sensethrough-foliage target detection. In: IEEE International Conference on Communications, pp. 2228鈥?232 (2008)
  
• 作者单位：Ishrat Maherin (18)
  Qilian Liang (18)
  
  18. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, 76019-0016, USA
  
• ISSN：1611-3349

文摘

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.