期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2015
卷号：2015
DOI：10.1155/2015/987526
出版社：Hindawi Publishing Corporation
摘要：We propose two constant-false-alarm-rate (CFAR) decision fusion approaches, the low-SNR and likelihood-ratio-based decision fusion in the central limit theory (LLDFCLT) and high-SNR and likelihood-ratio-based decision fusion in Kaplan-Meier estimator (HLDFKE). They are based on the clustered RSN model which combines clustering structure, target detection model, and fusion scheme. We mainly apply the clustering performances by low energy adaptive clustering hierarchy (LEACH) and hybrid energy-efficient distributed clustering approach (HEED) to RSN. Their CFAR detection performances in LLDFCLT and HLDFKE are analyzed and compared. Our analyses are verified through extensive simulations in different CFARs and various numbers of initial RSs and residual RSs in RSN. Monte Carlo simulations show that LLDFCLT can provide higher probability of detection (PD) than HLDFKE; and compared to LEACH, HEED not only prolongs the lifetime of ad hoc RSN but also improves target detection performances for different CFARs.
