CFAR Decision Fusion Approaches in the Clustered Radar Sensor Networks Using LEACH and HEED

Yaoyue Hu and Jing Liang

International Journal of Distributed Sensor Networks, 2015, vol. 11, issue 10, 987526

Abstract: 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.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1155/2015/987526 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:11:y:2015:i:10:p:987526

DOI: 10.1155/2015/987526

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().