Paper Info
Title
Accurate Analysis of Weighted Centroid Localization
Abstract
Source localization of primary users (PUs) is a spectrum awareness feature that can be very useful in enhancing the functionality of cognitive radios (CRs). When the cooperating CRs have limited information about the PU, weighted centroid localization (WCL) based on received signal strength measurements represents an attractive low-complexity solution. This paper proposes a new analytical framework to accurately calculate the performance of WCL based on the statistical distribution of the ratio of two quadratic forms in normal variables. In particular, we derive an analytical expression for the root mean square error and an exact expression for the cumulative distribution function of the two-dimensional location estimate. The proposed framework accounts for the presence of independent and identically distributed shadowing as well as correlated shadowing with distance-dependent intensity. The methodology is general enough to include the analysis of the one-dimensional error, which leads also to the evaluation of the bias of the position estimate. Numerical results confirm that the analytical framework is able to predict the performance of WCL capturing all the essential aspects of propagation as well as CR network spatial topology.
Year
DOI
Venue
2019
10.1109/TCCN.2018.2874452
IEEE Transactions on Cognitive Communications and Networking
Keywords
Field
DocType
Shadow mapping,Sensors,Estimation,Covariance matrices,Weight measurement,Root mean square,Wireless sensor networks
Computer science,Quadratic form,Algorithm,Mean squared error,Shadow mapping,Real-time computing,Cumulative distribution function,Root mean square,Independent and identically distributed random variables,Wireless sensor network,Cognitive radio
Journal
Volume
Issue
ISSN
5
1
2332-7731
Citations 
PageRank 
References 
3
0.43
0
Authors
4
Name
Order
Citations
PageRank
Kagiso Magowe161.86
Andrea Giorgetti224015.57
Sithamparanathan Kandeepan31028.26
Xinghuo Yu43954300.63