Abstract-This paper addresses the problem of localizing a non-cooperative transmitter in the presence of a spectrally overlapped interferer in a Cognitive Receiver (CR) network. It has been observed that the performance of non-cooperative Weighted Centroid Localization (WCL) algorithm degrades in the presence of a spectrally overlapped interferer. We propose Cyclic WCL algorithm that uses cyclic autocorrelation (CAC) of received signals at CRs in the network to estimate the location coordinates of the target transmitter. Performance of the proposed algorithm is further improved by eliminating CRs in the vicinity of the interferer from the localization process. In order to identify and eliminate CRs in the vicinity of the interferer, the ratio of the variance and the mean of the square of absolute value of the CAC, referred to as Feature Variation Coefficient (FVC), is used. Theoretical analysis of the Cyclic WCL algorithm is presented in order to compute the root mean square error in the location estimates. We further study impacts of the interferer's power and location, CR density, and fading environment on the performance of Cyclic WCL. The comparison between Cyclic WCL and traditional WCL is also presented.
In the algorithm presented in REF , WCL is observed with improvement using cyclic autocorrelation (CAC) of received signal at cognitive receivers (CRs) through eliminating CRs in the vicinity of the interference from the localization process.
Cyclic Weighted Centroid Algorithm for Transmitter Localization in the Presence of Interference
Published in IEEE Transactions on Cognitive Communications and Networking, June 2016
Mathematics Engineering Computer Science