  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, 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 the 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.
