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We consider the problem of localizing spectrally overlapped sources incognitive radio networks. A new weighted centroid localization algorithm (WCL)called Cyclic WCL is proposed, which exploits the cyclostationary feature ofthe target signal to estimate its location coordinates. In order to analyze thealgorithm in terms of root-mean-square error (RMSE), we model the locationestimates as the ratios of quadratic forms in a Gaussian random vector. Withanalysis and simulation, we show the impact of the interferer location and itsmodulation scheme on the RMSE. We also study the RMSE performance of thealgorithm for different power levels of the target and the interference.Further, the comparison between Cyclic WCL and WCL w/o cyclostationarity ispresented. It is observed that the Cyclic WCL provides significant performancegain over WCL.

Cyclic weighted centroid localization for spectrally overlapped sources in cognitive radio networks