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Cognitive radio is a way of opportunistically sharing the scarce spectrum among licensed and unlicensed users of the spectrum. One of the key challenges in deploying cognitive radio networks (CRNs) is to find out the spectrum holes in the primary (licensed) wireless systems for allowing the secondary (unlicensed) users to share. In order to detect spectrum holes, CRN needs to sense the spectrum to find whether the primary user (PU) is available. In this paper, a cooperative algorithm to estimate the PU location and its coverage, based on received signal strength (RSS) at several local secondary users (SUs) locations, is presented. Maximum likelihood (ML) algorithm is used to estimate the PU locations and its transmission power. Locations of SUs are estimated using Time Difference of Arrival (TDOA) method and the cooperation among the Cognitive Radio Base Stations (CBSs) of CRN. For the convenience of analysis we take error as Euclidian distance between actual location and estimated location. The performance evaluation uses the mean error as a metric.