Reliable Real-time Localization and Tracking of Interferers Using Cooperative Spectrum Sensing

Locating active users/transmitters and predicting interference patterns in real-time is a key challenge in next generation radio networks to enable robust wireless communication and highly dynamic radio resource allocation. We consider a network of collaborating spectrum sensing units (SUs) and propose a two-phase approach for localization and tracking of transmitters using received signal strength at SUs. The first phase involves estimation of locations of multiple transmitters using compressed sensing based method. In the second phase, the location estimates are improved and velocities of mobile transmitters are estimated using extended Kalman filter. The proposed approach is evaluated through simulations considering an industrial channel model with correlated shadowing. We investigate the effect of SU placement, transmitter density, mobility and fading on the localization performance.