Source localization and tracking in a Cognitive Radio environment consisting of frequency and spatial mobility

Source localization and tracking of a Cognitive Radio (CR) is a significant challenge because of the dynamic opportunistic behavior of the radio across the spatial, frequency, and temporal domains. For any localization or tracking scheme to be effective, it must be able to adapt as a CR adapts to its surroundings. The extended semi range-based (ESRB) localization scheme was proposed as a solution to the aforementioned problem, but resulted in considerable communication overhead and storage requirements within the wireless sensor network (WSN) as well as poor reliability due to frequent divergence of the non-linear least squares method (NLSM) in the localization process. Furthermore, tracking a mobile CR was accomplished in a brute force manner by repeating the same positioning technique without taking advantage of prior position estimates. In this paper, the ESRB localization scheme is modified to incorporate the Kalman filter as a recursive estimator to reduce the burden placed on the WSN and integrate an efficient means to estimate the position and velocity of a mobile CR over time. The proposed modification is modeled in the MATLAB programming language, and its efficacy is demonstrated through simulation. It is shown that the Kalman filter is an appropriate recursive estimator for use in the ESRB localization scheme, while accounting for both frequency and spatial mobility inherent in the CR environment.