Data assimilation for sensing aided geolocation database

Cognitive radio systems aim to take advantage of the spatiotemporal empty spectrum without causing harmful interference towards the primary network by utilizing knowledge of the prevailing radio environment. The radio environment is typically modeled with propagation models or by interpolating spatially distributed field measurement data. This paper presents a practical online data assimilation method based on the ensemble Kalman filter for estimating the spatial correlation of the time-variant primary field strength from a collection of sensing samples. The correlation structure known as the variogram or covariance function is in turn used in the algorithms for radio environment mapping. Furthermore, it is shown that the proposed method provides significant reduction in the computation time compared to traditional sampling methods, thus, it offers an efficient real-time solution for state estimation in the future geolocation databases.