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

Abstract

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.

Target Localization and Tracking by Fusing Doppler Differentials from Cellular Emanations with a Multi-Spectral Video Tracker

“…For example, it is sometimes difficult to differentiate between targets when using a thermal infrared (IR) sensor as target signatures are similar and thermal contrast between the target and the background can be low. Radio frequency (RF) communications contain identification information about the transmitting source but lack the ability to spatially localize the target with low uncertainty [ 1 ]. The combination of data from two or more sensors, referred to as sensor fusion, exploits the advantages of multiple sensors while overcoming the disadvantages of each individual sensor [ 2 , 3 ].…”