Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503. AGENCY USE ONLY (Leave blank)2. REPORT DATE December 2011 REPORT TYPE AND DATES COVERED 12b. DISTRIBUTION CODE A 13. ABSTRACT (maximum 200 words)Cognitive radio presents a unique challenge to source localization in that the radio has the ability to adapt to the environment, thus rendering current localization techniques ineffective due to a shifting combination of spatial, frequency, and temporal parameters. For any localization scheme to be effective, it must be able to adapt over time as a cognitive radio adapts to its surroundings. In this thesis an extended semi range-based localization scheme is proposed to accomplish this task. The proposed scheme estimates the position of a cognitive radio using the collaborative spectrum sensing results of a wireless radio frequency sensor network in a cognitive radio environment. The central idea behind the proposed scheme is to exploit the relationships between spatial, frequency, and temporal parameters of the environment to solve for the position of the cognitive radio. The proposed scheme is modeled in the MATLAB programming language, and its efficacy is demonstrated through simulation. It is shown that over time the proposed scheme is capable of estimating the frequency band of operation and the location of a cognitive radio, and is thus capable of accounting for both frequency and spatial mobility inherent in the cognitive radio environment. NUMBER OF PAGES 104 SUBJECT TERMS ABSTRACTCognitive radio presents a unique challenge to source localization in that the radio has the ability to adapt to the environment, thus rendering current localization techniques ineffective due to a shifting combination of spatial, frequency, and temporal parameters.For any localization scheme to be effective, it must be able to adapt over time as a cognitive radio adapts to its surroundings. In this thesis an extended semi range-based localization scheme is proposed to accomplish this task. The proposed scheme estimates the position of a cognitive radio using the collaborative spectrum sensing results of a wireless radio frequency sensor network in a cognitive radio environment. The central idea behind the proposed scheme is to exploit the relationships between spatial, frequency, and temporal parameters of the environment to solve for the position of the cognitive radio. The proposed scheme is modele...
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.
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