Báo cáo hóa học: "Research Article Decentralized Turbo Báo cáo hóa học: "Bayesian Compressed Sensing with Application to UWB Systems"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Decentralized Turbo Bayesian Compressed Sensing with Application to UWB Systems | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 817947 17 pages doi 2011 817947 Research Article Decentralized Turbo Bayesian Compressed Sensing with Application to UWB Systems Depeng Yang Husheng Li and Gregory D. Peterson Department of Electrical Engineering and Computer Science The University of Tennessee Knoxville TN 37996 USA Correspondence should be addressed to Depeng Yang dyang7@ Received 19 July 2010 Revised 1 February 2011 Accepted 28 February 2011 Academic Editor Dirk T. M. Slock Copyright 2011 Depeng Yang et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. In many situations there exist plenty of spatial and temporal redundancies in original signals. Based on this observation a novel Turbo Bayesian Compressed Sensing TBCS algorithm is proposed to provide an efficient approach to transfer and incorporate this redundant information for joint sparse signal reconstruction. As a case study the TBCS algorithm is applied in Ultra-Wideband UWB systems. A space-time TBCS structure is developed for exploiting and incorporating the spatial and temporal a priori information for space-time signal reconstruction. Simulation results demonstrate that the proposed TBCS algorithm achieves much better performance with only a few measurements in the presence of noise compared with the traditional Bayesian Compressed Sensing BCS and multitask BCS algorithms. 1. Introduction Compressed sensing CS theory 1 2 is blooming in recent years. In the CS theory the original signal is not directly acquired but reconstructed based on the measurements obtained from projecting the signal using a random sensing matrix. It is well known that most natural signals are sparse that is in a certain transform domain most elements are zeros or have very small .

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