tailieunhanh - báo cáo hóa học:" Research Article Low-Power Distributed Kalman Filter for Wireless Sensor Networks"

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 Low-Power Distributed Kalman Filter for Wireless Sensor Networks | Hindawi Publishing Corporation EURASIP Journal on Embedded Systems Volume 2011 Article ID 693150 11 pages doi 2011 693150 Research Article Low-Power Distributed Kalman Filter for Wireless Sensor Networks A. Abdelgawad1 and M. Bayoumi2 1 The Center for Advanced Computer Studies University of Louisiana at Lafayette Lafayette LA 70504 USA 2The Center for Advanced Computer Studies Department of Computer Science University of Louisiana at Lafayette Lafayette LA 70504 USA Correspondence should be addressed to A. Abdelgawad ama1916@ Received 28 April 2010 Revised 30 June 2010 Accepted 2 September 2010 Academic Editor Xiaorui Wang Copyright 2011 A. Abdelgawad and M. Bayoumi. 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. Distributed estimation algorithms have attracted a lot of attention in the past few years particularly in the framework of Wireless Sensor Network WSN . Distributed Kalman Filter DKF is one of the most fundamental distributed estimation algorithms for scalable wireless sensor fusion. Most DKF methods proposed in the literature rely on consensus filters algorithm. The convergence rate of such distributed consensus algorithms typically depends on the network topology. This paper proposes a low-power DKF. The proposed DKF is based on a fast polynomial filter. The idea is to apply a polynomial filter to the network matrix that will shape its spectrum in order to increase the convergence rate by minimizing its second largest eigenvalue. Fast convergence can contribute to significant energy saving. In order to implement the DKF in WSN more power saving is needed. Since multiplication is the atomic operation of Kalman filter so saving power at the multiplication level can significantly impact the energy consumption of the DKF. This paper also proposes a novel light-weight and