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A Quality-aware Incremental LMS Algorithm for Distributed Adaptive Estimation

A Quality-aware Incremental LMS Algorithm for Distributed Adaptive Estimation
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摘要 In this paper, we consider the problem of unknown parameter estimation using a set of nodes that are deployed over an area. The recently proposed distributed adaptive estimation algorithms(also known as adaptive networks) are appealing solutions to the mentioned problem when the statistical information of the underlying process is not available or it varies over time. In this paper, our goal is to develop a new incremental least-mean square(LMS) adaptive network that considers the quality of measurements collected by the nodes. Thus, we use an adaptive combination strategy which assigns each node a step size according to its quality of measurement. The adaptive combination strategy improves the robustness of the proposed algorithm to the spatial variations of signal-to-noise ratio(SNR). The performance of our algorithm is more remarkable in inhomogeneous environments when there are some nodes with low SNRs in the network. The simulation results indicate the efficiency of the proposed algorithm. In this paper, we consider the problem of unknown parameter estimation using a set of nodes that are deployed over an area. The recently proposed distributed adaptive estimation algorithms(also known as adaptive networks) are appealing solutions to the mentioned problem when the statistical information of the underlying process is not available or it varies over time. In this paper, our goal is to develop a new incremental least-mean square(LMS) adaptive network that considers the quality of measurements collected by the nodes. Thus, we use an adaptive combination strategy which assigns each node a step size according to its quality of measurement. The adaptive combination strategy improves the robustness of the proposed algorithm to the spatial variations of signal-to-noise ratio(SNR). The performance of our algorithm is more remarkable in inhomogeneous environments when there are some nodes with low SNRs in the network. The simulation results indicate the efficiency of the proposed algorithm.
出处 《International Journal of Automation and computing》 EI CSCD 2014年第6期676-682,共7页 国际自动化与计算杂志(英文版)
关键词 Adaptive networks distributed estimation least mean-square (LMS) incremental cooperation quality aware Adaptive networks distributed estimation least mean-square (LMS) incremental cooperation quality aware
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  • 1D. B. Kingston, A. W. Beard. Real-time attitude and po- sition estimation for small UAVs using low-cost sensors. In Proceedings of the AIAA 3rd Unmanned Unlimited Tech- nical Conference, Workshop and Exhibit, AIAA, Chicago USA, pp. 6488-6496, 2004.
  • 2B. Ludington, E. Johnson, G. Vachtsevanos. Augmenting UAV autonomy. Robotics Automation Magazine, vol. 13, no. 3, pp. 63-71, 2006.
  • 3M. K. Kaiser, N. R. Gans, W. E. Dixon. Vision-based esti- mation for guidance, navigation, and control of an aerial vehicle. IEEE Transactions on Aerospace and Electronic Systems, vol. 46, no. 3, pp. 1064-1077, 2010.
  • 4S. Weiss, D. Scaramuzza, R. Siegwart. Monocular-SLAM- based navigation for autonomous micro helicopters in GPS- denied environments. Journa/ of Field Robotics, vol. 28, no. 6, pp. 854-874, 2011.
  • 5P. Yang, W. Y. Wu, M. Moniri, C. C. Chibelushi. A sensor- based SLAM algorithm for camera tracking in virtual stu- dio. International Journal of Automation and Computing, vol. 5, no. 2, pp. 152-162, 2008.
  • 6J. Artieda, J. M. Sebastian, P. Campoy, J. F. Correa, I. F. Mondragon, C. Martinez, M. Olivares. Visual 3-D SLAM from UAVs. Journal of Intelligent 35 Robotic Sys- tems, vol. 55, no. 4-5, pp. 299-321, 2009.
  • 7F. Caballero, L. Merino, J. Ferruz, A. Ollero. Vision-based odometry and SLAM for medium and high altitude flying UAVs. Journal of Intelligent gz Robotic Systems, vol. 54, no. 1-3, pp. 137-161, 2009.
  • 8J. Kelly, S. Saripalli, G. S. Sukhatme. Combined visual and inertial navigation for an unmanned aerial vehicle. In Pro- ceedings of the International Conference on Field and Ser- vice Robotics, FSR, Chamonix, France, pp. 255-264, 2007.
  • 9K. H. Yang, W. S. Yu, X. Q. Ji. Rotation estimation for mobile robot based on single-axis gyroscope and monocularcamera. International Journal of Automation and Comput- ing, vol. 9, no. 3, pp. 292-298, 2012.
  • 10V. Sazdovski, P. M. G. Silson. Inertial navigation aided by vision-based simultaneous localization and mapping. IEEE Sensors Journal, vol. 11, no. 8, pp. 1646-1656, 2011.

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