Robust Hierarchical Data Fusion Scheme for Large-Scale Sensor Network
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
The advanced driver assistant system (ADAS) requires the collection of a large amount of information including road conditions, environment, vehicle status, condition of the driver, and other useful data. In this regard, large-scale sensor networks can be an appro- priate solution since they have been designed for this purpose. Recent advances in sensor network technology have enabled the man-agement and monitoring of large-scale tasks such as the monitoring of road surface temperature on a highway. In this paper, we considerthe estimation and fusion problems of the large-scale sensor networks used in the ADAS. Hierarchical fusion architecture is proposedfor an arbitrary topology of the large-scale sensor network. A robust cluster estimator is proposed to achieve robustness of the network against outliers or failure of sensors. Lastly, a robust hierarchical data fusion scheme is proposed for the communication channel between the clusters and fusion center, considering the non-Gaussian channel noise, which is typical in communication systems.
Abstract
The advanced driver assistant system (ADAS) requires the collection of a large amount of information including road conditions, environment, vehicle status, condition of the driver, and other useful data. In this regard, large-scale sensor networks can be an appro- priate solution since they have been designed for this purpose. Recent advances in sensor network technology have enabled the man-agement and monitoring of large-scale tasks such as the monitoring of road surface temperature on a highway. In this paper, we considerthe estimation and fusion problems of the large-scale sensor networks used in the ADAS. Hierarchical fusion architecture is proposedfor an arbitrary topology of the large-scale sensor network. A robust cluster estimator is proposed to achieve robustness of the network against outliers or failure of sensors. Lastly, a robust hierarchical data fusion scheme is proposed for the communication channel between the clusters and fusion center, considering the non-Gaussian channel noise, which is typical in communication systems.
참고문헌 (14)
- F. Martinerie, "Data Fusion and Tracking Using HMMs in a Distributed Sensor Network", IEEE Trans. Aerospace and Electronics System, 33(1), pp.11~28 (1997)
- D. Reid, "An Algorithm for Tracking Multiple Targets", IEEE Trans. Automatic Control, 24(6), pp.843~854 (1979)
- A. L. Barabási, "Emergence of Scaling in Random Networks", Science, 286(15), pp.509~512 (1999)
- R. Albert, "Error and Attack Tolerance of Complex Networks", Nature, 406(6794), pp.378~382 (2000)
- R. Albert, "Diameter of the World Wide Web", Nature, 401(6749), pp.130~131 (1999)
- Impact of Scale-free Topologies on Gossiping in Wireless Sensor Networks, "B. Garbinato", University of Lausanne, ~, pp.University of Lausanne, Swiss, Tech. Rep. DOP-20070302 (2007)
- B. Hashemloo, "Forecasting Pavement Surface Temperature Using Time Series and Artificial Neural Networks", MS, pp.2008
- D. G. Lainiotis, "Partitioned Linear Estimation Algorithm : Discrete Case", IEEE Trans. Automatic Control, 20(3), pp.255~257 (1975)
- D. Y. Kim, "Suboptimal Filter For Continuous-Time Linear Systems with Unknown Parameters", Asian Journal of Control, 10(5), pp.525~534 (2008)
- A. Ahmad, "Decentralized Robust Kalman Filtering for Uncertain Stochastic Systems over Heterogeneous Sensor Networks", Signal Processing, 88(8), pp.1919~1928 (2008)
- D. L. Hall, "Handbook of Multisensor Data Fusion" (2001), CRC Press
- D. Franken, "Improved Fast Covariance Intersection for Distributed Data Fusion", Proc. 8th Int. Conf. on Information Fusion (FUSION ‘05) (2005)
- K. Watanabe, "Adaptive Estimation and Control: Partitioning Approach" (1991), Prentice-Hall
- F. L. Lewis, "Optimal Estimation with an Introduction to Stochastic Control Theory" (1986), John Wiley and Sons