In this paper, a new class of Gaussian processes is proposed for resource-constrained mobile sensor networks. Such a Gaussian process builds on a GMRF with respect to a proximity graph over a surveillance region. The main advantages of using this class of Gaussian processes over standard Gaussian processes defined by mean and covariance functions are its numerical efficiency and scalability due to its built-in GMRF and its capability of representing a wide range of non-stationary physical processes. The formulas for predictive statistics such as predictive mean and variance are derived and a sequential field prediction algorithm is provided for sequentially sampled observations. For a special case using compactly supported weighting functions, we propose a distributed algorithm to implement field prediction by correctly fusing all observations. Simulation and experimental results illustrate the effectiveness of our approach.
Bibliographical noteFunding Information:
The authors thank Mr. Justin Mrkva for his effort to obtain the experimental data used in Section 4.2 . The authors would also like to thank the associate editor and the anonymous reviewers for their valuable comments and suggestions. Yunfei Xu received his Ph.D. in Mechanical Engineering from Michigan State University in 2011. He also received his M.S. and B.S. degrees in Automotive Engineering from Tsinghua University, Beijing, China, in 2004 and 2007, respectively. Currently, he is a Research Fellow with the Department of Mechanical Engineering at Michigan State University. His current research interests include environmental adaptive sampling algorithms, Gaussian processes, and statistical learning algorithms with applications to robotics and mobile sensor networks. His paper was a finalist for the Best Student Paper Award at the Dynamic System and Control Conference (DSCC) 2011. Dr. Xu is a member of IEEE and ASME. Jongeun Choi received his Ph.D. and M.S. degrees in Mechanical Engineering from the University of California at Berkeley in 2006 and 2002 respectively. He also received a B.S. degree in Mechanical Design and Production Engineering from Yonsei University at Seoul, Republic of Korea in 1998. He is currently an Assistant Professor with the Department of Mechanical Engineering and the Department of Electrical and Computer Engineering at the Michigan State University. His research interests include adaptive, distributed and robust control and statistical learning algorithms, with applications to mobile robotic sensors, environmental adaptive sampling, engine control, and biomedical problems. He was a recipient of an NSF CAREER Award in 2009. His papers were finalists for the Best Student Paper Award at the 24th American Control Conference (ACC) 2005 and the Dynamic System and Control Conference (DSCC) 2011. Dr. Choi is a member of IEEE and ASME.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering