scholarly journals Sparse Gaussian Process for Spatial Function Estimation with Mobile Sensor Networks

Author(s):  
Bowen Lu ◽  
Dongbing Gu ◽  
Huosheng Hu ◽  
Klaus McDonald-Maier
Author(s):  
Yunfei Xu ◽  
Jongeun Choi

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 Bayesian posterior predictive statistics such as prediction mean and variance are derived and a sequential field prediction algorithm is provided for sequentially sampled observations. For a special case using compactly supported kernels, we propose a distributed algorithm to implement field prediction by correctly fusing all observations in Bayesian statistics. Simulation results illustrate the effectiveness of our approach.


2010 ◽  
Vol 21 (3) ◽  
pp. 490-504 ◽  
Author(s):  
Fu-Long XU ◽  
Ming LIU ◽  
Hai-Gang GONG ◽  
Gui-Hai CHEN ◽  
Jian-Ping LI ◽  
...  

2012 ◽  
Vol 23 (3) ◽  
pp. 629-647 ◽  
Author(s):  
Lei WU ◽  
Xiao-Min WANG ◽  
Ming LIU ◽  
Gui-Hai CHEN ◽  
Hai-Gang GONG

Author(s):  
Jongeun Choi ◽  
Dejan Milutinović

This tutorial paper presents the expositions of stochastic optimal feedback control theory and Bayesian spatiotemporal models in the context of robotics applications. The presented material is self-contained so that readers can grasp the most important concepts and acquire knowledge needed to jump-start their research. To facilitate this, we provide a series of educational examples from robotics and mobile sensor networks.


Sign in / Sign up

Export Citation Format

Share Document