Spatial prediction with mobile sensor networks using Gaussian processes with built-in Gaussian Markov random fields

Authors:
Yunfei Xu;Jongeun Choi
Affiliations:
Department of Mechanical Engineering, Michigan State University, United States;Department of Mechanical Engineering, Michigan State University, United States and Department of Electrical and Computer Engineering, Michigan State University, United States
Venue:
Automatica (Journal of IFAC)
Year:
2012

Citing 6
Cited 1

Parallel and distributed computation: numerical methods

Parallel and distributed computation: numerical methods
Conditional-mean least-squares fitting of Gaussian Markov random fields to Gaussian fields

Computational Statistics & Data Analysis
Fast kriging of large data sets with Gaussian Markov random fields

Computational Statistics & Data Analysis
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies

The Journal of Machine Learning Research
Distributed learning and cooperative control for multi-agent systems

Automatica (Journal of IFAC)
Mobile Sensor Network Navigation Using Gaussian Processes With Truncated Observations

IEEE Transactions on Robotics

Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.15

Visualization

Abstract

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.