Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Neural Computation
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
The Journal of Machine Learning Research
Distributed learning and cooperative control for multi-agent systems
Automatica (Journal of IFAC)
Extremum seeking under stochastic noise and applications to mobile sensors
Automatica (Journal of IFAC)
Decentralized Environmental Modeling by Mobile Sensor Networks
IEEE Transactions on Robotics
Mobile Sensor Network Navigation Using Gaussian Processes With Truncated Observations
IEEE Transactions on Robotics
Automatica (Journal of IFAC)
Distributed parametric and nonparametric regression with on-line performance bounds computation
Automatica (Journal of IFAC)
Hi-index | 22.14 |
In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. The physical spatial field of interest is discretized and modeled by a Gaussian Markov random field (GMRF) with uncertain hyperparameters. From a Bayesian perspective, we design a sequential prediction algorithm to exactly compute the predictive inference of the random field. The main advantages of the proposed algorithm are: (1) the computational efficiency due to the sparse structure of the precision matrix, and (2) the scalability as the number of measurements increases. Thus, the prediction algorithm correctly takes into account the uncertainty in hyperparameters in a Bayesian way and is also scalable to be usable for mobile sensor networks with limited resources. We also present a distributed version of the prediction algorithm for a special case. An adaptive sampling strategy is presented for mobile sensing agents to find the most informative locations in taking future measurements in order to minimize the prediction error and the uncertainty in hyperparameters simultaneously. The effectiveness of the proposed algorithms is illustrated by numerical experiments.