Adaptive Sampling for Learning Gaussian Processes Using Mobile Sensor Networks

Xu, Yunfei; Choi, Jongeun

doi:10.3390/s110303051

Cited by 76 publications

(53 citation statements)

References 18 publications

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“…We also assume the bounds of θ, viz. σ s ∈ [1.6, 2.4] and σ t ∈ [4,12] are known. ∆ = 12 is used and η = 11 is selected satisfying the condition in Theorem 4.3.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Sequential Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks

Choi

Dass

et al. 2012

IEEE Trans. Automat. Contr.

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In this paper, we formulate a fully Bayesian approach for spatio-temporal Gaussian process regression such that multifactorial effects of observations, measurement noise and prior distributions are all correctly incorporated in the predictive distribution. Using discrete prior probabilities and compactly supported kernels, we provide a way to design sequential Bayesian prediction algorithms in which exact predictive distributions can be computed in constant time as the number of observations increases.For a special case, a distributed implementation of sequential Bayesian prediction algorithms has been proposed for mobile sensor networks. An adaptive sampling strategy for mobile sensors, using the maximum a posteriori (MAP) estimation, has been proposed to minimize the prediction error variances.Simulation results illustrate the practical usefulness of the proposed theoretically-correct algorithms.

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“…We also assume the bounds of θ, viz. σ s ∈ [1.6, 2.4] and σ t ∈ [4,12] are known. ∆ = 12 is used and η = 11 is selected satisfying the condition in Theorem 4.3.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…On the other hand, unknown parameters in the covariance function can be estimated by the maximum likelihood (ML) estimator. Such ML estimates may be regarded as the true parameters and then used in the prediction [12]. However, the point estimate itself needs to be identified using sufficient amount of measurements.…”

mentioning

confidence: 99%

Sequential Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks

Choi

Dass

et al. 2012

IEEE Trans. Automat. Contr.

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show abstract

“…In general, the mean and the covariance functions of a GP can be estimated a priori by maximizing the likelihood function [37].…”

Section: The ρ-Th Random Fieldmentioning

confidence: 99%

“…All parameters are learned simultaneously. If no prior information is given, then the maximum a posteriori probability (MAP) estimator is equal to the ML estimator [37].…”

Section: The ρ-Th Random Fieldmentioning

confidence: 99%

Feature selection for position estimation using an omnidirectional camera

Huan

Jadaliha

Choi

et al. 2015

Image and Vision Computing

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This paper considers visual feature selection to implement position estimation using an omnidirectional camera. The localization is based on a maximum likelihood estimation (MLE) with a map from optimally selected visual features using Gaussian process (GP) regression. In particular, the collection of selected features over a surveillance region is modeled by a multivariate GP with unknown hyperparameters. The hyperparameters are identified through the learning process by an MLE, which are used for prediction in an empirical Bayes fashion. To select features, we apply a backward sequential elimination technique in order to improve the quality of the position estimation with compressed features for efficient localization. The excellent results of the proposed algorithm are illustrated by the experimental studies with different visual features under both indoor and outdoor real-world scenarios.

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“…al., 2012;Purvis et. al., 2008;Oshman and Davidson, 1999;Xu and Choi, 2011). However, the position error of the mobile buoy is ignored.…”

mentioning

confidence: 99%

Optimal Distance between Mobile Buoy and Target for Moving Long Baseline Positioning System

et al. 2015

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This paper investigates the problem of how to design the distance between a mobile buoy and the target to derive maximum positioning accuracy with a Moving Long Baseline (MLBL). To that end, the positioning model and the error sources of MLBL are derived, respectively. It is assumed that the position measurement of the mobile buoy and the distance measurement between the mobile buoy and the target are corrupted by white Gaussian noises, and the variance of the distance measurement is distance-dependent. Using tools from estimation theory, the Positioning Accuracy Metric (PAM) is designed with the distance error and the position errors are considered. Based on the PAM, the optimal distance between the mobile buoy and target is deduced when the mobile buoys are in optimal geometry. Simulation examples illustrate the results.2. Mobile buoy. 3. Optimal distance. 4. Positioning accuracy.

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Adaptive Sampling for Learning Gaussian Processes Using Mobile Sensor Networks

Cited by 76 publications

References 18 publications

Sequential Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks

Sequential Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks

Feature selection for position estimation using an omnidirectional camera

Optimal Distance between Mobile Buoy and Target for Moving Long Baseline Positioning System

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