Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/102693
Title: A dynamic Bayesian nonparametric model for blind calibration of sensor networks
Authors: Yang, Jielong
Zhong, Xionghu
Tay, Wee Peng
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Blind Calibration
Dynamic Bayesian Nonparametrics
Issue Date: 2018
Source: Yang, J., Zhong, X., & Tay, W. P. (2018). A dynamic Bayesian nonparametric model for blind calibration of sensor networks. IEEE Internet of Things Journal, 5(5), 3942-3953. doi:10.1109/JIOT.2018.2847697
Series/Report no.: IEEE Internet of Things Journal
Abstract: We consider the problem of blind calibration of a sensor network, where the sensor gains and offsets are estimated from noisy observations of unknown signals. This is in general a nonidentifiable problem, unless restrictive assumptions on the signal subspace or sensor observations are imposed. We show that if each signal observed by the sensors follows a known dynamic model with additive noise, then the sensor gains and offsets are identifiable. We propose a dynamic Bayesian nonparametric model to infer the sensors’ gains and offsets. Our model allows different sensor clusters to observe different unknown signals, without knowing the sensor clusters a priori . We develop an offline algorithm using block Gibbs sampling and a linearized forward filtering backward sampling method that estimates the sensor clusters, gains, and offsets jointly. Furthermore, for practical implementation, we also propose an online inference algorithm based on particle filtering and local Markov chain Monte Carlo. Simulations using a synthetic dataset, and experiments on two real datasets suggest that our proposed methods perform better than several other blind calibration methods, including a sparse Bayesian learning approach, and methods that first cluster the sensor observations and then estimate the gains and offsets.
URI: https://hdl.handle.net/10356/102693
http://hdl.handle.net/10220/47842
DOI: 10.1109/JIOT.2018.2847697
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JIOT.2018.2847697.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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