On the Choice of Reference in Offset Calibration

Raj Thilak Rajan*

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Sensor calibration is an indispensable task in any networked cyberphysical system. In this paper, we consider a sensor network plagued with offset errors, measuring a rank-1 signal subspace, where each sensor collects measurements under a linear model with additive zero-mean Gaussian noise. Under varying assumptions on the underlying noise covariance, we investigate the effect of using an arbitrary reference for estimating the sensor offsets, in contrast to the 'average of all the unknown offsets' as a reference. We first show that the average reference yields an efficient minimum variance unbiased estimator. If the underlying noise is homoscedastic in nature, then we prove the average reference yields a factor 2 improvement on the variance, as compared to any arbitrarily chosen reference within the network. Furthermore, when the underlying noise is independent but not identical, we derive an expression for the improvement offered by the average reference. We demonstrate our results using the problem of clock synchronization in sensor networks, and discuss directions for future work.

Original languageEnglish
Title of host publication2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages291-295
Number of pages5
ISBN (Electronic)9798350344523
DOIs
Publication statusPublished - 2023
Event9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023 - Herradura, Costa Rica
Duration: 10 Dec 202313 Dec 2023

Publication series

Name2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023

Conference

Conference9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023
Country/TerritoryCosta Rica
CityHerradura
Period10/12/2313/12/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Blind calibration
  • Cramér-Rao bounds
  • Parametric constraints
  • Sensor networks

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