Reference-Free Calibration in Sensor Networks

Raj Thilak Rajan, Rob van Schaijk, Anup Das, Jac Romme, Frank Pasveer

Research output: Contribution to journalArticleScientificpeer-review


Sensor calibration is one of the fundamental challenges in large-scale Internet of Things networks. In this article, we address the challenge of reference-free calibration of a densely deployed sensor network. Conventionally, to calibrate an in-place sensor network (or sensor array), a reference is arbitrarily chosen with or without prior information on sensor performance. However, an arbitrary selection of a reference could prove fatal, if an erroneous sensor is inadvertently chosen. To avert single point of dependence, and to improve estimator performance, we propose unbiased reference-free algorithms. Although our focus is on reference-free solutions, the proposed framework allows the incorporation of additional references, if available. We show, with the help of simulations, that the proposed solutions achieve the derived statistical lower bounds asymptotically. In addition, the proposed algorithms show improvements on real-life datasets, as compared to prevalent algorithms.
Original languageEnglish
Pages (from-to)1-4
Number of pages4
JournalIEEE Sensors Letters
Issue number3
Publication statusPublished - 2018


  • Sensor signals processing
  • blind calibration
  • least squares
  • network calibration
  • sensor array
  • signal processing


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