Data-Driven Batch Processing for Parameter Calibration of a Sensor System

Research output: Contribution to journalArticlepeer-review


When modeling a sensor system mathematically, we assume that the sensor noise is Gaussian and white to simplify the model. If this assumption fails, the performance of the sensor model-based controller or estimator degrades due to incorrect modeling. In practice, non-Gaussian or non-white noise sources often arise in many digital sensor systems. Additionally, the noise parameters of the sensor model are not known in advance without additional noise statistical information. Moreover, disturbances or high nonlinearities often cause unknown sensor modeling errors. To estimate the uncertain noise and model parameters of a sensor system, this paper proposes an iterative batch calibration method using data-driven machine learning. Our simulation results validate the calibration performance of the proposed approach.

Original languageEnglish
Pages (from-to)475-480
Number of pages6
JournalJournal of Sensor Science and Technology
Issue number6
StatePublished - Nov 2023


  • Batch Processing
  • Data-Driven
  • Machine Learning
  • Parameter Calibration
  • Sensor System


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