Data-driven denoising of stationary accelerometer signals

Daniel Engelsman, Itzik Klein

Research output: Contribution to journalArticlepeer-review


Modern navigation solutions are largely dependent on the performances of the standalone inertial sensors, especially at times when no external sources are available. During these outages, the inertial navigation solution is likely to degrade over time due to instrumental noises sources, particularly when using consumer low-cost inertial sensors. Conventionally, model-based estimation algorithms are employed to reduce noise levels and enhance meaningful information, thus improving the navigation solution directly. However, guaranteeing their optimality often proves to be challenging as sensors performance differ in manufacturing quality, process noise modeling, and calibration precision. In the literature, most inertial denoising models are model-based when recently several data-driven approaches were suggested primarily for gyroscope measurements denoising. Data-driven approaches for accelerometer denoising task are more challenging due to the unknown gravity projection on the accelerometer axes. To fill this gap, we propose several learning-based approaches and compare their performances with prominent denoising algorithms, in terms of pure noise removal, followed by stationary coarse alignment procedure. Based on the benchmarking results, obtained in field experiments, we show that the learning-based models outperform traditional signal processing filtering in terms of pure inertial signal reconstruction. Moreover, they are shown to improve angular errors by one order of magnitude, given a navigation-related task.

Original languageEnglish
Article number113218
JournalMeasurement: Journal of the International Measurement Confederation
StatePublished - 15 Aug 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s)


  • Attitude estimation
  • Deep learning
  • Inertial sensors
  • Signal denoising

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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