Deep Learning for Inertial Sensor Alignment

Maxim Freydin, Nimrod Segol, Niv Sfaradi, Areej Eweida, Barak Or

Research output: Contribution to journalArticlepeer-review


Accurate alignment of a fixed mobile device equipped with inertial sensors inside a moving vehicle is important for navigation, activity recognition, and other applications. Accurate estimation of the device mounting angle is required to rotate the inertial measurement from the sensor frame to the moving platform frame to standardize measurements and improve the performance of the target task. In this work, a data-driven approach using deep neural networks (DNNs) is proposed to learn the yaw mounting angle of a smartphone equipped with an inertial measurement unit (IMU) and strapped to a car. The proposed model uses only the accelerometer and gyroscope readings from an IMU as input and, in contrast to existing solutions, does not require global position inputs from global navigation satellite systems (GNSS). To train the model in a supervised manner, IMU data is collected for training and validation with the sensor mounted at a known yaw mounting angle, and a range of ground truth labels is generated by applying a random rotation in a bounded range to the measurements. The trained model is tested on data with real rotations showing similar performance as with synthetic rotations. The trained model is deployed on an Android device and evaluated in real-time to test the accuracy of the estimated yaw mounting angle. The model is shown to find the mounting angle at an accuracy of 8 degrees within 5 seconds, and 4 degrees within 27 seconds. An experiment is conducted to compare the proposed model with an existing off-the-shelf solution.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Sensors Journal
StateAccepted/In press - 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:


  • Accelerometers
  • Automobiles
  • Data models
  • Deep Neural Network
  • Inertial Measurement Unit
  • Inertial Navigation System
  • Machine Learning
  • Sensor Alignment
  • Sensors
  • Supervised Learning
  • Task analysis
  • Training
  • Vectors

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


Dive into the research topics of 'Deep Learning for Inertial Sensor Alignment'. Together they form a unique fingerprint.

Cite this