Functional replicas of proprietary three-axis attitude sensors via LSTM neural networks

Hao Fu, Prashanth Krishnamurthy, Farshad Khorrami

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, machine-learning-based tools are utilized to learn a model of the functionality of a commercial chip with built-in signal processing and sensor fusion algorithms. Specifically, a fully integrated 9-axis Inertial Measurement Unit (IMU) with embedded algorithms providing the three-axis attitude and corresponding quaternion by fusing all the sensors is considered. Traditionally, extended Kalman filters are used for fusing IMU sensors; however, subtle algorithmic fixes (e.g., magnetic and angular alignment calibration for all sensors, Kalman filter tuning, temperature drift compensation, dynamic magnetic effects) need to be deployed to attain precise attitude (especially heading). A recurrent neural network (RNN) was trained using the chip to substitute for the built-in algorithms of the IMU Chip to output the approximate attitude given the 9-axis sensor data. We show the efficacy of our approach by mounting two IMUs on a board and utilize one IMU, which has its own internal algorithms, to train a machine learning system to fuse the raw data from the sensors on the second IMU to generate comparable accuracy (and in some cases, even outperform the original IMU).

Original languageEnglish (US)
Title of host publicationCCTA 2020 - 4th IEEE Conference on Control Technology and Applications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages70-75
Number of pages6
ISBN (Electronic)9781728171401
DOIs
StatePublished - Aug 2020
Event4th IEEE Conference on Control Technology and Applications, CCTA 2020 - Virtual, Montreal, Canada
Duration: Aug 24 2020Aug 26 2020

Publication series

NameCCTA 2020 - 4th IEEE Conference on Control Technology and Applications

Conference

Conference4th IEEE Conference on Control Technology and Applications, CCTA 2020
CountryCanada
CityVirtual, Montreal
Period8/24/208/26/20

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Optimization
  • Instrumentation

Fingerprint Dive into the research topics of 'Functional replicas of proprietary three-axis attitude sensors via LSTM neural networks'. Together they form a unique fingerprint.

Cite this