Adaptive Attitude Estimation Using a Hybrid Model-Learning Approach

Eran Vertzberger, Itzik Klein

Research output: Contribution to journalArticlepeer-review

Abstract

Attitude determination using the smartphone’s inertial sensors poses a major challenge due to the sensor low-performance grade and variate nature of the walking pedestrian. In this paper, data-driven techniques are employed to address that challenge. To that end, a hybrid deep learning and model based solution for attitude estimation is proposed. Here, classical model based equations are applied to form an adaptive complementary filter structure. Instead of using constant or model based adaptive weights, the accelerometer weights in each axis are determined by a unique neural network. The performance of the proposed hybrid approach is evaluated relative to popular model based approaches using experimental data.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Instrumentation and Measurement
DOIs
StateAccepted/In press - 2022

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Accelerometers
  • Adaptation models
  • Attitude and Heading Reference System
  • complementary filter
  • Data-driven navigation
  • deep learning
  • Estimation
  • inertial sensors
  • Magnetometers
  • Mathematical models
  • Position measurement
  • Quaternions

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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