A nonlinear complementary filter approach for MAV 3D-attitude estimation with low-cost MARG/ADS

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Attitude is an important parameter for micro-unmanned aerial vehicles (MAVs) during its autonomous and stable flying. The MAVs with small size required low power usually use low-cost magnetometer, accelerometer and gyroscope (MARG) to calculate attitude information. But the attitude errors always increase with time due to the gyroscope output corrupted by additive high noise levels and uncertain bias drift. Therefore, a robust and novel nonlinear complementary filter (NCF) approach for MAVs attitude estimation based on low-cost MARG and embedded air data sensors (ADS) is proposed to correct the integrated error and compensate the gyroscope bias on-line. Based on the MAVs kinematic equations and derived coordinate transformation matrix together with different measurement properties, an attitude observer as input is derived for NCF. Finally, a series of experiments are executed on a MAV platform called BH-1 to demonstrate the improved performances of proposed nonlinear complementary filter compared to traditional complementary filter (CF).

Original languageEnglish
Title of host publicationProceedings of the IEEE/ION Position, Location and Navigation Symposium, PLANS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages42-47
Number of pages6
ISBN (Electronic)9781509020423
DOIs
StatePublished - 26 May 2016
EventIEEE/ION Position, Location and Navigation Symposium, PLANS 2016 - Savannah, Georgia
Duration: 11 Apr 201614 Apr 2016

Publication series

NameProceedings of the IEEE/ION Position, Location and Navigation Symposium, PLANS 2016

Conference

ConferenceIEEE/ION Position, Location and Navigation Symposium, PLANS 2016
Country/TerritoryGeorgia
CitySavannah
Period11/04/1614/04/16

Keywords

  • Micro Aerial Vehicles
  • attitude estimation
  • compensation online
  • gyroscope bias
  • nonlinear complementary filter

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