Earticle

초록

영어

Complementary filters coupled with MEMS IMU are preferred in applications where computational simplicity, low power and low cost is of prime importance. Such algorithms are equipped with fixed filter’s gain, however improvements can be realized by changing the filter’s gain as per the dynamic situation experienced by the platform. This paper is intended to evaluate the performance of conventional fixed gain complementary algorithm against adoptive gain complementary filter for attitude estimation using MEMS IMU as a test subject. As only IMU (Inertial Measurement Unit) has been exploited without using any aided sensory system, so the mandate is limited to evaluate performance of these algorithms on the basis of Euler angles roll and pitch estimation. Significant performance improvement is observed by varying filter gain in accordance with dynamic situation experienced by the system.

목차

Abstract
 1. Introduction
 2. Complementary Filter and MEMS IMU Modeling
  2.1. Complementary Filter
  2.2. Attitude Estimation from Gyro
  2.3. Attitude Estimation from Accelerometer
 3. Attitude Estimating Algorithms
  3.1. Conventional Complementary Filter (CCF)
  3.2. Modified Gain Complementary Filter (MGCF)
  3.3. Unscented Kalman Filter (UKF)
 4. Results and Discussion
  4.1. Simulated Data Results
  4.2. Experimental Data Result
 5. Conclusion
 References

키워드

저자

• Dung Duong Quoc [ Dept. of Automatic Test and Control, Harbin Institute of Technology, Harbin, China, Le Qui Don Technical University, Cau Giay, Ha Noi, Viet Nam ]
• Jinwei Sun [ Dept. of Automatic Test and Control, Harbin Institute of Technology, Harbin, China ] Corresponding author
• Van Nhu Le [ Research center for Space Optics Engineering, Harbin Institute of Technology, China, Le Qui Don Technical University, Cau Giay, Ha Noi, Viet Nam ]
• Lei Luo [ Dept. of Automatic Test and Control, Harbin Institute of Technology, Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605