Multiple Model Adaptive Complementary Filter for Attitude Estimation

Kottath, Rahul and Narkhede, Parag and Kumar, Vipan and Karar, Vinod and Poddar, Shashi (2017) Multiple Model Adaptive Complementary Filter for Attitude Estimation. Aerospace Science and Technology, 69. pp. 574-581. ISSN 1270-9638

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Attitude estimation plays a major role in the autonomy of unmanned aerial vehicles and requires fusion of different sensor measurements. This paper describes an adaptive estimation scheme in which the weight parameter for the complementary filter (CF) is varied over time. The adaptive mechanism proposed here is inspired from the multiple model adaptive estimation (MMAE) scheme used for varying noise parameters in the Kalman filter structure. In this paper, the linear complementary filters are used as elementary blocks in the MMAE structure and their weights are modified probabilistically to obtain an accurate orientation estimate. It avoids the problem of manual selection of weight factor for complementary filter and provides a robust orientation estimate against varying system dynamics. The proposed MMAE based adaptive CF scheme is modular in nature and is dependent on the residual error between estimated and the measured orientation angle. It is applied on the real world datasets logged from inertial sensors and the performance of MMAE based CF structure is found to work promisingly as compared to the non-linear complementary filter versions and the extended Kalman filter framework.

Item Type: Article
Uncontrolled Keywords: Attitude estimation; Complementary filter; Kalman filter; Multiple model adaptive estimation
Subjects: CSIO > Optics
Divisions: Strategic Instrumentation
Depositing User: Ms T Kaur
Date Deposited: 28 Feb 2019 15:52
Last Modified: 28 Feb 2019 15:52

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