Reproduce “AMO-HEAD: Adaptive MARG-Only Heading Estimation for UAVs under Magnetic Disturbances” on OpenResearch
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About this paper
Qizhi Guo, Siyuan Yang, Junning Lyu, Jianjun Sun, Defu Lin, Shaoming He
An adaptive Extended Kalman Filter (AEKF) framework, AMO-HEAD, was developed by researchers at Beijing Institute of Technology to provide robust heading estimates for Unmanned Aerial Vehicles (UAVs) in indoor environments with severe magnetic disturbances. The system achieved a heading Root Mean Square Error (RMSE) of 1.58 degrees, demonstrating improved accuracy and robustness compared to existing open-source algorithms.
Key insights
- Dynamic adjustment of both process noise and magnetometer measurement noise covariances is crucial for maintaining robust heading estimation in environments with varying magnetic interference.
- Explicitly modeling various sources of process noise, including Euler discretization error and gyroscope bias as random walks, leads to improved filter stability and accuracy.
- A continuous adaptive weighting approach for magnetometer data, rather than binary detect-and-reject, allows the filter to adapt gracefully to different levels of magnetic disturbance.
Summary by alphaXiv.
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