Process / pipelineGradient Descent Filtering

Madgwick Filter

The Madgwick Filter is a computationally lightweight attitude estimation algorithm that fuses inertial measurements (accelerometer, gyroscope) with magnetic measurements (magnetometer) to compute a quaternion orientation. Introduced by Sebastian Madgwick in 2010, the algorithm uses gradient descent optimization to minimize the error between measured and expected sensor outputs, yielding accurate, drift-free attitude estimates on embedded systems with minimal computational cost. The Madgwick Filter is now ubiquitous in consumer electronics, robotics, and aerospace systems.

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Sources

  1. Madgwick, S. O. H., Harrison, A. J. L., & Vaidyanathan, R. (2011). Estimation of IMU and MARG orientation using a gradient descent algorithm. IEEE International Conference on Rehabilitation Robotics (ICORR), 1–7. link
  2. Madgwick, S. O. H. (2010). An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Report x-io Technologies, University of Bristol, UK. link
  3. Sabatini, A. M. (2006). Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing. IEEE Transactions on Biomedical Engineering, 53(7), 1346–1356. DOI: 10.1109/TBME.2006.875664

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Referenced by

ScholarGateMadgwick Filter (Madgwick IMU and AHRS Algorithms). Retrieved 2026-06-04 from https://scholargate.app/en/aerospace/madgwick-filter