Microelectromechanical inertial sensors with embedded attitude determination algorithms have become standard in modern wave measurement buoys, though their proprietary nature often limits transparency in evaluating wave parameter accuracy. This paper presents the results of a field experiment with a prototype wave measuring buoy, in which raw triaxial accelerometer, gyroscope, and magnetometer data were recorded onto a memory card with minimal preprocessing. Subsequent post-processing was performed using various attitude estimation algorithms with open-source and easily accessible software implementations. The study examined both direct methods based on gravity and magnetic field measurements and more complex approaches, including the complementary filter and its variations (Mahony and Madgwick filters), as well as the Kalman filter and its extended version. The resulting attitude estimates enabled computation of both spectral wave characteristics and bulk parameters including significant wave height, peak period and mean direction. Comparative analysis against reference resistive wave gauge measurements revealed algorithm-dependent performance in the context of sea wave measurement. These findings offer practical insights for scenarios requiring either post-processing of raw buoy data or development of optimized embedded systems where full raw data transmission is not feasible.
Yurovsky et al. (Mon,) studied this question.
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