The dynamic marine environment induces platform motions that compromise the reliability of ocean observation platforms and measurement instruments, leading to errors in the measurement of ocean dynamic parameters such as wind, waves and currents. Throughout its deployment and operational cycle, an ocean observation platform experiences irregular motions. Conventional attitude estimation algorithms based on a nine-axis Micro Inertial Measurement Unit (MIMU), such as Kalman filtering or complementary filtering, often fail to achieve timely convergence, resulting in significant errors in attitude computation. To address this issue, this study proposes a two-stage extended Kalman filtering system for MIMU-based attitude estimation. In the first stage, the system utilizes acceleration data to estimate the initial attitude angles, thereby improving filter convergence speed. In the second stage, it employs hierarchical processing of roll, pitch, and yaw angles to enhance system robustness. Laboratory and field experiments at sea demonstrate that the proposed method achieves rapid convergence and high-accuracy attitude estimation for ocean observation platforms operating under non-stationary motion conditions.