Performance Evaluation of Mems Inertial Sensors for Autonomous Naval Platforms

This study evaluates the performance of MEMS-based inertial navigation systems (INS) for autonomous surface vehicles operating under varying maritime conditions. A comprehensive simulation framework is developed by integrating stochastic sensor error models, strapdown INS mechanization, and an Extended Kalman Filter (EKF) for sensor fusion. Maritime disturbances, including wave-induced motion and vibration noise, are modeled using a spectral-based approach to represent realistic sea states. Two sensor classes, low-cost and tactical-grade MEMS, are analyzed under calm and rough sea conditions. The results demonstrate that low-cost sensors exhibit rapid error growth in unaided conditions, while tactical-grade sensors provide improved stability. The proposed EKF-based approach significantly mitigates error accumulation, reducing the overall drift by approximately 60–80% under periodic GNSS aiding conditions. However, performance degradation is observed in high sea states due to nonlinear motion and increased noise levels. The findings highlight that low-cost MEMS sensors can be effectively utilized in short-duration missions with appropriate filtering, whereas tactical-grade sensors remain essential for long-term and high-reliability maritime operations.