NINT: Neural Inertial Navigation Based on Time Interval Information in Underwater Environments

Constrained by the significant attenuation of electromagnetic waves in water, inertial navigation has become the core autonomous and controllable navigation method for underwater navigation. However, traditional inertial navigation methods suffer from increasing errors in inertial devices over time, leading to rapid divergence and loss of navigational capability in the absence of external correction source information input. Moreover, high-precision inertial navigation systems (INSs) are often expensive and difficult to apply in consumer-grade autonomous underwater vehicles (AUVs). This article conducts an in-depth study on the time factor that has been overlooked in traditional inertial navigation, focusing on the inherent errors of inertial devices, vehicle attitude changes during navigation, and error propagation in positioning. The study explores the application of the time factor in deep learning methods for inertial navigation, proposing a pure inertial deep learning navigation method based on time constraints. This method incorporates time factors neglected in traditional navigation methods and designs multiple motion-state self-switching models tailored to different vehicle motion states. Finally, the effectiveness of the method is validated through actual long-range sea trials, with experimental results demonstrating higher prediction accuracy compared to other mainstream methods and effective suppression of error divergence.