Adaptive Deep Learning Algorithm Design for Diverse Inertial Navigation Systems

This paper aims to study the design of deep learning adaptive algorithms for different types of inertial navigation systems. Underwater inertial navigation plays a crucial role in fields such as ocean exploration, but improving its accuracy faces challenges, and combined navigation methods are often adopted. Deep learning is widely applied in inertial navigation, which can be used for sensor data processing, positioning, attitude estimation, and can also be combined with other navigation technologies. Based on previous research, this paper delves into the characteristics of different inertial navigation devices, combines with large language models, and proposes an LLM-based adaptive parameter selection expert system. This system can select appropriate deep learning network parameters based on parameters such as the material, accuracy, and service life of the inertial navigation device to ensure the matching between the model and the inertial navigation device. The network design is divided into a displacement prediction module and a heading angle prediction module, and different targeted designs are adopted. The experiment was conducted in the Yellow Sea, equipped with strapdown fiber optic inertial navigation systems and strapdown laser inertial navigation systems, and targeted training was carried out according to the characteristics of the two sets of systems. The fiber optic inertial navigation system has an earlier factory time and a large data scale; the laser inertial navigation system has a recent factory time and high accuracy. The experimental results show that the error is significantly suppressed after adopting the deep learning algorithm, and it is feasible to use the expert system to drive the setting of targeted parameters, which has the value of promotion and research.