Modern algorithms for constructing predictive models in MEMS-based inertial navigation systems

Authors: Nabieva Z.Z.
Published in issue: #4(105)/2026
DOI:
Category: Instrument Engineering, Metrology, Information-Measuring Instruments and Systems | Chapter: Instruments and Measuring Methods
Keywords: inertial navigation systems, micro-electromechanical systems, predictive models, MEMS sensor error, error correction, error prediction, machine learning, multilayer neural networks
Published: 22.09.2026

This article presents a review of modern algorithms for constructing predictive models for inertial navigation systems (INS) using microelectromechanical-system (MEMS) sensors. It is shown that the transition from linear and quasilinear models to fully nonlinear architectures significantly enhances the accuracy of approximation and short-term prediction of drift, bias, and noise components in MEMS sensors, including operation under complex thermal and dynamic conditions. Promising research directions are outlined, including the use of generative models for the synthesis of navigation and error signals, the development of quantum-classical hybrid algorithms for sensor parameter optimization, and the implementation of adaptive multi-level architectures that integrate physical, statistical, and neural network components into a unified predictive system.

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