Enhancing Inertial Navigation Accuracy through Integrated MEMS Accelerometers and GPS with Advanced AI Modeling

Inertial navigation systems (INS) are fundamental for precise positioning in various applications, including aerospace, robotics, and autonomous vehicles. However, traditional INS often suffers from drift errors over time, leading to degraded accuracy. To address this challenge, this paper proposes a novel approach that integrates data from several MEMS accelerometers and a GPS receiver through advanced artificial intelligence (AI) modeling.

The proposed system utilizes four MEMS accelerometers and a GPS receiver to collect multi-dimensional data related to motion and position. These datasets are then used to train a machine learning model, specifically designed to learn and predict the relationship between accelerometer inputs and corresponding positioning estimations. The AI model employs sophisticated algorithms to effectively fuse the information from all sensors, thereby compensating for the limitations of individual sensors and enhancing the overall accuracy of the navigation system.

The novelty of this approach lies in the integration of MEMS accelerometer data with GPS measurements, being coupled with the advanced AI-based machine learning models. By leveraging the inherent strengths of MEMS accelerometers for capturing motion dynamics and the accuracy of GPS for absolute positioning, the proposed system achieves improved navigation performance while mitigating the effects of drift errors over time.

Experimental results demonstrate the effectiveness of the proposed method in enhancing inertial navigation accuracy, particularly in scenarios where GPS signals may be obstructed or unavailable.