Authors - AA Adebomehin, FJ Ibrahim, AS Dahiru, TK Akinduyite, TO Obinna-Esiowu, I Ofodile, OA Odeyemi, T Salman Abstract - This paper presents novel AI-enabled Kalman filter techniques that enhance positioning accuracy in inertial navigation systems (INS). Essentially, accurate positioning remains a core challenge in multi-sensor INS; since performance of traditional Kalman filtering degrades in nonlinear environments. Our method significantly improves the precision of INS by integrating adaptive AI-based machine-learning Kalman filters with classical estimation theory. The approach focuses on multi-sensor fusion, adaptive noise modeling, and robust improvements for INS applications. Simulation results demonstrate the effectiveness of the proposed techniques, especially airborne INS. This is significant in that achievement of precision without sacrificing overall accuracy is essential to sensor data in view of factors like sensor thermal noise, lower inference quantization, and tolerance which could affect real–world performances. Additionally, in a multi–sensor fusion setting, effectiveness of INS hinges on crucial and dependable filter systems for real data integration, noise reduction, reliable predictive analysis, and real-time processing. Consequently, this research developed AI-based models and utilized them for all filter GPS positioning data to update the INS states covering INS position output, INS internal states (position, velocity and heading), and finally for estimation of bias on the accelerometer sensor. It is believed that the approach has possible applications in diverse fields like defense & security with favorable implications for autonomous systems; as well as surveys & disaster management. Key highlights of the improved multi-sensor INS algorithm models are presented in this paper.
