Optimized Inertial Tracking and Dead Reckoning for Motion Estimation in Dynamic Systems
Rishabh Satish Changwani · 2025
Accurate motion estimation is essential for navigation and tracking in dynamic systems. This study presents an optimized approach to inertial tracking using sensor fusion techniques, integrating an Inertial Measurement Unit (IMU) with advanced filtering algorithms. The study evaluates multiple orientation estimation methods, including complementary, Mahony, Madgwick, and Extended Kalman filters, to achieve precise positioning in real-time applications. Additionally, a dead reckoning framework is developed to estimate displacement over time, mitigating drift through bias compensation and adaptive corrections. Experimental validation demonstrates the effectiveness of the proposed approach in tracking motion with reduced computational overhead. The findings contribute to improving localization accuracy in systems reliant on inertial navigation without external positioning data.