Reliable navigation in environments where Global Positioning System (GPS) signals are unavailable or degraded remains a critical challenge for autonomous systems, defense operations, and underground or indoor applications. This research proposes a robust navigation framework that leverages Doppler-based velocity estimation to enhance positioning accuracy in GPS-denied environments. The system integrates inertial measurement units (IMUs) with Doppler shift observations derived from radio frequency or acoustic signals to provide continuous and drift-reduced localization. A sensor fusion approach, combining Extended Kalman Filtering and machine learning-based error correction, is employed to mitigate accumulated drift and measurement noise. The proposed model is evaluated in complex scenarios such as urban canyons, tunnels, and indoor settings, demonstrating improved trajectory estimation and resilience compared to conventional inertial-only methods. Experimental results indicate that Doppler-assisted navigation significantly enhances reliability, reduces positional error, and ensures continuous operation in challenging conditions. This approach offers a scalable and efficient solution for next-generation navigation systems in autonomous vehicles and robotics.
Yogapriya et al. (Thu,) studied this question.