Enhancing Positioning in GNSS Denied Environments Based on an Extended Kalman Filter Using Past GNSS Measurements and IMU

Recent urbanization has posed challenges for the global navigation satellite system (GNSS) to provide accurate navigation solutions. This is especially true in GNSS-denied environments, where the clear line of sight (LOS) path between the satellites and receiver is lacking. For such environments, fusion-based techniques relying on external sensors and/or other signals are widely used. However, such external sensors and signals may not be feasible and/or cost-effective every time. To overcome these limitations, this work proposes a system that makes explicit use of past available measurements, under certain assumptions, to generate new synthetic measurements. For this purpose, two functions are proposed in this work: a geometrically decaying series and a linear combination of past measurements. To enhance the overall performance of the system, an inertial measurement unit (IMU) is used as an additional measurement source in the extended Kalman filter (EKF). In addition, an approach to adapt the noise co-variances that support the generation of synthetic measurements is proposed. Furthermore, we derive the optimal gain under specific assumptions for a concrete theoretical understanding of the proposed algorithm. The proposed algorithm is tested and validated through two real-world datasets collected in Hong Kong, one corresponding to a moving vehicle inside a significantly long sea tunnel and another set in a harsh urban area, involving complex trajectories. A detailed analysis of the results has been performed with respect to all the aforementioned contributions. Additionally, the proposed algorithm has been compared with other existing algorithms. Experimental results show that a mean error of about 4 m is attained inside the tunnel, while it is around 4.6 m for the second scenario set in a harsh urban environment.