An Innovative Approach for Improving Navigation Performance of Robust Land-Based Vehicles

Mostafa Ghouneem - Cairo University, Giza, 12613, Egypt
Amr Wassal - Cairo University, Giza, 12613, Egypt


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.3-2.2647

Abstract


The Extended Kalman Filter (EKF) stands as a prominent choice within navigation systems, particularly in scenarios involving the integration of a Reduced Inertial Sensor System (RISS) with the Global Positioning System (GPS). However, despite its widespread adoption, the EKF grapples with many challenges, including the propensity to underestimate filter uncertainties, contend with unreliable GPS signals, and confront errors stemming from linearization processes. These issues invariably contribute to a decline in overall system performance. Considering these challenges, this paper endeavors to introduce a groundbreaking integration algorithm to mitigate the inherent shortcomings of the EKF. The proposed algorithm employs innovative strategies to address these challenges comprehensively. Firstly, it incorporates a dynamic self-tuning mechanism meticulously designed to improve filter configuration in real-time, ensuring adaptability to varying operating conditions. The algorithm also integrates a meticulously engineered GPS Integrity algorithm to filter out mistaken readings and bolster the reliability of the navigation solution. Furthermore, the algorithm adopts the Unscented Kalman Filter (UKF), renowned for handling non-linearities directly, thereby cutting the need for the cumbersome linearization procedures inherent in the EKF. Comparative evaluations against the traditional EKF method prove the effectiveness of the proposed approach. Significant performance enhancements are evident using two datasets from a VTI SCC1300-D04 IMU unit compared to high-precision Novatel SPAN ground truth data. These improvements are quantified through RMSE analysis, showing substantial strides in navigation accuracy. Overall, the results underscore the transformative potential of the proposed integration algorithm in advancing navigation system capabilities.

Keywords


Navigation system; Reduce Inertial Sensor System; Inertial Navigation Systems; Extended Kalman Filter; Unscented Kalman Filter; Global Positioning System; Micro- Electro-Mechanical-System; Root Mean Square Error.

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References


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