Measurement Characterization and Autonomous Outlier Detection and Exclusion for Ground Vehicle Navigation With Cellular Signals

• Published in: IEEE transactions on intelligent vehicles Vol. 5; no. 4; pp. 670 - 683
• Main Authors: Maaref, Mahdi, Kassas, Zak M.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anomaly detection; cellular; Data analysis; Global navigation satellite system; GNSS; IMU; Inertial platforms; Land vehicles; Navigation; Outlier detection; Outliers (statistics); Position errors; RAIM; Root-mean-square errors; Signal integrity
• ISSN: 2379-8858; 2379-8904
• DOI: 10.1109/TIV.2020.2991947

An autonomous measurement outlier detection and exclusion framework for ground vehicle navigation using cellular signals is developed. The ground vehicle aids its onboard inertial measurement unit (IMU) with cellular signals in a tightly-coupled fashion in the absence of global navigation satellite system (GNSS) signals. First, cellular pseudoranges are characterized from an extensive wardriving campaign collected with a ground vehicle in different environments: open sky, urban, and deep urban. Then, a framework is developed, which accounts for outliers due to line-of-sight blockage and short multipath delays. These outliers induce biases in cellular pseudoranges and compromise the integrity of the navigation solution. Experimental results are presented evaluating the efficacy of the proposed framework on a ground vehicle navigating in the absence of GNSS signals. The results demonstrate the proposed framework detecting and excluding outliers, reducing the position root mean squared error (RMSE) by 41.5% and the maximum position error by 43.1%.