Implementation of Parallel Cascade Identification at Various Phases for Integrated Navigation System

Umar Iqbal, Ashraf Abosekeen, Jacques Georgy, Areejah Umar, Aboelmagd Noureldin and Michael J. Korenberg
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Umar Iqbal: Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39759, USA
Ashraf Abosekeen: Electrical Engineering Branch, Military Technical College (MTC), Cairo 11762, Egypt
Jacques Georgy: TDK-InvenSense Inc., #405, 1000 Veterans Place NW, Calgary, AB T3B 4M1, Canada
Areejah Umar: Department of Biological Science, Mississippi State University, Starkville, MS 39759, USA
Aboelmagd Noureldin: Department of Electrical and Computer Engineering, Royal Military College of Canada (RMCC), Kingston, ON K7K7B4, Canada
Michael J. Korenberg: Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON K7L3N6, Canada

Future Internet, 2021, vol. 13, issue 8, 1-17

Abstract: Global navigation satellite systems (GNSS) are widely used for the navigation of land vehicles. However, the positioning accuracy of GNSS, such as the global positioning system (GPS), deteriorates in urban areas due to signal blockage and multipath effects. GNSS can be integrated with a micro-electro-mechanical system (MEMS)–based inertial navigation system (INS), such as a reduced inertial sensor system (RISS) using a Kalman filter (KF) to enhance the performance of the integrated navigation solution in GNSS challenging environments. The linearized KF cannot model the low-cost and small-size sensors due to relatively high noise levels and compound error characteristics. This paper reviews two approaches to employing parallel cascade identification (PCI), a non-linear system identification technique, augmented with KF to enhance the navigational solution. First, PCI models azimuth errors for a loosely coupled 2D RISS integrated system with GNSS to obtain a navigation solution. The experimental results demonstrated that PCI improved the integrated 2D RISS/GNSS performance by modeling linear, non-linear, and other residual azimuth errors. For the second scenario, PCI is utilized for modeling residual pseudorange correlated errors of a KF-based tightly coupled RISS/GNSS navigation solution. Experimental results have shown that PCI enhances the performance of the tightly coupled KF by modeling the non-linear pseudorange errors to provide an enhanced and more reliable solution. For the first algorithm, the results demonstrated that PCI can enhance the performance by 77% as compared to the KF solution during the GNSS outages. For the second algorithm, the performance improvement for the proposed PCI technique during the availability of three satellites was 39% compared to the KF solution.

Keywords: land vehicle navigation; system identification; inertial sensors; GNSS; Kalman filter; parallel cascade identification (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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