Improved GPS/IMU Loosely Coupled Integration Scheme Using Two Kalman Filter-based Cascaded Stages
- PDF / 5,572,949 Bytes
- 23 Pages / 595.276 x 790.866 pts Page_size
- 53 Downloads / 160 Views
RESEARCH ARTICLE-ELECTRICAL ENGINEERING
Improved GPS/IMU Loosely Coupled Integration Scheme Using Two Kalman Filter-based Cascaded Stages Nader Nagui1 · Omneya Attallah1 · M. S. Zaghloul1 · Iman Morsi1 Received: 30 October 2019 / Accepted: 11 November 2020 © King Fahd University of Petroleum & Minerals 2021
Abstract Despite the fact that accelerometers and gyroscopes are used in inertial navigation systems (INS) to provide navigation information without the aid of external references, accumulated systematic errors are shown in sensor readings on long-term usage. In this work, a new approach is proposed to overcome this problem, by using extended Kalman filter (EKF)—linear Kalman filter (LKF), in a cascaded form, to couple the GPS with INS. GPS raw data are fused with noisy Euler angles coming from the inertial measurement unit (IMU) readings, in order to produce more consistent and accurate real-time navigation information. The proposed algorithm is designed to run through three software threads simultaneously. The multi-thread processing provides better use of hardware resources and applies more efficient INS/GPS loosely coupled integration scheme compared to the conventional method. Two datasets are used to verify the efficacy of the proposed approach against the existing GPS/INS coupling techniques. The first set is synthetic data generated by MATLAB that represents a static vehicle at known coordinates. The second one is a real road test data collected in Ontario, Canada. Accordingly, the root mean square error (RMSE) values for the proposed approach in ENU directions have reached 0.022, 0.034 and 0.010 m, respectively, for a static vehicle, as well as 0.493, 0.453 and 0.110 m, respectively, for a movable vehicle—which is notably competitive with other recent related work. Keywords Extended Kalman filter · Inertial navigation system · Sensor fusion · Global positioning system · Geo-referencing applications
1 Introduction An autonomous navigation system (ANS) has been involved lately in several applications such as military missions, ocean environment inspection, automotive cars, agricultural missions, and forest surveys. Predominantly any ANS possesses two types of sensors [1]: a dead-reckoning sensor and an external sensor. Normally, dead-reckoning sensors; e.g.,
B B
Nader Nagui [email protected] Omneya Attallah [email protected] M. S. Zaghloul [email protected] Iman Morsi [email protected]; [email protected]
1
Department of Electronics and Communications Engineering, Arab Academy for Science Technology & Maritime Transport, Alexandria PO1029,, Egypt
accelerometers and magnetometers are very powerful but accumulate errors with time. Therefore, they must be periodically updated by information from external sensors, e.g., GPS that provides absolute tidings by making measurements from known waypoints. However, GPS signal accuracy decreases with time. This is due to several physical phenomena reasons such as shadowing, signal attenuation and multipath. Thus, another navigation syst
Data Loading...
