A Novel Unscented Kalman Filter Strategy to Enhance Navigation System Performance
M. Mahmoud1, I. Alaa2, A. Wassal3, A. Noureldin4
1M. Mahmoud, Lecturer Assistant computer engineering, Cairo University, Cairo, Egypt.
2I. Alaa, Ph.D., Alberta University, Canada.
3A. Wassal, Professor computer engineering, Cairo University, Cairo, Egypt.
4A. Noureldin, Professor Electrical and Computer Engineering, RMC / Queens University, Kingston, Ontario, Canada.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 2446-2453 | Volume-8 Issue-5, January 2020. | Retrieval Number: E5649018520/2020©BEIESP | DOI: 10.35940/ijrte.E5649.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Extended Kalman Filter (EKF) is the most widely estimation algorithm used for nonlinear system such as a navigation system to fuse an inertial navigation system (INS) with Global Positioning System (GPS) which its information has complementary nature to get more accurate navigation information. Unfortunately, the performance of INS/GPS fusion using EKF is degraded due to the linearization error and GPS error. Therefore, a new algorithm is developed to overcome these issues. This algorithm uses the sampling-based Unscented Kalman Filter (UKF) to solve the linearization problem, and ignore the GPS reading when there is a large error in its measurements. The new algorithm is named Adaptive Loosely Coupled Unscented Kalman Filter (ALCUKF). The ALCUKF-based INS/GPS systems are presented for two different datasets. The first dataset is acquired using a high-end tactical-grade SPAN unit featuring Novatel HG1700 IMU module. The second dataset is acquired from a MEMS-based SCC1300-D04 IMU unit from VTI. The results of the new method are compared against reference ground truth trajectories and measured quantitatively using the Root Mean Square Error (RMSE). The ALCUKF increased the navigation system performance significantly when compared with EKF for both datasets as shown in the paper.
Keywords: Adaptive Loosely Coupled Unscented Kalman Filter (ALCUKF), Extended Kalman Filter (EKF), Global Positioning Systems (GPS), Inertial Navigation Systems (INS), Micro- Electro-Mechanical-System (MEMS), Root Mean Square Error (RMSE), Unscented Kalman Filter (UKF).
Scope of the Article: Mechanical Design.