Abstract: The integration of the Inertial Navigation System (INS) and the Global Positioning System (GPS) is widely applied to seamlessly determine the time-variable position and orientation parameters of a system for navigation and mobile mapping applications. For optimal data fusion, the Kalman filter (KF) is often used for real-time applications. Backward smoothing is considered an optimal post-processing procedure. However, in current INS/GPS integration schemes, the KF and smoothing techniques still have some limitations. This article reviews the principles and analyzes the limitations of these estimators. In addition, an on-line smoothing method that overcomes the limitations of previous algorithms is proposed. For verification, an INS/GPS integrated architecture is implemented using a low-cost micro-electro-mechanical systems inertial measurement unit and a single-frequency GPS receiver. GPS signal outages are included in the testing trajectories to evaluate the effectiveness of the proposed method in comparison to conventional schemes.
Keywords: on-line smoothing; INS/GPS integration; Kalman filter
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Chiang, K.-W.; Duong, T.T.; Liao, J.-K.; Lai, Y.-C.; Chang, C.-C.; Cai, J.-M.; Huang, S.-C. On-Line Smoothing for an Integrated Navigation System with Low-Cost MEMS Inertial Sensors. Sensors 2012, 12, 17372-17389.
Chiang K-W, Duong TT, Liao J-K, Lai Y-C, Chang C-C, Cai J-M, Huang S-C. On-Line Smoothing for an Integrated Navigation System with Low-Cost MEMS Inertial Sensors. Sensors. 2012; 12(12):17372-17389.
Chiang, Kai-Wei; Duong, Thanh T.; Liao, Jhen-Kai; Lai, Ying-Chih; Chang, Chin-Chia; Cai, Jia-Ming; Huang, Shih-Ching. 2012. "On-Line Smoothing for an Integrated Navigation System with Low-Cost MEMS Inertial Sensors." Sensors 12, no. 12: 17372-17389.