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Sensors 2012, 12(12), 17372-17389; doi:10.3390/s121217372

On-Line Smoothing for an Integrated Navigation System with Low-Cost MEMS Inertial Sensors

Department of Geomatics, National Cheng-Kung University, 1 University Road, Tainan 701, Taiwan
Industrial Technology Research Institute, 195 Chung Hsing Road, Chutung, Hsinchu 310, Taiwan
Author to whom correspondence should be addressed.
Received: 17 October 2012 / Revised: 6 December 2012 / Accepted: 7 December 2012 / Published: 13 December 2012
(This article belongs to the Section Physical Sensors)


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. View Full-Text
Keywords: on-line smoothing; INS/GPS integration; Kalman filter on-line smoothing; INS/GPS integration; Kalman filter

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

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.

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