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Sensors 2012, 12(12), 17372-17389;

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)
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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

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

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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.

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