A hybrid error modeling for MEMS IMU in integrated GPS/INS navigation system

Continuity of accurate navigational data for intelligent transportation applications has been widely provided by utilizing low-cost navigation systems through integrating GPS with micro-electro-mechanical-system (MEMS) inertial sensors. To achieve the required accuracy, augmentation of Kalman filter (KF) with nonlinear error modeling techniques such as fast orthogonal search (FOS) was introduced to enhance the navigational solution by estimating and eliminating a great part of both linear and nonlinear errors of azimuth angle sensed by MEMS gyro. Although this augmented approach enhanced the overall navigational accuracy to some extent, it still suffers from some drawbacks that diverge the system accuracy during GPS long outage periods. These drawbacks stem from the wide-variational behavior and high nonlinearities of the errors in MEMS gyros which make it difficult to depend on the non-adaptive linear error model provided by KF to model the two types of MEMS azimuth errors.In this paper we tried to minimize the effect of uncertainties associated with the KF azimuth prediction during the absence of GPS by introducing a hybrid error model which employs support vector machine (SVM) to model the KF output and FOS, based on autoregressive (AR) concept, to model the nonlinear azimuth errors. The performance of the proposed hybrid SVM-FOS approach is evaluated for GPS/ RISS (Reduced inertial sensor system integrated system) and the results were compared with the conventional KF and augmented KF-FOS approaches.

[1]  Masayoshi Tomizuka,et al.  Novel hybrid of strong tracking Kalman filter and wavelet neural network for GPS/INS during GPS outages , 2013 .

[2]  Nesreen I. Ziedan,et al.  Modeling of Nonlinear Errors for Integrated GPS/MEMS-based INS Navigation Systems , 2016 .

[3]  Aboelmagd Noureldin,et al.  A New High-Resolution GPS Multipath Mitigation Technique Using Fast Orthogonal Search , 2016 .

[4]  Mark G. Petovello,et al.  Benefits of Combined GPS/GLONASS with Low-Cost MEMS IMUs for Vehicular Urban Navigation , 2012, Sensors.

[5]  Guo Wei,et al.  Application of Least Squares-Support Vector Machine in system-level temperature compensation of ring laser gyroscope , 2011 .

[6]  Aboelmagd Noureldin,et al.  Low-Cost Three-Dimensional Navigation Solution for RISS/GPS Integration Using Mixture Particle Filter , 2010, IEEE Transactions on Vehicular Technology.

[7]  Aboelmagd Noureldin,et al.  An Enhanced 3D Multi-Sensor Integrated Navigation System for Land-Vehicles , 2014 .

[8]  U. Iqbal,et al.  An integrated reduced inertial sensor system — RISS / GPS for land vehicle , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

[9]  Aboelmagd Noureldin,et al.  Robust Modeling of Low-Cost MEMS Sensor Errors in Mobile Devices Using Fast Orthogonal Search , 2013, J. Sensors.

[10]  Aboelmagd Noureldin,et al.  Three-Dimensional Reduced Inertial Sensor System/GPS Integration for Land-Based Vehicles , 2013 .

[11]  Yun-Wen Huang,et al.  Improving the attitude accuracy of a low cost MEMS/GPS integrated system using GPS heading sensors , 2007 .

[12]  Aboelmagd Noureldin,et al.  Nonlinear Error Modeling of Reduced GPS/INS Vehicular Tracking Systems Using Fast Orthogonal Search , 2014 .

[13]  Achim Wagner,et al.  Cascaded Kalman and particle filters for photogrammetry based gyroscope drift and robot attitude estimation. , 2014, ISA transactions.

[14]  Nguyen Quang Vinh.  INS/GPS Integration System Using Street Return Algorithm and Compass Sensor , 2017 .

[15]  Marco Pini,et al.  Loose and Tight GNSS/INS Integrations: Comparison of Performance Assessed in Real Urban Scenarios , 2017, Sensors.

[16]  Gong Zhang,et al.  GPS/INS Integrated Navigation Based on UKF and Simulated Annealing Optimized SVM , 2013, 2013 IEEE 78th Vehicular Technology Conference (VTC Fall).

[17]  Zhi Shen.  Nonlinear Modeling of Inertial Errors by Fast Orthogonal Search Algorithm for Low Cost Vehicular Navigation , 2012 .

[18]  Ertuğrul Çam,et al.  Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines , 2015 .

[19]  Hongguang Jia,et al.  Error Prediction for SINS/GPS after GPS Outage Based on Hybrid KF-UKF , 2015 .

[20]  M. J. Korenberg,et al.  A robust orthogonal algorithm for system identification and time-series analysis , 1989, Biological Cybernetics.

[21]  Jeroen D. Hol,et al.  Sensor Fusion and Calibration of Inertial Sensors, Vision, Ultra-Wideband and GPS , 2011 .

[22]  Xiyuan Chen,et al.  INS/WSN-Integrated Navigation Utilizing LS-SVM and ∞ Filtering , 2012 .

[23]  Aboelmagd Noureldin,et al.  Low cost two dimension navigation using an augmented Kalman filter/Fast Orthogonal Search module for the integration of reduced inertial sensor system and Global Positioning System , 2011 .