Expectation Maximization Based GPS/INS Integration for Land-Vehicle Navigation

Huang, Dongliang; Leung, Henry; El‐Sheimy, Naser

doi:10.1109/taes.2007.4383607

Cited by 27 publications

(10 citation statements)

References 13 publications

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“…However, the fitting is actually an approximation process, thus unavoidably involving error. Huang et al reported an expectation-maximization method to estimate noise statistics, where the maximum likelihood estimation is used to increase the likelihood after each iteration [10,11]. However, this method is computationally expensive, requiring the trade-off between estimation accuracy and computational time.…”

Section: Related Workmentioning

confidence: 99%

Random weighting estimation of kinematic model error for dynamic navigation

Zhong

Gao

Wei

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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This paper presents a new random weighting method to deal with the systematic error of the kinematic model for dynamic navigation. This method incorporates random weights in the kinematic model to control the systematic error of the kinematic model for improving the navigation accuracy. A theory of random weighting estimation is established, showing that 1) the random weighting estimation of the kinematic model's systematic error is unbiased and 2) the covariance matrix of the predicted state vector can be controlled by adjusting the covariance matrices of the predicted residual vector and estimated state vector to improve the accuracy of state prediction. Random weighting estimations are also constructed for the systematic error of the kinematic model as well as the covariance matrices of predicted residual vector, predicted state vector, and state noise vector. Experimental results demonstrate the effectiveness of the proposed random weighting method in resisting the Manuscript disturbances of the kinematic model noise for improving the accuracy of dynamic navigation. Yongmin Zhong is a senior lecturer within the School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Melbourne, Australia. His research interests include virtual reality and haptics, soft tissue modelling and surgery simulation, robotics, mechatronics, optimum estimation and control, and integrated navigation system. Shesheng Gao is a professor at the School of Automatics, Northwestern Polytechnical University, Xi'an, China. His research interests include control theory and engineering, navigation, guidance and control, optimum estimation and control, integrated inertial navigation system, and information fusion.Weihui Wei is going for his Ph.D. degree at the School of Automatics, Northwestern Polytechnical University, Xi'an, China. His research interests include control theory and engineering, navigation, guidance and control, and information fusion.Chengfan Gu is an ARC DECRA fellow within the School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Melbourne, Australia. Her current research activity includes computational modelling, micro/nano manufacturing, fatigue and bio/nano mechanics, and advanced materials characterization.

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Section: Related Workmentioning

confidence: 99%

Random weighting estimation of kinematic model error for dynamic navigation

Zhong

Gao

Wei

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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“…Up to now, many adaptive estimation algorithms [12] have been used to estimate the noise parameters in the Kalman filter process to suppress the divergence of the Kalman filter, such as innovation-based AKF [13], the expectation maximization-based AKF [14] etc. There are also many variants of Kalman filtering and fusion filtering algorithms to suppress divergence, such as Unscented Kalman Filter (UKF) [15], Extended Kalman Filter (EKF) [16], Elimination of Kalman filter [17], genetic filter algorithm, and so on.…”

Section: Introductionmentioning

confidence: 99%

Improved initial alignment algorithm of SINS on shaking base based on Kalman filter

Shi

Hou

2022

Preprint

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In the initial alignment of shaking base, Kalman filter is easy to diverge, and the alignment result is poor. Many scholars introduce various nonlinear filtering methods to solve this problem, but these filtering methods not only have large amount of calculation, but also are low accuracy. Based on the problem, an improved adaptive Kalman filter (IAKF) algorithm is proposed to complete the initial alignment of the shaking base. The experimental results show that the improved algorithm has high anti-interference and anti-divergence ability. Under the simulation conditions, pitch, roll and heading errors can be stabilized at about 10 degrees. The real measurement with Redmi K30Pro mobile phone shows that the alignment result of the improved algorithm is consistent, and the difference of multiple alignment results is stable within 5 degrees, while the difference of traditional method is more than 20 degrees. It can be applied in smart phone navigation with the strapdown inertial navigation system.

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“…INS gives accurate information of position, velocity and attitude [4]. However, the accuracy of the lowcost INS system consisting of micro electrical measurement units degrades sharply and the errors in position and velocity increase over time [5].…”

Section: Introductionmentioning

confidence: 99%

“…The whole ANN will be trained when the GPS signal is available and offers prediction otherwise. In comparison with ANN, Support Vector Machine (SVM) can provide better genetic ability, thus it takes shorter time for training to obtain better training performance [4] [14]. This paper proposes a novel architecture using UKF and SVM regression algorithm to improve the GPS/INS integration system.…”

Section: Introductionmentioning

confidence: 99%

GPS/INS Integrated Navigation Based on UKF and Simulated Annealing Optimized SVM

Jiang

Liu

Zhang

et al. 2013

2013 IEEE 78th Vehicular Technology Conference (VTC Fall)

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The accuracy of Global Positioning System (GPS) is often combined with the reliability of Inertial Navigation System (INS) to accomplish navigation. This paper proposes an innovative way to filter and fuse the GPS and INS information. UKF is employed to simulate the information convergence of the dynamic model which maintains better performance in nonlinear system. So we can obtain a fair precise filtering result when both are online. At the same time, the INS data is trained with the result as training target when it is the unique input. This paper raises the idea that Support Vector Machine (SVM) is adopted to train the INS data during GPS outage and the simulated annealing is applied to realize the optimization of the parameters of kernel function and the penalty function in the SVM algorithm. Therefore, the integration navigation could retain almost as precise as the GPS when the GPS is off-line.

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Expectation Maximization Based GPS/INS Integration for Land-Vehicle Navigation

Cited by 27 publications

References 13 publications

Random weighting estimation of kinematic model error for dynamic navigation

Random weighting estimation of kinematic model error for dynamic navigation

Improved initial alignment algorithm of SINS on shaking base based on Kalman filter

GPS/INS Integrated Navigation Based on UKF and Simulated Annealing Optimized SVM

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