An adaptive information fusion method to vehicle integrated navigation

Wu, Qiuping; Gao, Zhenzhong; De-jun, Wan

doi:10.1109/plans.2002.998915

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…Liu proposes an algorithm to compute ISF by comparing the inverse of the norm of the error covariance of each local filter [14], which considers only the error covariance. In [15], Qiuping considers GPS constellation in the determination of the ISF for a vehicular application, which is, however, not applicable to other applications that do not use GPS. Zhang proposes an algorithm to compute the ISF based on the generalized eigenvalue decomposition of the error covariance of each local filter [16], and Zhi proposes an algorithm to compute the ISF using the eigenvalue of the error covariance and the singular value of the observability matrix [17].…”

Section: Introductionmentioning

confidence: 99%

Slip and Slide Detection and Adaptive Information Sharing Algorithms for High-Speed Train Navigation Systems

Kim

Kong

Jeon

2015

IEEE Trans. Intell. Transport. Syst.

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The position and velocity information of highspeed trains (HSTs) are essential to passenger safety, operational efficiency, and maintenance, for which an accurate navigation system is required. In this paper, we propose a twostage federated Kalman filter (TS-FKF) for an HST navigation system that uses multi-sensors, such as tachometer, inertial navigation system, differential GPS, and RFID, with a feedback scheme. However, the FKF with a feedback scheme often shows severe performance degradation in the presence of undetected large sensor errors. Tachometers often have large slip or slide errors during the train's acceleration, deceleration, and moving along a curved railway, and there are significant performance differences between different sensors. To make the proposed system robust to these errors, we propose a slip and slide detection algorithm for the tachometer and an adaptive information-sharing algorithm to deal with a large tachometer error and performance difference between sensors. We provide theoretical analysis and simulation results to demonstrate the performance of the proposed navigation system with the proposed algorithms.Index Terms-Train navigation system, positioning, slip and slide detection, sensor fusion, federated Kalman filter, information sharing.

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Section: Introductionmentioning

confidence: 99%

Slip and Slide Detection and Adaptive Information Sharing Algorithms for High-Speed Train Navigation Systems

Kim

Kong

Jeon

2015

IEEE Trans. Intell. Transport. Syst.

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“…Additionally, DR system is a selfcontained, autonomous and high sampling rate navigation system compared with GPS. However error of DR will accumulate with time [3,4] . The integration of GPS and DR system combines the short-term stability of DR system with the long-term stability of GPS, and the integrated system improves performance in terms of accuracy, reliability, integrity and availability.…”

Section: Introductionmentioning

confidence: 99%

“…EKF algorithm is used in [3][4][5][6][7]. The SEKF is also involved in [8], and it can give better results than that of the FEKF.…”

Section: Introductionmentioning

confidence: 99%

The Application and Design of Second Order EKF Based on GPS/DR Integration for Land Vehicle Navigation

Zhang

Rong

Zhong³

et al. 2008

2008 International Conference on MultiMedia and Information Technology

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This paper proposes a land vehicle navigation algorithm based on the global positioning system (GPS) and the dead reckoning (DR).To achieve high location and velocity accuracy, second order extended Kalman filter (SEKF) is introduced for GPS/DR integrated navigation system. And the algorithm of the first extendedKalman filter (FEKF) and the second EKF (SEKF) are given. Further, the state models and measurement models of GPS/DR are set up. For comparision purpose, the algorithm performance of FEKF and SEKF is contrasted. Numerical results demonstrate that the proposed SEKF algorithm gives much higher navigation accuracy than FEKF algorithm for land vehicle navigation. The algorithm has obvious advantages of high accuracy.

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“…While a Kalman filter is usually employed to fuse the and measurements, this approach requires a priori knowledge on the stochastic and deterministic parameters of both systems. GPS and DR integrated navigation system has been widely studied [1][2].…”

mentioning

confidence: 99%

The performance comparison and algorithm analysis of first order EKF, second order EKF and smoother for GPS/DR navigation

Zhang

Rong

Zhong

2008

2008 11th IEEE International Conference on Communication Technology

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Several algorithms based on the global positioning system (GPS) and the dead reckoning (DR) are proposed. For comparison purpose, the performance of the GPS/DR integrated navigation system is analyzed with first extended Kalman filter (FEKF), second extended Kalman filter (SEKF) and the Rauch Tung Striebel-smoother (RTS). In this paper, the state models and measurement models of GPS /DR are set up. Furthermore, the GPS/DR integrated navigation system based on the three algorithms is simulated, and the algorithm performance is compared by the simulation results. The numerical emulation demonstrates that the EKF-RTS gives clearly better estimates than the EKF, and the SEKF is superior to the FEKF.

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An adaptive information fusion method to vehicle integrated navigation

Cited by 9 publications

References 4 publications

Slip and Slide Detection and Adaptive Information Sharing Algorithms for High-Speed Train Navigation Systems

Slip and Slide Detection and Adaptive Information Sharing Algorithms for High-Speed Train Navigation Systems

The Application and Design of Second Order EKF Based on GPS/DR Integration for Land Vehicle Navigation

The performance comparison and algorithm analysis of first order EKF, second order EKF and smoother for GPS/DR navigation

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