Performance analysis of improved iterated cubature Kalman filter and its application to GNSS/INS

In order to improve the accuracy and robustness of GNSS/INS navigation system, an improved iterated cubature Kalman filter (IICKF) is proposed by considering the state-dependent noise and system uncertainty. First, a simplified framework of iterated Gaussian filter is derived by using damped Newton–...

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Veröffentlicht in:ISA transactions 2017-01, Vol.66, p.460-468
Hauptverfasser: Cui, Bingbo, Chen, Xiyuan, Xu, Yuan, Huang, Haoqian, Liu, Xiao
Format: Artikel
Sprache:eng
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Zusammenfassung:In order to improve the accuracy and robustness of GNSS/INS navigation system, an improved iterated cubature Kalman filter (IICKF) is proposed by considering the state-dependent noise and system uncertainty. First, a simplified framework of iterated Gaussian filter is derived by using damped Newton–Raphson algorithm and online noise estimator. Then the effect of state-dependent noise coming from iterated update is analyzed theoretically, and an augmented form of CKF algorithm is applied to improve the estimation accuracy. The performance of IICKF is verified by field test and numerical simulation, and results reveal that, compared with non-iterated filter, iterated filter is less sensitive to the system uncertainty, and IICKF improves the accuracy of yaw, roll and pitch by 48.9%, 73.1% and 83.3%, respectively, compared with traditional iterated KF. •An improved framework of iterated Gaussian filter for integrate navigation is derived.•Iterated measurement update with state-dependent noise is analyzed.•The effect of different nonlinearities in the filtering of GNSS/INS is studied separately.•Field test and numerical experiment are employed to verify proposed filter.
ISSN:0019-0578
1879-2022
DOI:10.1016/j.isatra.2016.09.010