2024
DOI: 10.1016/j.asr.2023.07.026
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Backward smoothing adaptive SVDCKF integrated navigation algorithm

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Cited by 2 publications
(3 citation statements)
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“…The sampling time interval is ∆t, and the first derivative of the IF at the time t ′ is shown in equation (24),…”
Section: Inlstft Algorithm Principlementioning
confidence: 99%
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“…The sampling time interval is ∆t, and the first derivative of the IF at the time t ′ is shown in equation (24),…”
Section: Inlstft Algorithm Principlementioning
confidence: 99%
“…data fusion algorithms are used to optimize the output data of measuring components to reduce measurement errors. Typical data fusion algorithms are the Kalman filter [17,19,20] (KF), extended KF [21], unscented KF [22] (UKF), and cubature KF [23][24][25] (CKF), which can achieve relatively accurate estimation while ensuring reliable sensor output. In addition, Liu et al [26] proposed an optimal data fusion algorithm based on adaptive fading maximum correlation entropy generalized height CKF (AFCCKF-ODF) to enhance the robustness of integrated navigation systems.…”
Section: Introductionmentioning
confidence: 99%
“…The reason behind this lies in the fact that under vehicle motion, the output of in-ertial devices contains significant noise. How to distinguish between valid data and noise, and reduce the impact of vibration noise on system stability and positioning accuracy, remains to be studied [8][9][10]. In different scenarios, when the effectiveness of sensors in the integrated navigation system changes, corresponding adjustments need to be made to the state space model [11,12].…”
Section: Introductionmentioning
confidence: 99%