Square Root Cubature Kalman Filter-Kalman Filter Algorithm for Intelligent Vehicle Position Estimate
Jianmin Duan, Hui Shi, Dan Liu, Hongxiao Yu
Abstract
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Jianmin Duan, Hui Shi, Dan Liu, Hongxiao Yu
Abstract
Open-access reader
A new filtering algorithm, adaptive square root cubature Kalman filter-Kalman filter (SRCKF-KF) is proposed to reduce the problems of amount of calculation, complex formula-transform, low accuracy, poor convergence or even divergence. The method uses cubature Kalman filter (CKF) to estimate the nonlinear states of model while its linear states are estimated by the Kalman filter (KF). The simulation and practical experiment results show that, compared to the extended Kalman filter (EKF) and unscented Kalman filter (UKF). The modified filter not only enhances the numerical stability, guarantees positive definiteness of the state covariance, but also increases accuracy, which has high practicability.
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A new filtering algorithm, adaptive square root cubature Kalman filter-Kalman filter (SRCKF-KF) is proposed to reduce the problems of amount of calculation, complex formula-transform, low accuracy, poor convergence or even divergence. The method uses cubature Kalman filter (CKF) to estimate the nonlinear states of model while its linear states are estimated by the Kalman filter (KF). The simulation and practical experiment results show that, compared to the extended Kalman filter (EKF) and unscented Kalman filter (UKF). The modified filter not only enhances the numerical stability, guarantees positive definiteness of the state covariance, but also increases accuracy, which has high practicability.
Key concepts: Extended Kalman filter, Fast Kalman filter, Invariant extended Kalman filter, Unscented transform, Alpha beta filter, Kalman filter, Ensemble Kalman filter, Control theory (sociology)