约简二次扩展平方根容积卡尔曼滤波及其应用

doi:10.7527/S1000-6893.2014.0002

Abstract

Abstract:

The traditional nonaugmented cubature Kalman filter (CKF) fails to estimate the status of a nonlinear dynamic system because of its complex additive noises. By analyzing the accuracy of the nonaugmented and augmented cubature transformation, a reduction algorithm of twice augmented square-root CKF (RTA-SRCKF) is proposed based on the reduction of the augmented cubature points. The novel filter, by twice augmenting the process noise and the measurement noise respectively in the time-update and the measurement-update step, is suitable for estimating the states of the nonlinear dynamic system with complex additive noises. Without any loss of estimation accuracy, its complexity is simplified, the computational cost is reduced and real-time performance is also improved since its number of sampling points is greatly reduced. The theoretical analysis result is verified by the initial alignment simulation of the strapdown inertial navigation system (SINS) with large misalignment angles and the estimation accuracy of the azimuth misalignment angle is close to its limit precision in theory.

Key words: nonlinear filter, twice augmentation, square-root cubature Kalman filter, strapdown inertial navigation system, initial alignment, large misalignment angle, real-time performance

CLC Number:

V249.3
TN713

ZHAO Xijing, WANG Lixin, HE Zhikun, ZHANG Bo. Reducted Twice Augmented Square-root Cubature Kalman Filter and Its Application[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2014, 35(8): 2286-2298.

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Reducted Twice Augmented Square-root Cubature Kalman Filter and Its Application

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