TY - JOUR

T1 - Angular velocity estimation based on adaptive simplified spherical simplex unscented Kalman filter in GFSINS

AU - Wu, Qingya

AU - Jia, Qingzhong

AU - Shan, Jiayuan

AU - Meng, Xiuyun

PY - 2014/6

Y1 - 2014/6

N2 - In this paper, the adaptive simplified spherical simplex unscented Kalman filter was proposed to calculate angular velocity in gyro-free strapdown inertial navigation system. Firstly, a general angular velocity calculation modeling method with time-varying process noise was proposed, which was not limited to a certain kind of accelerometer configuration. Then aiming at the issues of large amount of calculation of unscented Kalman filter and the time variation of the process noise, and based on the characteristics of additive noise and linear state equation, the adaptive simplified spherical simplex unscented Kalman filter was proposed to estimate the angular velocity. The sampling points were decreased in this method through adopting the spherical simplex sampling strategy and not augmenting the state, thus improving the calculation efficiency. Meanwhile, Sage-Husa suboptimal maximum a posteriori noise estimator was brought in to estimate the process noise in real time in order to settle the problem of filter divergence induced by the time variation. Lastly, the proposed algorithm was simulated and also contrasted with the integration method, the evolution method and the conventional adaptive UKF algorithm. The simulation results indicated that the adaptive simplified spherical simplex unscented Kalman filter algorithm has higher precision than the integration method and evolution method and has higher efficiency than the AUKF, which could effectively improve the calculation precision and meanwhile guarantee the calculation efficiency.

AB - In this paper, the adaptive simplified spherical simplex unscented Kalman filter was proposed to calculate angular velocity in gyro-free strapdown inertial navigation system. Firstly, a general angular velocity calculation modeling method with time-varying process noise was proposed, which was not limited to a certain kind of accelerometer configuration. Then aiming at the issues of large amount of calculation of unscented Kalman filter and the time variation of the process noise, and based on the characteristics of additive noise and linear state equation, the adaptive simplified spherical simplex unscented Kalman filter was proposed to estimate the angular velocity. The sampling points were decreased in this method through adopting the spherical simplex sampling strategy and not augmenting the state, thus improving the calculation efficiency. Meanwhile, Sage-Husa suboptimal maximum a posteriori noise estimator was brought in to estimate the process noise in real time in order to settle the problem of filter divergence induced by the time variation. Lastly, the proposed algorithm was simulated and also contrasted with the integration method, the evolution method and the conventional adaptive UKF algorithm. The simulation results indicated that the adaptive simplified spherical simplex unscented Kalman filter algorithm has higher precision than the integration method and evolution method and has higher efficiency than the AUKF, which could effectively improve the calculation precision and meanwhile guarantee the calculation efficiency.

KW - Gyro-free

KW - Sage-Husa

KW - angular velocity calculation

KW - spherical simplex sampling

KW - unscented Kalman filter

UR - http://www.scopus.com/inward/record.url?scp=84900547387&partnerID=8YFLogxK

U2 - 10.1177/0954410013492255

DO - 10.1177/0954410013492255

M3 - Article

AN - SCOPUS:84900547387

SN - 0954-4100

VL - 228

SP - 1375

EP - 1388

JO - Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering

JF - Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering

IS - 8

ER -