Abstract
This paper addresses a design and application for the problem of state estimation for an unmanned autonomous helicopter (UAH) equipped with instruments including an inertial measurement unit (IMU), a magnetometer and a global positioning system (GPS). A dynamic enhanced robust cubature Kalman filter (DERCKF) is proposed in this article. First, a robust filtering strategy is formulated to provide a strong constraint for abnormal values. Second, a new robust CKF is formulated based on the spherical cubature and Gaussian quadrature rules to estimate the probability state, without requiring calculation of the Jacobian and Hessian matrices. Then, an enhanced rule is proposed to help eliminate the accuracy degradation caused by model uncertainty disturbance when the experimental platform is operating and to improve the estimation performance of the filter. Meanwhile, by detecting the system uncertainty state, a dynamic enhanced strategy is formulated to achieve automatic adjustments for the dynamic enhanced robust rule and guarantee that the DERCKF will realize valid system state estimation at all times. Finally, numerical experimental results are presented to demonstrate the effectiveness and robustness of the DERCKF.
Highlights
Unmanned autonomous helicopters (UAHs) have the characteristics of high maneuverability, small size, low cost and vertical takeoff and landing [1], [2]
Jianwang et al proposed a Bayesian-based strong tracking interpolatory cubature Kalman filter (CKF) method to improve the traditional CKF performance in target tracking [28] in simulations, but the fading factor exists during the entire filtering process, and system uncertainty is not considered in the process
Regarding the problem of nonlinear UAH state estimation, the traditional enhanced algorithm requires the Jacobian calculation of the measurement system, which limits the practical applications of the traditional Kalman filter
Summary
Unmanned autonomous helicopters (UAHs) have the characteristics of high maneuverability, small size, low cost and vertical takeoff and landing [1], [2]. The process noise and the state error for UAHs are very difficult to identify, with the characteristics of uncertainty and high compiling Jianwang et al proposed a Bayesian-based strong tracking interpolatory CKF method to improve the traditional CKF performance in target tracking [28] in simulations, but the fading factor exists during the entire filtering process, and system uncertainty is not considered in the process These disadvantages make it difficult to implement these algorithms with UAHs, for which uncertainty appears randomly during the entire process. To overcome the problems of the conventional filters and to improve the state estimation performance, we propose a dynamic enhanced robust cubature. A dynamic enhanced strategy is designed to guarantee that the enhanced rule is injected into the process when the system state meets the enhanced decision criteria by detecting the system uncertainty state
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