A new unscented Kalman filter controlled variablestructureinteracting multiple model technique is developed in this paper. The model set of this multiple model algorithm is built by different coordinated turn models. The corresponding turn rates and the state estimation process are realized by unscented Kalman filtering and ensures significant self-adjusting capabilities. The algorithm is disti...

To combat the impacts of uncertain noise on estimation vehicle state parameters and high cost sensors, a state-observer design with an adaptive unscented Kalman filter (AUKF) is developed. The equation observer derived by establishing vehicle’s three degrees-of-freedom (DOF) model. On this basis, Sage–Husa algorithm (UKF) are combined to form AUKF adaptively update statistical feature measureme...

This article proposes a detailed nonlinear mathematical model of an antagonistic pneumatic artificial muscle (PAM) actuator system for estimating the joint angle and torque using unscented Kalman filter (UKF). The proposed is described in hybrid state-space representation. It includes contraction force PAM, dynamics, fluid dynamics compressed air, mass flows valve, friction models. A part model...

In this paper, we propose a channel tracking method for massive MIMO systems under both timevarying and spatial-varying circumstance. Exploiting the characteristics of massive antenna array, a spatial-temporal basis expansion model (ST-BEM) is designed to reduce the effective dimensions of uplink/downlink channel, which decomposes channel state information (CSI) into the time-varying spatial in...