Due to the randomness and uncertainty in atmospheric environment, accompanied by a variety of unknown noise. Accurate prediction PM 2.5 concentration is very important for people prevent injury effectively. In order predict more accurately this hybrid modelling method support vector regression adaptive unscented Kalman filter (SVR-AUKF) proposed case incorrect or Firstly, model was established ...

Abstract The secret key rate of a continuous-variable quantum distribution (CV-QKD) system is limited by excess noise. A issue typical to all modern CV-QKD systems implemented with reference or pilot signal and an independent local oscillator controlling the noise generated from frequency phase accrued transmitter receiver. Therefore accurate estimation compensation, so-called carrier recovery,...

To ensure the safety of space station and improve accuracy estimated trajectory tracking noncooperative target, an optical imaging micro-/nanosatellite based on APSO-SSUKF (adaptive particle swarm optimization-spherical simplex unscented Kalman filter) is proposed to track low-orbit target using angle-only measurement. First, algorithm considers effect J2 perturbation, uses data as observation ...

Book review on Edita Gromová, Natália Rondziková, Igor Tyšš (eds.) (2021) Archívny výskum (textov) v interdisciplinárnych súvislostiach, Nitra: Katedra translatológie FF UKF Nitre.

We consider the problem of estimating a nonlinear state-space model whose state process is driven by an ordinary differential equation (ODE) or a stochastic differential equation (SDE), with discrete-time data. We propose a new estimation method by minimizing the conditional least squares (CLS) with the conditional mean function computed approximately via unscented Kalman filter (UKF). We deriv...

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...