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Minimum dispersion coefficient criteria based positioning algorithm for BDS

Wang L., Li L.

Archives of Electrical Engineering

2018

Vol. 67, nr 4

739--753

The BeiDou navigation satellite system (BDS) is one of the four global navigation satellite systems. More attention has been paid to the positioning algorithm of the BDS. Based on the study on the Kalman filter (KF) algorithm, this paper proposed a novel algorithm for the BDS, named as the minimum dispersion coefficient criteria Kalman filter (MDCCKF) positioning algorithm. The MDCCKF algorithm adopts minimum dispersion coefficient criteria (MDCC) to remove the influence of noise with an alpha-stable distribution (ASD) model which can describe non-Gaussian noise effectively, especially for the pulse noise in positioning. By minimizing the dispersion coefficient of the positioning error, the MDCCKF assures positioning accuracy under both Gaussian and non-Gaussian environment. Compared with the original KF algorithm, it is shown that the MDCCKF algorithm has higher positioning accuracy and robustness. The MDCCKF algorithm provides insightful results for potential future research.

Modeling of joint signal detection and parameter estimation on the background of non-Gaussian noise

Palahin V., Filipov V., Leleko S., Ivchenko O., Palahina O.

Journal of Applied Mathematics and Computational Mechanics

2015

Vol. 14, nr 3

87--94

The paper presents the results and describes the process of modeling of a system of joint signal detection and parameter estimation on the background of a non-Gaussian noise based on moment and cumulant description of random variables, polynomials of Kunchenko and moment quality criterion type of Neyman-Pearson.

Stochastic multivariable self-tuning tracker for non-Gaussian systems

Filipovic V.

International Journal of Applied Mathematics and Computer Science

2005

Vol. 15, no 3

351-357

This paper considers the properties of a minimum variance self-tuning tracker for MIMO systems described by ARMAX models. It is assumed that the stochastic noise has a non-Gaussian distribution. Such an assumption introduces into a recursive algorithm a nonlinear transformation of the prediction error. The system under consideration is minimum phase with different dimensions for input and output vectors. In the paper the concept of Kronecker’s product is used, which allows us to represent unknown parameters in the form of vectors. For parameter estimation a stochastic approximation algorithm is employed. Using the concept of the stochastic Lyapunov function, global stability and optimality of the feedback system are established.