An Improved Gaussian Filter for Dynamic Positioning Ships With Colored Noises and Random Measurements Loss | Zendy

Xiaogong Lin | Zendy; Yuzhao Jiao | Zendy; Dawei Zhao | Zendy

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An Improved Gaussian Filter for Dynamic Positioning Ships With Colored Noises and Random Measurements Loss

Author(s) -

Xiaogong Lin,

Yuzhao Jiao,

Dawei Zhao

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2789336

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

An improved Gaussian Filter (GF) is designed for nonlinear Dynamic Positioning (DP) ships with cross-correlated colored noises and random measurements loss. For the actual nonlinear Dynamic Position System (DPS), the state noises and measurement noises do not satisfy the assumption of Gaussian white noises and the loss of measurements may occur randomly. Therefore, the following circumstances are considered: the state noises and measurement noises are cross-correlated colored noises at the same and adjacent sampling moments; the measurement loss occurs randomly for the data transmission between the sensor units and the estimator units. In order to get the estimator for nonlinear DP ships with cross-correlated colored noises and random measurements loss, a GF framework based on Bayesian theory is proposed, and then the Cubature Mix Kalman Filter based on spherical-radial method is obtained. In the end, the simulation results show that the proposed algorithm has better estimation performance than Unscented Kalman Filter with Measurements Loss and standard Cubature Kalman Filter.

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