Distributed State Estimation for Dynamic Positioning Systems with Uncertain Disturbances and Transmission Time Delays

Abstract

The dynamic positioning system of unmanned underwater vehicles (UUVs) is a complex and large-scale system mainly due to the nonlinear dynamics, uncertainty in model parameters, and external disturbances. With the aid of the bio-inspired computing (BIC) method, the designed three-dimensional (3D) spatial positioning system is used for enlarging communication constraints and increasing signal coordination processing. With the growing of measurement scales, the issue of the networked high-precision positioning has been developed rapidly. Then, an information fusion estimation approach is presented for the distributed networked systems with data random transmission time delays and lost and disordered packets. To reduce the communication burden, an adaptive signal selection scheme is employed to reorganize the measurement sequence, and the parameter uncertainties as well as cross-correlated noise are used to describe the uncertain disturbances. Moreover, a reoptimal weighted fusion state estimation is designed to alleviate the information redundancy and maintain higher measurement accuracy. An illustrative example obtained from the 3D spatial positioning system is given to validate the effectiveness of the proposed method.

Discrete Weibull-Rayleigh distribution properties and parameter estimations

“…In tests, however, the lifetime measurement of a component or system is often a discrete value, sometimes, the data might be stochastically uncertain [3], and the distributed state estimation becomes important [4] for uncertain data and missing data [5]. So, some scholars put forward the discretization idea of continuous distribution and gave some different methodologies.…”

Evaluation of the suitability of human settlements in Qingpu district, Shanghai

“…However, the practice of qualitative and quantitative evaluation of the suitability of human settlement environments is still in the exploration stage globally. Few existing studies consider the uncertainty [7,8] and missing data [9] in the evaluation system of the suitability on the human settlement environment.…”

A water quality prediction model for large-scale rivers based on projection pursuit regression in the Yangtze River

“…The other method is to use a water quality model to predict. The advantage of the former is that the method is simple and the required parameters are less, and the disadvantage is that a long series of measured water quality data is needed, and sometimes missing data distribution is not known [7] or the data are stochastically uncertain [8,9]. The latter method is more complex and requires more parameters, but it is theoretical [10].…”