A Nonlinear Sate Estimate for Dynamic Positioning Based on Improved Particle Filter

Yongjia, Chen; Xuan-fang, Yang; Liu, Z.

doi:10.1109/imcec.2018.8469693

“…When the linearization procedure does not provide a fine approximation for highly nonlinear systems, another estimation method called the unscented Kalman filter (UKF) is used. Instead of approximating a nonlinear function as EKF does, such filters approximate the probability distribution [9,51,52]. The algorithm is based on Bayesian theory and the deterministic sampling technique, also known as unscented transform (UT).…”

Section: Kalman Filtering Techniquesmentioning

confidence: 99%

Signals and Images in Sea Technologies

2021

0

View full text Add to dashboard Cite

show abstract

“…Such calculations represent a strain on the system since the calculation of parameters should be obtained in real time. Furthermore, the KF is driven by white noise, which further contributes to the problems and limitations of achieving an accurate estimation [22,52].…”

Section: Kalman Filtering Techniquesmentioning

confidence: 99%

“…When the linearization procedure does not provide a fine approximation for highly nonlinear systems, another estimation method called the unscented Kalman filter (UKF) is used. Instead of approximating a nonlinear function as EKF does, such filters approximate the probability distribution [9,51,52]. The algorithm is based on Bayesian theory and the deterministic sampling technique, also known as unscented transform (UT).…”

Section: Kalman Filtering Techniquesmentioning

confidence: 99%

“…This filter can approximate any arbitrary distribution, and it is not limited to a Gaussian noise only. It is based on the Monte Carlo method, and approximation is done using the probability density function with the sample mean instead of the integral operation [52]. The more accurate approximation is obtained by use of more particles.…”

Section: Kalman Filtering Techniquesmentioning

confidence: 99%

“…In another work, Jaros et al (2018) [58] provided a comparative analysis of two particle KF formulations based on a kinematic model and/or a dynamic ship model. Similarly, Chen et al (2018) [52] in the proposed algorithm used PF as a framework and additionally UKF as a density function for particle state optimization. This approach has proven satisfactory in terms of estimating the low-frequency oscillation of the ship.…”

Section: Kalman Filtering Techniquesmentioning

confidence: 99%

See 2 more Smart Citations

Improving the Performance of Dynamic Ship Positioning Systems: A Review of Filtering and Estimation Techniques

Lerga

¹

,

Lerga

²

,

Prpić-Oršić

³

et al. 2020

JMSE

View full text Add to dashboard Cite

Various operations at sea, such as maintaining a constant ship position and direction, require a complex control system. Under such conditions, the ship needs an efficient positioning technique. Dynamic positioning (DP) systems provide such an application with a combination of the actuators mechanism, analyses of crucial ship variables, and environmental conditions. The natural forces of induced nonlinear waves acting on a ship’s hull interfere with the systems. To generate control signals for actuators accurately, sensor measurements should be filtered and processed. Furthermore, for safe and green routing, the forces and moments acting on the ship’s hull should be taken into account in terms of their prediction. Thus, the design of such systems takes into account the problem of obtaining data about the directional wave spectra (DWS). Sensor systems individually cannot provide high accuracy and reliability, so their measurements need to be combined and complemented. Techniques based on the recursive Kalman filter (KF) are used for this purpose. When some measurements are unavailable, the estimation procedure should predict them and, based on the comparison of theoretical and measured states, reduce the error variance of the analyzed signals. Different approaches for improving estimation algorithms have evolved over the years with the indication of improvement. This paper gives an overview of the state-of-the-art estimation and filtering techniques for providing optimum estimation states in DP systems.

show abstract