Research On Measurement Methods For Dynamic Positioning Systems Based On Nonlinear Estimation Fusion Of STF-ASCKF

With the rapid growth of marine engineering field,ships are equipped with dynamic positioning systems(DPS)to meet the needs of marine resource development,seabed exploration,and marine scientific research.The peformance of a DPS is fundamentally dependent on the validity and accuracy of the acquired measurement data.To precisely capture the ship’s position and attitude information,DPS employs a variety of sensors,thus constructing a multi-sensor measurement system.However,the measurement accuracy of the vessel’s state is significantly compromised by factors such as time-varying interference induced by complex sea conditions,uncertainty in vessel model parameters and noise characteristics,and electromagnetic interference experienced by sensors.To mitigate these challenges,this thesis proposes a novel approach combining nonlinear estimation and multi-sensor fusion methods and applies it to the measurements taken by the DPS to improve the positioning accuracy and reliability.The main works of the thesis can be summarized into the following aspects:Firstly,the motion characteristics of DP vessel are analyzed by considering the impact of environmental factors such as wind,waves,and currents.The three-degree-of-freedom(3DOF)kinematic and dynamic equations for vessels are established in both the inertial and body-fixed coordinate systems.Based on the measurement principles of different sensors,corresponding measurement system models were constructed.Secondly,to eliminate the interference of rolling and pitching on sensor measurements,a compensation method based on the L1-ELM forecasting approach is proposed.Concurrently,to address issues such as inconsistencies in sampling times,differences in data types among various sensors,and the occurrence of outliers,data processing operations such as time alignment,spatial alignment,as well as quality detection are conducted on the measurement data.Thirdly,considering the uncertainty of the noise and model parameters,a Strong Tracking Adaptive Square Root Cubature Kalman Filter(STF-ASCKF)is proposed.The algorithm employs Maximum-a-Posteriori estimation and Variational Bayesian estimation to assess the unknown process noise variance and measurement noise variance,respectively.By dynamically adjusting the prediction error covariance using the strong tracking fading factor,the impact of parameter uncertainty is mitigated.The performance of the algorithm is validated through theoretical analysis and simulation experiments,confirming that the algorithm maintains reasonable computational complexity while demonstrating high stability and estimation accuracy.Subsequently,based on the STF-ASCKF as a sub-filter,distributed multi-sensor fusion structures are designed using the scalar-weighted fusion framework and the federated filtering framework.The STF-ASCKF is converted into an equivalent information filtering form,the STF-ASCIF,and a distributed fusion algorithm based on the STF-ASCIF is proposed.The key feature of the fusion algorithm is the introduction of a method for computing the global strong tracking fading factor,which effectively reduces the data transmission volume and computational burden of the fusion center.Finally,the aforementioned improved method for extracting dynamic positioning measurement information is applied to the DPS.By simulating and comparing the PID positioning control under two different sea conditions,the effectiveness of the proposed method in positioning control is thereby verified.