Application Of Nonlinear Filtering In Marine Integrated Navigation

In the field of modern navigation systems,INS(Inertial Navigation Systems)feature the advantages of autonomy and high mobility.However,because the systems work alone for an extended period of time,errors that accumulate over time cause the navigation information to diverge.By contrast,GNSS(Global Navigation Satellite Systems)provide stable and continuous high-precision navigation information.Nevertheless,GNSS signals are vulnerable to external interferences that lead to navigation information losses.By integrating INS and GNSS together,it resolves the aforementioned problems.Thus,INS/GNSS combining the advantages of INS and GNSS has become an option for developing integrated navigation systems used by ships today.This study investigated the nonlinear filtering algorithms of INS/GNSS integrated navigation systems.An improved particle filter algorithm is proposed to deal with the failure of particle filter over time in integrated navigation.First,this study explained how the INS and GNSS worked.And the corresponding integrated navigation solution equations are established according to the solving characteristics of inertial navigation system.Secondly,it focuses on the study of the four commortly used filtering algorithms in integrated navigation,analyzes the shortcomings of the four filtering algorithms in practical applications,and establishes the error state model and the full state filtering model of integrated navigation respectively according to the characteristics of the algorithm.Through the simulation analysis of a simple two-dimensional motion model,it is shown that the filter technology can play an accurate and stable role in integrated navigation.Finally,for the particle filter prediction stage,an improved particle filter algorithm was proposed,solving the problem where external observed values were unable to correct predicted value errors when the prior predicted value errors were overly large.By including the latest observed values and using them to fix predicted values in advance,this study matched importance functions with likelihood functions,eliminating the problem of particle importance weight degradation.The filter algorithms were tested in various simulations using Python,where the simulation experiment verified that the improved particle filter algorithm could improve the precision and stability of integrated navigation systems...