Online Evaluation And Optimization Of INS/GNSS Integrated Navigation

Aircraft such as launch vehicles have higher and higher requirements for the accuracy performance of navigation systems.For an aircraft with high dynamics and complex flight environment,a key issue in limiting the performance of navigation accuracy is the “consistence between actual flight and ground test” problem,which includes the measurement model parameters and the ground binding filtering parameters of integrated navigation are inconsistent with the actual situation.Therefore,this paper focuses on the deep utilization of integrated navigation information,taking SINS/GNSS integrated navigation system as the research object,mainly to solve the online evaluation and optimization of integrated navigation.Firstly,SINS/GNSS integrated navigation filtering model based on velocity and position observation is established.For the simulation flight trajectory of a launch vehicle,observability analysis and cross-impact analysis of the navigation states are carried out,and the states that can be corrected online are determined.Secondly,for the limitation of online evaluation without external true value reference,the quantitative index functions of accuracy,convergence and reliability are proposed from the perspective of observation data quality and filtering quality.The meaning and internal relationship of each index are studied,and an online evaluation index system is established.The polarity and sensitivity of different indexes to the change of filtering noise parameters are analyzed by simulation experiments.Then,based on the time characteristic difference of SINS and GNSS errors,the deep information fusion is studied,which realizes observation noise parameters of GNSS calculated online by using the short-term high-precision incremental information of SINS.What's more,the rationality of this idea is verified by the real flight data.At the same time,the Sage-Husa adaptive filtering based on suboptimal unbiased maximum posterior estimation is improved and applied to online estimation of system noise parameters.Combining the above researches,an optimization algorithm and parallel architecture for SINS/GNSS integrated navigation are proposed.On the basis of conventional integrated navigation algorithms,the architecture carries a parallel module to realize online estimation of filtering noise and filtering solution.By synthetically evaluating the performance of different filtering noise parameters,the optimal estimation is determined.The state correction decision is made according to the state correction decision,and the determined filtering noise parameters are fed back to the conventional algorithm,which realize online optimization of navigation states and filtering parameters.Based on the proposed optimal algorithm and architecture,the realization steps of filtering noise parameters online estimation are provided;an online comprehensive evaluation index function is obtained combining the Analytic Hierarchy Process(AHP);the online correction decision of navigation states is studied,and the effectiveness of state correction decision is verified by simulation.Finally,based on a simulation flight data,for two scenarios: one is the ideal situation that the actual observation noise has not changed significantly and the filtering parameters are consistent with the actual,the other one is the harsh situation that the actual observation noise changed significantly and the filtering parameters are inconsistent with the actual,the effectiveness of the optimal design algorithm is verified by comparison.The simulation results show that the optimal design algorithm can estimate the filtering noise parameters and navigation states in parallel,and then determine the best noise parameters and navigation states at the current time through comprehensive performance evaluation.At the same time,this optimal algorithm can realize the online feedback of speed,position,attitudes and accelerometer bias through state correction decision.Also,the correction of filtering parameters is completed.All the results show that the optimization algorithm and parallel architecture can improve the accuracy and expand application scope of integrated navigation system.