Fault Diagnosis Of Multiple-sensor Integrated Navigation Based On Federal Kalman Filter

This paper is against at the background of the complex environment of a certain highlong unmanned aerial vehicle(HLUAV)integrated navigation system,look around the third class issue in the multiple source information fusion problem,we put forward an integrated navigation system of multiple-mode information fusion structure,fault diagnosis,system reconfiguration.This paper mainly focus the following three aspects:The first part,Firstly,Based on KF,We summarize a methods for constructing system equations,and establish the system equation of SINS/GNSSS/CNS/SAR,according to the engineering requirements.Secondly,do some research on the relationship between information allocation coefficient and system performance,then propose a principle of information distribution,providing theoretical basis for FKF;Thirdly,we propose the FKF algorithm based on time series analysis,through analysis the time series information obtain the degree of smoothing,adjust the information distribution coefficient,giving the system structure based on physical meaning,and improving the filtering accuracy,data stationarity and information utilization.The second part.First of all,we do study the fault-tolerant reconfiguration algorithm from two aspects: fault detection and reconstruction algorithm.This paper introduces the basic theory and method of fault detection,we give the basic strategy and the concrete steps of fault detection.Then propose an improving redundant signal reconstruction method and design a fault-tolerant reconfiguration management module.Then we simulate the improved fault-tolerant reconfiguration management algorithm and propose an improved redundant signal voting algorithm.Last,using the statistical information of the difference between the redundant signals,combined with the self-detection and mutual detection mechanism,the fault signal is isolated via voting the information weighted mean,and then we get an export signal adjustment harmony.The third part.at first,We analyze the problem of stochastic weighted estimation of the system error of the combined navigation dynamics model and the observation model,and prove the basic idea of stochastic weighted estimation and the system error of the model.Then,in order to reduce the influence of model error on the state estimation and improve the accuracy of the system,we propose a new stochastic weighted estimation algorithm.In the last we prove that the stochastic weight estimation method can improve the navigation accuracy,reduce the observation error on the state estimation of interference,and improve the accuracy of the navigation system.