Research On The Key Technologies Of MEMS-SINS/GPS Integration Navigation Syetem

The integration of Strapdown Inertial Navigation System (SINS) based on thetechnology of Micro-Electro Mechanical Systems (MEMS) and Global PositioningSystem (GPS) provides a feasible solution for the implementation of navigationsystem featured with low-cost, small scale and lightweight. With the implementationof the navigation system of UAV as background, the inertial-assisted tightly-coupledstructure as a framework, this dissertation studies the key technologies, whichinvolves MEMS-Inertial Measurement Uunit (IMU) error analysis and compensationscheme, inertial-assisted receiver tracking loop design, integrated navigation systemfilter design, to improve the accuracy and the reliability of MEMS-SINS/GPSintegrated navigation system.The main accomplishments are listed below:(1) The error analysis and compensation scheme has been studied to establishthe high-precision error model for MEMS-IMU. As to the systematic error, the modeland calibration schemes are proposed for error mitigation; as to the stochastic error,the major error terms and parameters are identified using Allan variance for errormodeling. The error model provides design input for the simulation of inertialinstrument and the design of integrated navigation system filter.(2) The inertial-aided tightly-coupled structure is designed to solve the problemof the GPS outages in high dynamic environment. The nonlinear model for SINS error is analyzed and established, and the nonlinear measurement model for thepseudo-range difference and pseudo-range rate difference is derived, providing thebasis for the filter design. An approach based on the concept of feedforward toreconfigure the PLL model is introduced, eliminating the effects of dynamic stress.High dynamic experiment shows the reliable tracking for the signal is possible underthe dynamic circumstances of50g/s. The structure solves the problem of loss of lockcaused by the dynamics stress fundamentally and practicality.(3) As to the nonlinearity issue in integrated navigation model, the filteringaccuracy and calculation complex of Extended Kalman Filtering (EKF) andUnscented Kalman Filter (UKF) are studied, the improved UKF filtering algorithm isproposed, with UKF executing time update and EKF executing sequencialmeasurement update. Simulation result demonstrates that the accuracy of theimproved algorithm is the same as UKF, but30%better than EKF, and the calculationtime has been reduced by45%compared with UKF, meeting the system accuracy andreal-time requirements.(4) To make MEMS-based SINS/GPS meet the accuracy requirements duringGPS outages, Radial Basis Function Neural Network (RBFNN) aided adaptiveKalman filtering information fusion method is proposed. RBFNN training strategyand Kalman filtering measurement noise adaptive algorithm are designed. Vehicleexperiment shows that the position error is within15m during40s GPS outages; andwithin90m during100s GPS outages. The proposed method can effectively damp thedivergence of the navigation error during the GPS outages.(5) The prototype of the MEMS-SINS/GPS inertial-assisted tightly-coupledintegrated navigation system is designed; the calibration system for inertial sensor andthe hardware-in-loop simulation system are established. The proposed system solutionhas been validated by means of hardware-in-loop simulation, dynamic test, andvehicle experiment, result shows that position error is within7m, velocity error within0.4m/s, attitude error within0.2°and bearing error within0.6°.