Research On GNSS/INS Deep Integrated Navigation Of Reusable Launch Vehicle

This dissertation is mainly focused on the research of GNSS/INS deep integrated navigation for reusable launch vehicle(RLV). The traditional simplex integrated navigation has its inherent flaws of low level of information mergence, poor reliability and poor navigation precision. Therefore, in order to enhance RLV’s navigation reliability, continuity and high precision in flight round trip, a GNSS/INS deep integrated navigation system is designed. The primary contents are as follow:First of all, define the common coordinate system and its transformation for RLV, and carry out its force analysis. In the launch system, establish kinematics model of the reusable launch vehicle. Design flight plan and flight sequence of RLV, and complete the simulation in the whole flight trajectory, which is the foundation of the follow-up deep integrated navigation system design and simulations.Second, the research is focused on GNSS/INS deep integrated navigation. Integrated navigation system consists of strapdown inertial navigation system and satellite navigation system. Respectively, construct the model for the two systems and induct the error propagation model; analyze acquisition and tracking system for the INS-assisted GPS satellite signals, design the loop circuit for acquisition and tracking, and establish the mathematical model for the carrier loop circuit of the INS assisted GPS receptor, as well undertake an emulation proof; based on the established strapdown inertial system, the satellites and the loop aided models, construct a model for the deep integrated satellite navigation system, carry out the filtering computing and the comprehensive emulation analysis of the integrated navigation in the light of the traditional Kalman filtering algorithm; substitute the traditional satellite navigation system for the difference satellite navigation system in the terms of the problems on the time delay, for which the two component systems analyze and put forward the resolution for the environmental interference delay and for the dynamic tracking delay, respectively.Finally, the systems carry out the analyzing work for the optimization method of the attitude estimation, adopting the adaptive unscented Kalman filter integrated navigation attitude estimation, construct the integrated navigation model based on the position, speed and attitude errors as the state parameter. As well contrast attitude estimation generated by the precision between the traditional Kalman filter and the adaptive unscented Kalman filter; and experiment with the validity and rationality of the attitude estimation for the integrated navigation out of the adaptive unscented Kalman filter.