Research Of SINS/GPS Tightly Integrated Navigation System Based On Pseudo-range And Pseudo-range Rate

Strap-down Inertial Navigation System (SINS) does not depend on any externalinformation, autonomy is strong and data update speed is fast, but the error of SINS willdiverge over time, so SINS is not suitable for long time navigation; Global PositioningSystem (GPS) can work with high precision and long term, its error does not diverge overtime, but there are also some shortcomings such as multipath effects and easily-affectedradio interference, resulting in low stability of navigation. Therefore, GPS can’t performwell as a standalone system. In the high accuracy navigation field, GPS and SINS aremostly integrated together to overcome their own shortcomings, which makes thenavigation performance better than the performance of two separate systems. Therefore,this paper focuses on the study of SINS/GPS tightly coupled system in order to achievebetter navigation performance.Firstly, the SINS/GPS integrated navigation system based on pseudo-range/pseudo-range rate is mainly studied in this paper, and then the mathematical model of thesystem is established. Through the in-depth study of extended kalman filtering algorithm,As far as the nonlinear mathematical model, linearization is earlier than filtering. On thisbasis, filter is designed. SINS is corrected by state estimation each time.Secondly, in the practical application, the SINS/GPS tightly integrated navigationsystem is faced with the situation that state error model is inaccurate or noise statisticalcharacteristics are inconsistent with reality. However, traditional Extended Kalman Filter(EKF) method cannot effectively solve it, causing large filter errors in this system. Thispaper presents a method of GDOP estimation and adaptive Extended Kalman Filter, whichcombines satellite positions of ephemeris with corrected SINS position output instead ofGPS positioning results to calculate GDOP; on this basis, using the GDOP value andinnovation realizes the measurement noise variance matrix Rkonline real-time estimationwhile taking advantage of innovation realizes the state noise variance matrix Qkonlineestimation to achieve the adaptive effect and improve navigation accuracy in tightlyintegrated navigation system.Finally, in order to verify the proposed adaptive extended kalman filtering algorithm iseffective. After the design of SINS/GPS integrated navigation system implementation scheme, the algorithm in Visual C++platform for simulation, and the results are analyzedand evaluated.