Research On Theories And Methods Of Deeply Coupled GNSS/INS Integrated Navigation

GNSS/INS deeply coupled integrated navigation system has become the research hotspot inrecent years, and it is a kind of deeply integrated model, which involves the GNSS receivertracking loop, and much deeper information fusion degree. The essential performance ofGNSS/INS deeply integrated navigation system is: using INS navigation solution and satelliteephemeris, calculating the Doppler frequency in the direction from receiver to satellite, andfeedback to the GNSS receiver tracking loop in real time to make the GNSS receiver notsensitive to dynamic stress; at the same time, using the integrated navigation filter to exportcorrection message of INS, this insure the GNSS receiver only tracks Doppler error caused byINS calculation error, GNSS receiver crystal and external noise. When GNSS signal is interferedor shaded, INS navigation solution can achieve seamless navigation, predict the dynamicinformation of carrier and then make continuously estimate of Doppler of GNSS receiver andphase shift, Doppler frequency and phase shift information continuously estimated, which cangreatly reduce the signal re-capture time of GNSS receiver.This paper aims at studying the theory of GNSS/INS deeply coupled integrated navigation,the basic of realization and the overall performance promotion of deeply coupled integratednavigation system. The contents cover the GNSS/INS integrated navigation theory, simulationplatform, INS aided GNSS signal acquisition and tracking, algorithms of GNSS/INS deeplycoupled integrated navigation, fault diagnosis of GNSS/INS deeply integrated navigation model.The main work and conclusions of the paper are summarized as follows:1. The GNSS/INS deeply coupled integrated navigation simulation platform is constructed.The GSNRx-utTM design ideas of PLAN research team in Canada are referenced to develop theGNSS/INS deeply coupled integrated navigation simulation platform based on C#programlanguage. Several modules are included in the simulation platform, such as trajectory generator,GNSS intermediate frequency signal simulator, GNSS receiver baseband signal processing, IMUsignal generator, INS navigation algorithm, integrated navigation filter, which are convenient forevaluation and validation of GNSS/INS deeply coupled integrated navigation performance, themerits of the integrated filtering algorithm, and also are the foundation of the follow-up study.2. A kind of INS aided GNSS receiver signal acquisition algorithm is designed. Acquisitionperformance of GNSS signal (signal detection statistics index and the average capture time) isanalyzed, and the integration time and Doppler estimation deviation and code phase estimationdeviation are emphatically studied to evaluate the influence on performance of acquisition. Inallusion to the fast acquisition of GNSS signals under the condition of high dynamic, the arithmetic of INS aiding GNSS receiver to acquire signal was proposed to analyze the suitabilityof GNSS signal and INS. The transcendental information of code phase and Doppler frequencyshift are gotten by using the position and velocity of INS combined with ephemeris information,as the center of code/carrier NCO. INS error as the search boundary, the time of GNSS receiversignal acquisition is shorten, and acquisition performance is improved. The simulation resultsshows that the low accuracy of the INS aided GNSS signal capture, in the120s, acquisitionsensitivity can be increased by4.2dB-Hz at least; Under the cold start, the average acquisitiontime decreases from132s to4s.3. The structure and method of INS aidding GNSS receiver tracking loop are researched andanalyzed. DLL/PLL/FLL of GNSS receiver tracking loop discriminator is studied.The carrierloop noise bandwidth, dynamic characteristic and received signal carrier noise ratio to theDLL/PLL/FLL tracking error are analyzed. A high dynamic GNSS receiver tracking loopstructure is designed. A bandwidth threshold iterative algorithm is proposed. Focusing on theanalysis of INS aided mathematical model of the GNSS receiver carrier tracking loop andstructural method. Through the simulation optimization INS aided by GNSS receiver trackingloop parameter Settings, the tracking performance of deep integrated navigation system wasimproved. Simulation results show that under the condition of high dynamic, the low accuracy ofINS aided third-order PLL loop can keep locking and tracking signal with low noise ratio.4. The centralized deep GNSS/INS integrated navigation system mathematical model isdeduced. System model of centralized deep combination is analyzed. Centralized deepGNSS/INS integrated navigation system mathematical model is deduced in detail. Themeasurement equation and status equation of the integrated navigation filter are given. Theextrapolation method based on software is used to achieve time synchronization problembetween different parameters. The cascading and centralized deep combination model aresimulated and analysed, the result shows that centralized deep integrated navigation system ontracking performance and the navigation performance is better than cascading deep integratednavigation system under the condition of low dynamic and high dynamic. when The performanceof the deep GNSS/INS integrated navigation is better in slow speed motion overall high speed.When GNSS signal attenuation, the tracking loop error of deep integrated navigation systemincreases, but as the loop is still locked, the navigation precision turns lower, but can also meetthe users’ requirement of high precision.5. Using UKF in the filter to solve the nonlinear problem of the centralized GNSS/INSintegrated navigation. Using UKF algorithms in integrated navigation filter. Simulation resultshows that the tracking performance of centralized deeply integrated navigation model based onUKF is better than that of kalman filter. 6. A kind of fault diagnosis and isolation method is designed for the centralized GNSS/INSintegrated navigation system. For the sake of avoiding one certain channels correlator faultinformation "polluting" the whole system, the method using residual error sequence to establishthe time series model of the GNSS receiver correlator was proposed. At the time of GNSSoutputting I/Q channels abnormal information this guaranteed the precision of the integratednavigation system. Simulation results show that the proposed fault diagnosis method is a goodway to avoid the fault information’s influence on navigation performance. A centralizedGNSS/INS integrated navigation deep integration software was designed and developed, thismade a firm foundation for the subsequent engineering realization.