Research On Data Processing Algorithm For GNSS/INS Integrated Navigation And Positioning

Navigation refers to the technology or method of guiding the carrier from the starting point to the destination,and its application has penetrated into all aspects of human society.The progress of science and technology and the development of society put forward higher and higher requirements for precise navigation and positioning,in many cases,a single navigation system cannot meet all users' requirement of accuracy,availability and reliability.Multiple navigation systems are required for simultaneous measurement and integrated processing to obtain more accurate and reliable results to improve the ability of navigation and positioning service.Global Navigation Satellite System(GNSS)and Inertial Navigation System(INS)have complementary advantages and disadvantages.They are currently the most practical form of integrated navigation and the focus of researchers.Therefore,the integration navigation system of GNSS and INS is selected as the research object.First,the basic algorithm research on positioning and velocity estimation of GNSS and INS is derived.Then,the loosely and tightly coupled integration algorithms of GNSS/INS were studied and proposed.On this basis,a method of non-holonomic constraints based on the carrier motion is derived,which effectively improves the navigation performance of INS when it loses GNSS for a long time.Then,the RTS smoothing filter algorithm is proposed and studied.The accuracy of the GNSS/INS integrated navigation post-processing algorithm is effectively improved with the RTS smoothing filter algorithm.In addition,in order to further reduce the cost and improve the accuracy of the integrated navigation system,an integrated navigation algorithm based on the BDS RTK and low-cost IMU is achieved and shows ideal results in vehicular test.The research content of this thesis is mainly composed of three parts.(1)GNSS positioning and velocity estimation algorithm,involving GNSS basics,GNSS positioning and velocity estimation algorithm with high precision,etc;(2)INS position,velocity and attitude determination algorithm,involving INS basics,INSupdating algorithm and INS error analysis,etc;(3)GNSS/INS integrated navigation algorithm and its enhanced algorithm,involving GNSS/INS loosely and tightly integration model,non-holonomic constraints algorithm,GNSS/INS high precision post-processing algorithm based on RTS smoothing filter,and the integration navigation algorithm based on BDS RTK and low-cost INS.The major works of this thesis are as follows:(1)The mathematical model and algorithm implementation of the GNSS positioning algorithm including SPP,PPP,RTK and the GNSS velocity estimation algorithm including Doppler and TDCP are completed.The data of IGS station is used to verify the correctness of the algorithm.The results show that the RTK algorithm can quickly achieve centimeter-level positioning,and the PPP algorithm can also achieve centimeter-level accuracy after convergence.And the TDCP algorithm shows higher accuracy than the Doppler algorithm in GNSS velocity estimation.(2)On the basis of defining the common used coordinate system of inertial navigation and deriving the transformation matrix,the algorithm for INS position,velocity and attitude updating is given in detail,and the error model of INS is derived through disturbance analysis.Based on the simulation data test,the feasibility of the algorithm is verified.The results show that the INS has high short-term accuracy,but the error accumulates rapidly over time,which make it difficult to achieve long-term autonomous navigation and positioning.(3)Introduce and derive the mathematical models of GNSS/INS loosely and tightly coupled integration,and gives the architecture of GNSS/INS data processing.On this basis,the GNSS/INS integration algorithm is implemented.And through the test data,the advantages of the tightly-coupled integration algorithm with less than 4 satellites are verified.In order to further improve the accuracy of GNSS/INS integrated navigation,the RTS smooth filtering algorithm is proposed.By effectively using the measurement information for forward and backward filtering,the error of position,velocity and attitude are significantly reduced.Considering in the practical application of vehicle-mounted integrated navigation system,the GNSS often loses lock.The GNSS/INS non-holonomic constraints algorithm which is based on the characteristics of vehicle-mounted is derived,and is verified through vehicular test data.The results show that non-holonomic constraints can effectively reduce the cumulative error of INS within a certain period of time,so that when GNSS loses lock for a long time,it can continue to output reliable navigation information.(4)Based on the BDS which is independently developed and operated by China and low-cost MEMS IMU,a mathematical model of the low-cost BDS RTK/INS integrated system has been derived and implemented.The performance of positioning,velocity estimation and attitude estimation of integrated navigation system is analyzed and evaluated through vehicular test data.The results show that the low-cost BDS RTK+MEMS performs well in positioning and velocity estimation,which has a certain practical value.