Research On Position Error Suppression Methods Of MINS/GNSS Integrated Navigation System For Vehicle

Under the trend of intelligence,informatization,and Internet of Things,terminal positioning such as drones,unmanned ships,and unmanned vehicles has become more and more important.On urban roads,accurate and real-time positioning of vehicles is of great significance to reducing urban traffic congestion.In addition,the accurate positioning of vehicles is conducive to route planning and reduces transportation costs.In this application context,the integrated navigation system combined with the Micro Inertial Navigation System(MINS)and the Global Navigation Satellite System(GNSS)is becoming an important part in the vehicle navigation.In order to improve the positioning accuracy of the integrated navigation system,this thesis studies the positioning error suppression methods of MINS/GNSS integrated navigation system.There are three main factors that affect the positioning error of the MINS/GNSS integrated navigation system,namely the error of the MEMS inertial sensor,the change of the error characteristics of the GNSS positioning information(referring to the position information decoded by the GNSS receiver),and the GNSS outage.These three factors that affect the positioning performance of the MINS/GNSS integrated navigation system are studied separately.The main research work and innovations of this thesis are as follows.(1)In order to realize the low-cost and high-precision calibration requirement of MEMS inertial sensors,the six-position parameter correction method is proposed.Firstly,the error model of the output data of MEMS inertial sensor is studied,and then the data characteristics at different positions are analyzed.On this bias,the six-position parameter correction method is proposed for the parameter estimation of MEMS accelerometer and gyroscope under approximately constant temperature.The simulation results show that the proposed method can obtain a scale factor with a relative error of 0.06%,a bias estimation with an error of 0.5 mg,and non-orthogonal parameters with a maximum error of 0.006(equivalent to an angular error of 0.35°).Repeated calibration experiments also demonstrate the effectiveness of the six-position parameter correction method.Due to the consistency of the estimated parameters of MEMS accelerometer calibrated by the six-position parameter correction method,a calibration method which calibrates the drift error of parameters with temperature was proposed.This method combines the sixposition parameter correction method and the 9-parameter least squares method.First,the characteristics of data drift under heating conditions are analyzed,and an error compensation method for the static data drift curve is derived.Then,the proposed method is applied to remove the parameter drift trend,and the accurate parameter drift curve with temperature is obtained.The experimental results show that when the temperature rises from 12℃ to 47℃,after the conversion of the calibrated temperature drift parameters,the drift of the X-axis accelerometer changes from-29.2 mg to 0.5 mg.The drift of the Y-axis accelerometer changes from 18.3 mg to-1.9 mg,and the drift of the Z-axis accelerometer changes from28.5 mg to-1.1 mg.(2)Aiming at the drift of gyro calibration parameters with temperature,a segmented system-level calibration method based on 24 states is proposed under the condition of heating experiment.After analyzing the characteristics of gyro parameters,a system-level calibration method based on error equations,which is suitable for low-cost calibration targets,is determined.Then,the system equation with 24 states is deduced,and a segmented systemlevel calibration method under heating conditions is proposed.The calibration method includes two steps.The first step is to obtain the drift trend of gyro parameters against temperature through a system-level calibration method under data segmentation.The second step is to remove the bias error in the parameter drift trend through accurate parameters at constant temperature,so as to obtain a more accurate temperature drift curve of the gyro parameters.In the 100 s pure inertial navigation experiment,the east positioning error changed from-230.8 meters to-89.7 meters.The north positioning error changed from 371.2 meters to 203 meters,and the altitude error changed from-9 meters to-5.3 meters,which proves the effectiveness of the proposed method.(3)For the change of GNSS positioning error characteristics,a robust filtering method based on standard deviation estimation of GNSS positioning error is proposed.Firstly,the characteristics of GNSS positioning error under static and moving states of the carrier are analyzed,and a GNSS positioning error model is established.Based on this error model,a robust filtering method based on standard deviation estimation of GNSS positioning error is proposed.The method firstly constructs the position difference sequence,and then applies the designed weighting function to obtain the weighting factor on the basis of the sequence,and then obtains the estimated value of the standard deviation of the GNSS positioning error.Based on the estimated value of standard deviation,positioning bias error detection and robust filtering are performed.Simulation and experiments show that when there is an offset error of 30 secs and 12 meters in the GNSS positioning information in the forward direction,the positioning error of the proposed method can be reduced to 6 meters,which is better than the speed constraint KF(Kalman Filter)and SH-KF,which have 15 meters positioning error.(4)In order to further improve the positioning accuracy of the integrated navigation system when GNSS is outage,the error prediction method based on ANFIS is studied and analyzed,and the MIMO and MISO error prediction methods with b-system position error as the model output are proposed.The target output of the proposed model is different from the target output of the current prediction model.Theoretically,the prediction error is not affected by the trajectory of the vehicle.In addition,ANFIS consequent parameter estimation method based on least square method and antecedent parameter estimation method based on genetic algorithm are proposed.Simulation experiments show that when the vehicle travels approximately in a straight line and the GNSS outage time lasts 60 secs,MIMO-ANFIS and MISO-ANFIS can reduce the 12 meters positioning error of the speed-constrained KF to 5 meters.