Design And Implementation Of Loosely-coupled Integrated Navigation Simulation System

In the recent years,the availability of accurate vehicle position becomes more urgent.The GNSS/INS is the most used integrated navigation scheme for land vehicles,which utilizes the Kalman filter to optimally fuse GNSS measurement and INS prediction for accurate and robust localization.Traditional Kalman filter based integrated navigation methods configure the process noise covariance and measurement noise covariance with pre-defined constants,which cannot adaptively characterize the various and dynamic environments and obtain accurate and continuous positioning results under complex environments.To obtain accurate and robust localization results under various complex and dynamic environments,we propose a novel noise covariance estimation algorithm for the GNSS/INS integrated navigation using multi-task learning model,which can simultaneously estimate the process noise covariance and measurement noise covariance for the Kalman filter.The predicted multiplication factors are used to dynamically scale process noise covariance matrix and measurement noise covariance matrix respectively according to the inputs of raw inertial measurement.Firstly,this system introduces the research background and significance of integrated navigation and positioning algorithm,and conducts research and study on related theories and technologies.Then,the requirements of the system are analyzed,and the simulation system is needed to verify the algorithm performance and simplify the algorithm flow in the current navigation algorithm research process.On the basis of the requirements analysis of the overall outline of the system design and coding.The last functional is testing and analysis of the system.Extensive experiments are conducted on our collected practical road dataset under three typical complex urban scenarios,i.e.,avenues,viaducts,tunnels.Experimental results demonstrate that compared with the traditional Kalman filter based integrated navigation algorithm with pre-defined fixed settings,our proposed method reduces 77.13%positioning error.The construction and testing of the loose-coupling integrated navigation simulation system are successfully completed.By comparing with the existing integrated navigation algorithm methods,the proposed method can achieve better positioning accuracy,which verifies the effectiveness of the proposed method.