Indoor Unmanned Vehicle Navigation Based On Inertial Navigation And UWB Positioning

With the development of science and technology,unmanned systems have entered more and more fileds,such as self-driving cars,unmanned delivery cars,unmanned warehouse management systems,etc.Accurate positioning and navigation is very important in unmanned system.The growing of the Global Navigation Satellite System(GNSS)can provide accurate navigation services in outdoor situations,but it is difficult to work at indoors situations because Satellite signals are difficult to obtain.With the increasing demand for indoor positioning and navigation,many indoor positioning schemes have emerged.However,a single positioning technology is difficult to meet the accuracy requirements in practical applications.This paper proposes an indoor unmanned vehicle navigation technology based on inertial navigation and Ultra Wide Band(UWB)positioning.This paper takes unmanned vehicle as the research object,mainly studies the navigation and positioning algorithm in indoor environment.Four UWB location information acquisition methods based on arrival signal strength,arrival Angle,arrival time and arrival time difference are analyzed.Single Sided Two-Way Ranging algorithm and Double Sided Two-Way Ranging algorithm are discussed.And the range error is calibrated by using Double Sided Two-Way Ranging algorithm.The source of UWB positioning error is analyzed,and the methods to suppress antenna error,non-line-ofsight error,clock error and multipath effect are proposed.In order to improve the positioning accuracy of UWB,this paper proposes a UWB positioning algorithm based on Kalman Filter.Compared with the Least Square positioning algorithm,the positioning effect has been significantly improved.In order to solve the problem that the UWB ranging information will appear outliers in the non-line-of-sight environment,which will affect the positioning accuracy,this paper also studies the combined location of UWB/IMU based on the Extended Kalman Filter.In order to solve the problem that the abrupt change of process noise covariance matrix affects the location estimation of the Extended Kalman Filter,a UWB/IMU combined location algorithm based on simplified Sage-Husa Adaptive Kalman Filter is proposed in this paper.Finally,the combined positioning algorithm is simulated,and the UWB/IMU combined positioning system established in this paper is tested on the built test platform.The results of the physical test show that the indoor unmanned vehicle navigation algorithm based on UWB/IMU proposed in this paper effectively improves the positioning accuracy.