High Precision Indoor Location System Based On UWB And IN Data Fusion

Ultra-wideband positioning system plays an important role in personnel positioning and material positioning.In the process of rescue and disaster relief and personnel training,it is of great significance to be able to obtain the location information of personnel in real time.However,because ultra-wideband positioning uses very short narrow pulses,there is non-line of sight interference in the positioning process,especially in complex environments.At the same time,due to the unreasonable distribution of the anchors,the accuracy of the positioning can be decreased.Compared with using a single sensor for positioning,using of multiple sensors and deploying data fusion technology to increase the positioning accuracy is one of the important research directions of location technology.In order to improve the positioning accuracy and extend the effective positioning range,this thesis studies and discusses the data fusion positioning algorithm of the inertial navigation and ultra-wideband with kalman filter model.The thesis firstly analyzes the error model of inertial navigation unit,then works out the attitude quaternion using the corrected real-time data.The attitude quaternion is used to transform the horizontal acceleration,which is the input control variable in kalman filter.The model of extended kalman filter and unsented kalman filter is established and the system error is considered as the system variable.The UWB positioning data is considered as the observation variable to rectify the estimated system variable.After the theoretical analysis,the extended kalman filter and unsented kalman filter is simulated respectively.Following this,the software and hardware platform is designed for further verifying the correctness of the theory.Besides,the data fusion algorithm calculates the horizontal acceleration by real-time attitude quaternion that ensures the device is not dependent on the specific wear position.This improves the integration of the equipment and simplifies the complexity of the system,thus making the device more portable.Finally,under the guidance of theoretical part and simulation analysis,a static and moving experimental analysis is carried out.The experimental results show that the UWB-IN data fusion algorithm,no matter extended kalman filter or unsented kalman filter,TOA or TDOA,is more accurate than UWB position algorithm.And it greatly reduces the possibility of positioning jitter,which makes the trajectory of the tag more smooth.On the edge of the anchor area,the 80% CEP of TOA kalman data fusion algorithm is reduced from 0.159 m to 0.121 m.In the area away from the anchor,the 80% CEP of data fusion algorithm is reduced from 0.255 m to 0.110 m.On the edge of the anchor area,the 80% CEP of TDOA kalman data fusion algorithm is reduced from 0.132 m to 0.097 m.In the area away from the anchor,the 80% CEP of data fusion algorithm is reduced from 0.422 m to 0.168 m.In addition,the degradation speed of positioning accuracy is far lower than that of using UWB positioning alone,which improves the range of localization.In general,the experiment verifies the validity of data fusion positioning algorithm.