Indoor Positioning Algorithm For Suppressing NLOS Based On UWB/INS Data Fusion

With the rapid development of wireless technology in recent years,indoor location services(such as supermarket shopping navigation,fire emergency location,inpatient location tracking,etc.)have become the basic needs of many Internet of things applications.The common goal of academia and industry due to the expansion of indoor positioning areas and the large-scale deployment of various base stations is reducing positioning costs and improving positioning accuracy further.However,the traditional single positioning systems cannot meet some high-precision positioning scenarios due to the motion model’s error and the device’s inherent error.The high-precision combined positioning system using the advantages of INS and UWB has become a research trend in indoor positioning.The comprehensive integration of the positioning information of multiple positioning systems to achieve a large-scale,low-cost,and high-precision positioning effect becomes an important research point of indoor positioning.The main work of this paper is as follows.Firstly,a dynamic and robust cubature Kalman filter algorithm based on INS / UWB is proposed in this thesis to address the problem of the measurement noise and the uncertainty of the motion model in the positioning process.The algorithm establishes an error positioning model based on UWB/INS,improves the time prediction process and measurement update process of the cubature Kalman filter algorithm,and achieves highprecision and stable positioning in a complex indoor environment.Secondly,an error suppressing algorithm is proposed to alleviate the large UWB/INS fusion positioning error in a complex indoor environment.The algorithm determines the indoor wireless signal propagation conditions(line-of-sight,non-line-ofsight state)based on the joint state detection method and uses power quality criteria to establish a UWB ranging error model.Then,the algorithm uses particle filtering to determine the error model and integrates the error model into the INS positioning algorithm to alleviate the impact of UWB errors on the fusion positioning system’s accuracy.Besides,the algorithm only relies on the UWB ranging and INS system,reducing the amount of ranging data required for positioning and reducing positioning costs while ensuring system positioning accuracy and stability.Finally,UWB and INS positioning systems’ basic test data is obtained in a complex environment.The data is applied to the simulation and performance evaluation of the proposed algorithm.The proposed UWB-INS fusion indoor pedestrian tracking algorithm performs better robustness and positioning accuracy in complex environment when compared to UWB or INS positioning system.