Research On UWB/MEMS Tightly Integrated Positioning Algorithms

With the development of Internet of Things technology,the navigation and positioning system of Location-based services(LBS)plays an increasingly important role in people's lives.Ultra-wide band(UWB)has the advantages of extremely narrow pulse signal,ultra-high time resolution and strong anti-interference ability,and is widely used in indoor positioning field.However,UWB is vulnerable to Non-line-of-sight(NLOS)environment,and the positioning service technology based on UWB still has the problem of poor accuracy and adaptability.Therefore,how to improve the accuracy and robustness of UWB positioning system has become the main research content of this paper.Firstly,the common coordinate system,the structure and working principle of MEMS inertial navigation system and UWB system are introduced,and the error equation of MEMS inertial navigation system is analyzed and deduced.The advantages and disadvantages of MEMS inertial navigation and UWB positioning system are expounded,and the working principle of UWB/MEMS tight integrated positioning system is introduced in detail.Secondly,the common information fusion filtering algorithms are introduced,and the advantages and disadvantages of each filtering algorithm are analyzed and summarized.In view of the low positioning accuracy and even divergence of traditional UWB system due to environmental interference,a mathematical model of UWB/MEMS tight combination is established by combining the positioning principles and error equations of UWB and MEMS inertial navigation,and the effectiveness of UWB/MEMS tight combination algorithm is verified by simulation and sports car experiments.Then,aiming at the problem that traditional UWB and UWB/MEMS integrated positioning algorithms need high-precision base station location information,the influence of base station location error on positioning accuracy of integrated system is analyzed through theory and simulation,and a base station location error compensation algorithm based on UWB/MEMS tight combination is proposed.The algorithm introduces the base station position error into the system state equation,and estimates and compensates the base station position error,thus reducing the influence of the base station position error on the positioning accuracy of the combined system and improving the positioning accuracy and practicability of the system.At the same time,according to the characteristics of the established compensation algorithm model,DUKF algorithm is used as the system information fusion filter,which reduces the calculation amount of the system filtering algorithm.The effectiveness of the base station position error compensation algorithm is verified by simulation and sports car experiment.Finally,aiming at the problem that UWB is easily affected by environmental interference,the components of UWB ranging error in NLOS environment are analyzed through measured data,and the influence of noise and outliers on positioning accuracy of integrated system is analyzed through simulation,and an improved robust adaptive filtering algorithm is proposed.The algorithm introduces the measurement error detection criterion,and adjusts the filter gain of each measurement channel in real time according to different measurement errors,so that the filter has stronger self-adaptability.Aiming at several common situations in which the measurement of the combined system is interfered,an anti-interference scheme of UWB/MEMS combined system is proposed,and the effectiveness of the algorithm is verified by simulation and sports car experiments.