Research On Nonlinear Filtering Algorithm Based On UWB And MEMS-IMU Integrated Positioning

Indoor mobile robots provide great convenience for current production and life.The highquality and high-efficiency completion of tasks of indoor mobile robots relies on stable,reliable and safe positioning algorithms.The integrated navigation system based on ultra-wideband and micro-inertial measurement unit provides a solution for the autonomous positioning and navigation of indoor mobile robots.However,for the changeable indoor environment,there are uncertainties in the positioning process of indoor mobile robots,and their system models often have the characteristics of nonlinearity and uncertainty.In order to improve the positioning accuracy and stability of indoor robots,it is of great significance to study nonlinear filtering algorithms based on integrated positioning of Ultra Wide Band(UWB)and inertial navigation systems.In this research,we have carried out the research on the integrated navigation system of UWB system and Micro-Electro-Mechanical Systems Inertial Measurement Unit(MEMSIMU)to improve the accuracy of integrated navigation algorithm.The research results have improved the accuracy,stability and safety of the positioning system.The main research contents are as follows.1.Introduced the working mechanism of ultra-wideband and inertial measurement elements.The ultra-wide security mechanism was also studied,an authentication-based ultrawideband security protection mechanism was proposed with the combination of actual application scenarios and ultra-wideband information transmission mechanism.The message authentication code and key chain are used to ensure the security of the ultra-wideband network and prevent the location information from being illegally intercepted during transmission.2.According to the characteristics of UWB and MEMS-IMU,an indoor robot positioning system based on the combination of UWB/MEMS-IMU was constructed.The errors of UWB and MEMS-IMU can be mutually suppressed,and the errors in the positioning process are initially reduced.The direct method is used to establish the state equation and measurement equation of the combined positioning system.3.The Kalman series of filter algorithms are studied in detail,including Kalman filter algorithm,extended Kalman filter algorithm and unscented Kalman filter algorithm.Aiming at the problem of poor system robustness of the unscented Kalman filter algorithm,the idea of innovation orthogonality is adopted to modify the algorithm to form an improved unscented Kalman filter algorithm,which enables the noise to be adjusted adaptively according to the system.The experimental results show that the performance of the improved unscented Kalman filter algorithm is better than that of the extended Kalman filter algorithm and the unscented Kalman filter algorithm.4.Aiming at the problem that the single modeling method in traditional integrated navigation cannot accurately estimate the maneuvering carrier,the interactive multi-model algorithm is applied to the UWB/MEMS-IMU integrated positioning system.The noise in the robot maneuvering process is analyzed,and design the IMM model set according to the noise model.The experimental results show that the IMM-AUKF algorithm has higher accuracy and better robustness than the AUKF and IMM-UKF algorithms.The combination of AUKF and IMM algorithm improves the positioning accuracy and stability of the UWB/MEMS-IMU integrated navigation system.