An INS/UWB joint indoor positioning algorithm based on hypothesis testing and yaw angle

Wireless sensor network (WSN) is widely used in indoor positioning, but indoor positioning is susceptible to non-line-of-sight (NLOS) propagation environment. The inertial navigation system (INS) does not depend on external information, but it will produce a large cumulative error when working for a long time. The combination of Ultra-wide band (UWB) positioning and inertial navigation positioning can not only effectively reduce the impact of NLOS interference, but also alleviate the impact of INS cumulative error. This paper proposes an algorithm based on yaw angle and UWB joint positioning. In order to weaken the cumulative error of the INS itself, this paper uses the UWB positioning results to correct the INS positioning data and yaw angle data through the extended Kalman filter (EKF), and then performs subsequent positioning according to the modified yaw angle until the next data correction. In addition, this algorithm uses a hypothesis test method for INS and UWB data processing, which weakens the error impact of environmental factors. The proposed algorithm is compared with existing algorithms using mean square error (RMSE) as an indicator. The simulation and experimental results show that the algorithm has better performance in NLOS interference environment.