A Hybrid EKF/WUFIR Filter for Indoor Localization Integrating INS and UWB Data

Due to the complex and variable indoor environment, ultra-wideband (UWB) signal transmission is often obstructed by walls and obstacles, resulting in non-line-of-sight (NLOS), which reduces localization accuracy. Inertial navigation system (INS) is an autonomous navigation system that does not rely on external information and is not affected by NLOS. Therefore, a hybrid EKF/WUFIR filter indoor localization algorithm that integrates INS and UWB data is proposed. The proposed algorithm is composed of three parts: INS localization, UWB localization and data fusion. In the INS localization part, the motion model is used to determine the state of the target in real time using measurement data obtained from the inertial measurement unit (IMU). In the UWB localization part, a resettable residual weighted particle filter algorithm is proposed to mitigate the effect of NLOS on the localization results. In the data fusion part, a hybrid filtering algorithm combining extended Kalman filter (EKF) and weighted unbiased finite impulse response (WUFIR) filtering is proposed to fuse the INS and UWB localization data. Simulation and experimental results show that the proposed algorithm outperforms other comparative algorithms in terms of robustness and localization accuracy.