Abstract
UWB is especially suitable for indoor positioning because of its strong anti-interference ability, it can achieve centimeter-level positioning accuracy in the line of sight (LOS)environment, but it is challenging to construct a robust and high-precision algorithm in complex and changeable environments. Therefore, we establish a UWB positioning model based on time difference of arrival (TDOA), discuss the iterative principle of standard Kalman filter (SKF) and two improved Kalman filter methods, based on dropped measurements method (SKF-DMM) and global offset method (SKF-GOM) respectively. Simulation results show that: in the NLOS environments, the positioning performance of SKFDMM algorithm is obviously superior to that of SKF and SKFGOM, which can improve the robustness and adaptability of UWB indoor positioning system to the complex environments.
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