Abstract: Aiming at the issues of accuracy and stability being easily affected by NLOS errors in the UWB positioning process, and existing methods of NLOS recognition and removal having complex models and strong environmental dependence, This paper proposes a method for recognizing and removing errors, which combines the a channel characteristic binary hypothesis with an improved UWB autonomous integrity monitoring based on sliding windows. First, this method focuses on identifying and removing significant errors, including gross errors and partial NLOS errors, leveraging the binary assumption of channel characteristics. By doing so, it enhances data quality and reduces the constraints associated with the single ranging fault assumption in UWB localization autonomous integrity monitoring. However, it still faces the challenge of insensitive identification for small-scale NLOS errors. Therefore, by combining the improved UWB localization autonomous integrity monitoring method, which can efficiently identify and remove NLOS ranging values that may not be recognized by the binary assumption method of channel characteristics. Distance measurement accuracy was introduced in the process of constructing inspection factors in autonomous integrity monitoring methods in order to improve accuracy of global test and fault positioning. And the method combines sliding window theory to reduce the missed detection of NLOS, achieving further identification and elimination of NLOS. Experimental results show that the proposed method in this paper is capable of effectively identifying and discarding outlier range measurements containing errors such as NLOS, thereby improving the accuracy and reliability of UWB positioning.

Key words: UWB, NLOS, SNR, improved autonomous integrity monitoring