UWB

Ultra-wideband (UWB) has attracted much attention in indoor positioning due to its high accuracy, low power consumption, and excellent anti-jamming capability. However, due to the complexity of the usage environment, UWB signals are often obstructed by objects such as metals and walls, leading to non-line-of-sight (NLOS) conditions and a decrease in positioning accuracy. To fully consider the effect of ranging errors caused by different occlusions on UWB signals, firstly, this paper analyzes the attenuation characteristics of UWB in different NLOS scenarios. By combining the channel impulse correspondence (CIR) and Markov Transition Field (MTF), an efficient classification method for NLOS identification is proposed. Further, for the signal attenuation characteristics in different scenarios, this paper proposes a polynomial fitting based on the Negative Log-Likelihood function (NLLPF), which greatly mitigates the ranging errors caused by different occlusions. Additionally, the dynamic scene adaptation of UWB localization is enhanced by fusing the Inertial Measurement Unit (IMU) using a confidence-considering EKF (CEKF). Finally, experimental results show that in indoor scenarios, the proposed method achieves a mean absolute error (MAE) of 5.60 cm, while in corridor scenarios, the MAE is 16.36 cm, significantly lower than the 12.42 cm and 54.88 cm of the original methods.