Non-line-of-sight ultra-wideband signal recognition using DWT-CNN

With the development of technology, the application of positioning techniques has become increasingly widespread. However, in indoor or specific situations, due to the complexity of the environment, it can be challenging to apply outdoor positioning techniques. Therefore, research on indoor positioning techniques is highly necessary. Ultra-Wideband (UWB) technology, as an indoor positioning method, can address many indoor positioning scenarios. However, when facing obstacles, it can still lead to non-line-of-sight (NLOS) situations, resulting in less accurate positioning. To address this issue, this paper proposes an approach that combines Discrete Wavelet Transform (DWT) and Convolutional Neural Networks (CNN). Firstly, it extracts coefficients from Channel Impulse Response (CIR) signals using DWT. Subsequently, these coefficients are input to a CNN to explore deep features, ultimately distinguishing between Non-Line-of-Sight (NLOS) and Line-of-Sight (LOS) signals based on these features. Experimental results demonstrate that this method achieves better classification accuracy compared to other approaches.