To mitigate the issue of positioning deviations in positioning systems caused by non-line of sight (NLOS) errors in Ultra-Wide Ban signals, this study presents an unsupervised clustering method that utilizes the characteristic parameters of the channel impulse response for identifying NLOS signals. The method involves the extraction of eight characteristic parameters from the channel impulse response waveform, followed by the use of the principal component analysis algorithm to reduce the dimension of the multi-dimensional features. An improved K-means clustering algorithm, based on iterative self-organizing data analysis, is then used to select K-values adaptively for distinguishing between LOS and NLOS signals. Finally, the redundancy and correlation of feature parameters are combined to distinguish the classification results. The experimental results demonstrate that this approach effectively identifies NLOS signals with better environmental adaptability and has a recognition accuracy of 95%.