基于ISODATA改进K均值聚类算法的NLOS识别技术

针对超宽带信号中非视距误差造成定位系统定位偏差的问题，提出了一种改进无监督算法的NLOS识别技术。本文提取信道脉冲冲激响应波形的8种特征参量，选择主成分分析算法对多维特征进行降维处理；采用基于迭代自组织数据分析法改进的K均值聚类算法，自适应地选择K值来区分视距和非视距信号；最后，结合特征参量的冗余性、相关性对分类结果进行判别。实验结果表明，该方法能有效地识别出NLOS信号，且具有较好的环境适应性，识别准确度达到95%。

NLOS identification technique based on K-means clustering algorithm improved by ISODATA

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%.