A Fast Imbalanced Binary Classification Approach to NLOS Identification in UWB Positioning

Non-line-of-sight (NLOS) propagation is an important factor affecting the positioning accuracy of ultra-wide band (UWB). In order to mitigate the NLOS ranging error caused by various obstacles in UWB ranging process, some scholars have applied machine learning methods such as support vector machine and support vector data description to the identification NLOS signals for mitigation NLOS error in recent years. Therefore, the identification of NLOS signals is of great significance in UWB positioning. The traditional machine learning method is based on the assumption that the number of samples of the line-of-sight (LOS) and NLOS signals are balanced. However, in reality, the number of LOS signals in UWB positioning is much larger than the NLOS signals. So the samples are characterized by class-imbalance. In response to this fact, we applied a fast imbalanced binary classification method based on moments (MIBC) to identify NLOS signals. The method uses the mean and covariance of the two first moments of the LOS signal samples to represent its probability distribution and then uses the probability distribution and all a small amount of NLOS signal samples to establish a model. This method does not depend on the number of LOS signals and is suitable for dealing with the problem of classification of the imbalance between the number of LOS and NLOS signals. Numerical simulations also verify that the method has better performance than LS-SVM and SVDD.