Abstract
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.
Highlights
ultra-wide band (UWB) technology has the characteristics of extremely narrow pulse and extremely wide bandwidth, which satisfies the requirements of positioning accuracy in indoor complex environment
Since the number of LOS signal samples N is much larger than the number of NLOS signals n, the momentbased imbalanced binary classification (MIBC) method proposed by us has lower complexity than the support vector data description (SVDD) algorithm
UWB positioning system faces the problem of serious positioning error caused by NLOS signals in practical application
Summary
UWB technology has the characteristics of extremely narrow pulse and extremely wide bandwidth, which satisfies the requirements of positioning accuracy in indoor complex environment. The traditional identification research on NLOS focuses on analyzing the statistical characteristics of the signals in UWB positioning. In the case where both LOS and NLOS exist, the nonparametric support vector machine is used for NLOS identification This approach to identify NLOS can be realized without statistical characteristics of waveforms. Our goal is to identify the small amount of NLOS signals which are very important for localization in NLOS environment This is a typical class-imbalance classification problem. Traditional machine learning methods such as LS-SVM will not effectively solve the problem of class-imbalance classification. The computational complexity of the algorithm is still high this method saves the time to train NLOS signals samples compared to LS-SVM.
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