Research on Non-Line-Of-Sight Recognition Method Based on Weighted K-Nearest Neighbor Classification

In the Ultra-WideBand (UWB) positioning system, the signal occlusion and the misjudgment of the direct signal affect seriously the positioning accuracy in complex environment. To solve this problem, Saturation (S) is proposed, which is a new characteristic parameter based on Channel Impulse Response (CIR). In this study, the Relief algorithm and the Mutual Information Feature Selection (MIFS) algorithm are used for feature selection combined with feature parameters proposed by researchers. Based on the correlation of the parameters, the optimal feature subset with corresponding weights is used for weighted K-nearest neighbor classification, which improves the accuracy of the Non-Line-Of-Sight (NLOS) recognition system. The influence of the number of training dataset and the value of K on the Weighted K-Nearest Neighbor (WKNN) algorithm is analyzed. An optimization scheme is proposed to reduce the amount of calculation and improve the real-time performance of the NLOS recognition system. The experimental results in different environments show that the method has high recognition accuracy and wide applicability, and the recognition accuracy reaches 95%.