ADAPTIVE NON-LINE-OF-SIGHT IDENTIFICATION SYSTEM BASED ON BEST FEATURE SUBSET
Identification has always been the focus of research in the field of security,and its research in non-line-of-sight scenarios is of great significance.Aimed at comfort and privacy of recognition,a best feature subset based adaptive non-line-of-sight identification system is proposed.Low-dimensional useful data of Wi-Fi signals was obtained by effectively combining multiple preprocessing methods.A robust human detection method was proposed to intercept effective fragments.A supervised feature extraction method was designed,and"forward search"was employed to obtain the best feature subset.A traditional Adaboost algorithm was improved to realize adaptive recognition under group variation.Experimental evaluation shows that when the number of volunteers in system is 2～12,which has better performance compared with related systems and traditional classification algorithms.
IdentificationNon-line-of-sightWi-Fi signalsThe best feature subsetAdaboost algorithm
万方数据
维普
