Analysis of Classification Methods for Wi-Fi Signals Based on the Use of Channel State Information Spatial Features and CNN

Author(s):  
Maksim A. Lopatin ◽  
Stanislav A. Fyodorov ◽  
Ge Dong
Author(s):  
George Medina ◽  
Akashdeep Singh Jida ◽  
Sravan Pulipali ◽  
Rohith Talwar ◽  
Nancy Amala J ◽  
...  

2021 ◽  
pp. 1-1
Author(s):  
Xiangyu Liu ◽  
Yong Wang ◽  
Mu Zhou ◽  
Wei Nie ◽  
Xiaolong Yang

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 222
Author(s):  
Tao Li ◽  
Chenqi Shi ◽  
Peihao Li ◽  
Pengpeng Chen

In this paper, we propose a novel gesture recognition system based on a smartphone. Due to the limitation of Channel State Information (CSI) extraction equipment, existing WiFi-based gesture recognition is limited to the microcomputer terminal equipped with Intel 5300 or Atheros 9580 network cards. Therefore, accurate gesture recognition can only be performed in an area relatively fixed to the transceiver link. The new gesture recognition system proposed by us breaks this limitation. First, we use nexmon firmware to obtain 256 CSI subcarriers from the bottom layer of the smartphone in IEEE 802.11ac mode on 80 MHz bandwidth to realize the gesture recognition system’s mobility. Second, we adopt the cross-correlation method to integrate the extracted CSI features in the time and frequency domain to reduce the influence of changes in the smartphone location. Third, we use a new improved DTW algorithm to classify and recognize gestures. We implemented vast experiments to verify the system’s recognition accuracy at different distances in different directions and environments. The results show that the system can effectively improve the recognition accuracy.


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