EE229-Project-Wireless-signal-classification

Introduction

This project shows the complete process from signal cutting to classification. Deep learning method(faster rcnn, yolov4) also supported.

File structure (traditional method)

• stft_with_color.m : generate the spectrogram
• recombination.py: adjusting the spectrogram
• process.py: run segmentation and get the signal pieces
• wash.m: zero-padding
• hog_kmeans.m: K-Means clustering

Wireless Signal Auto-Classification Dataset(WSAD)

• Number of data labels:

  Class	Wi-Fi	BLE	Others
  Numbers	4018	1531	1715

• Scale: 2k images

• Some models' performance on this dataset:

  Model	mAP	Inference Time
  Faster RCNN	82.64%	57.7ms/img
  YOLOv4	80.28%	5.6ms/img

You can download the whole dataset at https://jbox.sjtu.edu.cn/l/oFjsbK.

Reference

The main code of deep learning method references the Detectron2 platform and Darknet.