在这项工作中，我们通过使用机器学习算法和预先训练的基于卷积神经网络的算法SqueezeNet作为分类器，对无线干扰（如WiFi和蓝牙）下基于射频（RF）的无人机检测和识别系统（DDI）进行了全面的比较分析。在射频信号指纹识别研究中，信号的瞬态和稳态可以用来从射频信号中提取唯一的签名。通过利用无人机（UAV）的射频控制信号进行DDI，我们分别考虑了信号的每个状态进行特征提取，并比较了无人机检测和识别的利弊。使用各种小波变换（离散小波变换、连续小波变换和小波散射变换）从信号中提取特征，我们使用这些特征建立不同的模型。我们研究了这些模型在不同信噪比（SNR）水平下的性能。通过使用小波散射变换从30 dB SNR下的RF信号的稳态中提取特征（散点图），并使用这些散点图来训练SqueezeNet，我们在10 dB SNR下实现了98.9%的准确度。
英文摘要：
In this work, we performed a thorough comparative analysis on a radio frequency (RF) based drone detection and identification system (DDI) under wireless interference, such as WiFi and Bluetooth, by using machine learning algorithms, and a pre-trained convolutional neural network-based algorithm called SqueezeNet, as classifiers. In RF signal fingerprinting research, the transient and steady state of the signals can be used to extract a unique signature from an RF signal. By exploiting the RF control signals from unmanned aerial vehicles (UAVs) for DDI, we considered each state of the signals separately for feature extraction and compared the pros and cons for drone detection and identification. Using various categories of wavelet transforms (discrete wavelet transform, continuous wavelet transform, and wavelet scattering transform) for extracting features from the signals, we built different models using these features. We studied the performance of these models under different signal to noise ratio (SNR) levels. By using the wavelet scattering transform to extract signatures (scattergrams) from the steady state of the RF signals at 30 dB SNR, and using these scattergrams to train SqueezeNet, we achieved an accuracy of 98.9% at 10 dB SNR.
Sensing-Assisted LoS/NLoS Identification in Dynamic UAV Positioning Systems
Shandong University（山东大学）；Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR)（山东大学-南洋理工大学联合人工智能研究中心）；School of Software, Shandong University（山东大学软件学院）；Peking University（北京大学）；State Key Laboratory of Photonics and Communications, School of Electronics, Peking University（北京大学电子工程学院光通信与光子学国家重点实验室）；Donghua University（东华大学）；School of Information and Intelligence Science, Donghua University（东华大学信息智能科学学部）
Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-heuristically Optimized Non-local Means Filter
Functional MRI Time Series Generation via Wavelet-Based Image Transform and Spectral Flow Matching for Brain Disorder Identification
School of Information Technology, Monash University Malaysia（墨尔本大学马来西亚分校信息科技学院）；School of Engineering, Monash University Malaysia（墨尔本大学马来西亚分校工程学院）；Statistics Program, King Abdullah University of Science and Technology（国王阿卜杜勒·阿齐兹大学科学与技术学院统计学项目）
