Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference
Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference
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DOI：
10.1016/j.comcom.2020.02.065
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
Z. Uddin;M. Altaf;M. Bilal;L. Nkenyereye;A. Bashir
中科院分区：
文献类型：
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作者：
Z. Uddin;M. Altaf;M. Bilal;L. Nkenyereye;A. Bashir
由于体积小、传感能力和自主性，无人机（UAV）在各个领域具有巨大的应用，例如，遥感、导航、考古学、新闻学、环境科学和农业。然而，无人机（称为业余无人机（AmDr））的无监控部署可能导致严重的安全威胁，并对人类生命和基础设施构成风险。因此，及时检测AmDr对于保护敏感组织、人类生命和其他重要基础设施的安全至关重要。AmDR可以使用基于声音、视频、热和射频的不同技术来检测。然而，这些技术的性能在恶劣的大气条件下受到限制。在本文中，我们提出了一种有效的独立分量分析（伊卡）的无监督机器学习方法来检测各种声信号，即，鸟的声音，飞机，雷暴，雨，风和无人机在实际情况下。对信号进行解混后，利用独立分量分析（ICA）提取信号的梅尔倒谱系数（MFCC）、功率谱密度（PSD）和均方根值（RMS）等特征。通过倍频程带通滤波器组输出的信号，提取PSD和PSD信号的RMS。基于上述特征，使用支持向量机（SVM）和K最近邻（KNN）对信号进行分类以检测AmDr的存在或不存在。所提出的技术的独特特征是在存在多个声学干扰信号的情况下一次检测单个或多个AmDR。通过大量的仿真验证了所提出的技术，并观察到与KNN PSD的RMS值比与KNN和SVM的MFCC表现更好。
Owing to small size, sensing capabilities and autonomous nature, the Unmanned Air Vehicles (UAVs) have enormous applications in various areas e.g., remote sensing, navigation, archaeology, journalism, environmental science, and agriculture. However, the un-monitored deployment of UAVs called the amateur drones (AmDr) can lead to serious security threats and risk to human life and infrastructure. Therefore, timely detection of the AmDr is essential for the protection and security of sensitive organizations, human life and other vital infrastructure. AmDrs can be detected using different techniques based on sound, video, thermal, and radio frequencies. However, the performance of these techniques is limited in sever atmospheric conditions. In this paper, we propose an efficient un-supervise machine learning approach of independent component analysis (ICA) to detect various acoustic signals i.e., sounds of bird, airplanes, thunderstorm, rain, wind and the UAVs in practical scenario. After unmixing the signals, the features like Mel Frequency Cepstral Coefficients (MFCC), the power spectral density (PSD) and the Root Mean Square Value (RMS) of the PSD are extracted by using ICA. The PSD and the RMS of PSD signals are extracted by first passing the signals from octave band filter banks. Based on the above features the signals are classified using Support Vector Machines (SVM)and K Nearest Neighbour (KNN)to detect the presence or absence of AmDr. Unique feature of the proposed technique is the detection of a single or multiple AmDrs at a time in the presence of multiple acoustic interfering signals. The proposed technique is verified through extensive simulations and it is observed that the RMS values of PSD with KNN performs better than the MFCC with KNN and SVM.