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 unmonitored 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 unsupervise 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 Neighbor (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.

使用无人机侦测领域中的独立成分分析（ICA）机器学习方法可以有效地探测各种声音信号，包括来自鸟类、飞机、雷暴、雨、风和无人机等，并通过功率谱密度（PSD）和 PSD 的 RMS 值检测其是否为业余无人机，是一种在多声源干扰下探测一个或多个业余无人机的独特技术。

业余航拍器检测：利用机器学习方法在强干扰环境下的声音信号

Amateur Drones Detection: A machine learning approach utilizing the  acoustic signals in the presence of strong interference

The use of amateur drones (ADrs) is expected to significantly increase over
the upcoming years. However, regulations do not allow such drones to fly over
all areas, in addition to typical altitude limitations. As a result, there is
an urgent need for ADrs surveillance solutions. These solutions should include
means of accurate detection, classification, and localization of the unwanted
drones in a no-fly zone. In this paper, we give an overview of promising
techniques for modulation classification and signal strength based localization
of ADrs by using surveillance drones (SDrs). By introducing a generic altitude
dependent propagation model, we show how detection and localization performance
depend on the altitude of SDrs. Particularly, our simulation results show a 25
dB reduction in the minimum detectable power or 10 times coverage enhancement
of an SDr by flying at the optimum altitude. Moreover, for a target no-fly
zone, the location estimation error of an ADr can be remarkably reduced by
optimizing the positions of the SDrs. Finally, we conclude the paper with a
general discussion about the future work and possible challenges of the aerial
surveillance systems.

本研究旨在提出一种使用监控无人机的调制分类和信号强度定位方法，以精确监测和定位非法飞行的业余爱好者随身携带的无人机，通过在合适高度飞行，调整监控无人机的位置等手段，提高监测和定位性能。