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An improved sensing method using radio frequency detection

机译：一种改进的利用射频检测的传感方法

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Easy-to-pilot unmanned aerial vehicles (UAVs) are now readily available off the shelf. This has created the problem of micro unmanned aerial vehicle (MUAV) in private or sensitive areas which can represent a personal or public threat. An improved MUAV detection method is proposed in this paper using artificial neural network (ANN) based feature extraction and reconstruction. The clutters form staticon-static objects in the collected radio frequency (RF) signals are suppressed via employing the background estimate method and suppressing the linear trend. Then principal component of the RF signals are extracted based on the singular value decomposition (SVD) method. The higher order cumulant (HOC) algorithm is utilized to improve the signal to noise ratio (SNR) of the RF signals, which can make Gaussian noise prone to zero. Hilbert spectrums of the analyzed features are considered to determine if one MUAV is present in the detection area using ANN. Finally, the region of interest (ROI) containing RF signals is defined to estimate the azimuth and first frequency of MUAV. Detection results in real-life scenarios are obtained which show the effectiveness of the proposed technique in detecting MUAV. (C) 2019 Elsevier B.V. All rights reserved.
机译：易于驾驶的无人飞行器（UAV）现在就可以买到了。这在私人或敏感区域中造成了微型无人机（MUAV）的问题，可能代表个人或公共威胁。提出了一种基于人工神经网络（ANN）的特征提取与重构的改进的MUAV检测方法。通过使用背景估计方法并抑制线性趋势，可以抑制在收集的射频（RF）信号中形成静态/非静态对象的杂波。然后，基于奇异值分解（SVD）方法提取RF信号的主成分。利用高阶累积量（HOC）算法来改善RF信号的信噪比（SNR），这会使高斯噪声趋于零。考虑到分析特征的希尔伯特光谱，使用ANN确定在检测区域中是否存在一个MUAV。最后，定义包含RF信号的感兴趣区域（ROI）以估计MUAV的方位角和第一频率。获得了真实场景中的检测结果，表明了该技术在检测MUAV中的有效性。 （C）2019 Elsevier B.V.保留所有权利。

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