Unmanned Aerial Vehicle Detection and Recognition Method Based on Multi-dimensional Signal Feature
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摘要: 如今，无人机(UAVs)在军用民用领域得到大规模应用，在无人机带来便利的同时也带来了巨大的安全隐患。针对无人机的探测识别技术逐渐成为研究热点，传统的无人机探测方法主要是通过获取雷达回波信号、无人机声音信号和光电信号的方式对无人机进行探测。然而，这类方法往往容易受到环境影响具有一定的局限性，无法对无人机进行精确的定位和识别。该文提出一种基于多维信号特征的无人机识别方法，该方法首先通过自适应三角阈值法从接收到的无线信号中探测并筛选出无人机信号，同时解析获取的无线信号的信道状态信息(CSI)。然后，利用正交匹配追踪算法(OMP)进行参数估计来获取无人机的位置信息对无人机进行定位。最后，提取无人机信号中的盒维数和径向积分双谱(RIB)来对无人机进行分类识别。通过实验，该方法对无人机的3维定位精度小于1 m，对无人机的分类识别精度最高能达到100%。Abstract: Nowadays, Unmanned Aerial Vehicles (UAVs) are widely used in military and civilian fields. While UAVs bring convenience, they also bring huge security risks. The detection and identification technology for UAVs has gradually become a research hotspot. The traditional UAV detection method mainly detects UAVs by obtaining radar echoes, UAV sound signals and photoelectric signals. However, such methods are often susceptible to environmental influences and have certain limitations, and cannot accurately locate and identify UAVs. A UAV identification method based on multi-dimensional signal features is proposed in this paper. Firstly, UAV signals from the received wireless signals through the adaptive triangular threshold method are detected and screened, and at the same time the Channel Status Information (CSI) of the acquired wireless signals is analyzed. Then, the Orthogonal Matching Pursuit (OMP) algorithm is used for parameter estimation to obtain the position information of the UAV to locate the UAV. Finally, the box dimension and Radial Integral Bispectrum (RIB) in UAV signals are extracted to classify and identify UAVs. Through experiments, the method's three-dimensional positioning accuracy for UAVs is less than 1 m, and the classification and recognition accuracy for UAVs can reach up to 100%.
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