Analysis of Imageless Ground Scene Classification Using a Millimeter-Wave Dynamic Antenna Array
Analysis of Imageless Ground Scene Classification Using a Millimeter-Wave Dynamic Antenna Array
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DOI：
10.1109/tgrs.2022.3169385
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发表时间：
2022
影响因子：
8.2
通讯作者：
Daniel Chen;J. Nanzer
中科院分区：
文献类型：
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作者：
Daniel Chen;J. Nanzer
我们提出了一个分析的能力，无图像的地面场景分类使用的傅立叶域的信息与旋转动态毫米波天线阵列获得的一个子集。该概念基于对场景中的人造物体产生的信号伪影的检测，其表现在傅立叶或空间频率域中。诸如建筑物和道路之类的人造结构通常以尖锐边缘为特征，尖锐边缘生成被限制在窄角度范围内但在宽空间频率带宽上延伸的空间频率响应。这些伪影可以通过在傅立叶域中生成环形滤波器来检测，该环形滤波器可以通过具有旋转动力学的线性天线阵列的新颖设计来获得。我们讨论的毫米波线性动态阵列的设计产生环形滤波器和分析这样的阵列的能力，从那些没有安装在空中平台，如无人机上时，包含人工结构的地面场景进行分类。我们比较环形滤波器的设计和探索使用的启发式分类器和K-最近邻（K-NN）分类器的微波地面场景从数据库中获得的大数据集。使用可以用两元件天线阵列实现的单个环形滤波器，观察到0.6%-3.2%的小分类误差。在由四个元素组成的线性阵列中实现多个滤波器将误差降低到0.3%。
We present an analysis of the capability for imageless ground scene classification using a subset of the Fourier domain information obtained with a rotationally dynamic millimeter-wave antenna array. The concept is based on the detection of signal artifacts generated by artificial objects in a scene, which manifests in the Fourier, or spatial frequency, domain. Man-made, artificial structures, such as buildings and roads, are generally characterized by sharp edges, which generate spatial frequency responses that are confined to a narrow angular range but extend over a broad spatial frequency bandwidth. These artifacts can be detected by generating a ring-shaped filter in the Fourier domain, which can be obtained through the novel design of a linear antenna array with rotational dynamics. We discuss the design of a millimeter-wave linear dynamic array for generating ring-filters and analyze the ability of such an array to classify ground scenes containing artificial structures from those without when mounted on an aerial platform, such as a drone. We compare ring filter designs and explore the use of a heuristic classifier and the K-nearest neighbor (K-NN) classifier on a large dataset of microwave ground scenes obtained from a database. Using a single ring filter that can be implemented with a two-element antenna array, small classification errors of 0.6%–3.2% were observed. Implementing multiple filters in a linear array consisting of four elements reduced the error to 0.3%.