Target classification using radar cross-section statistics of millimeter-wave scattering

Abstract

Purpose This paper aims to discuss the classification of targets based on their radar cross-section (RCS). The wavelength, the dimensions of the targets and the distance from the antenna are in the order of 1 mm, 1 m and 10 m, respectively. Design/methodology/approach The near-field RCS is considered, and the physical optics approximation is used for its numerical calculation. To model real scenarios, the authors assume that the incident angle is a random variable within a narrow interval, and repeated observations of the RCS are made for its random realizations. Then, the histogram of the RCS is calculated from the samples. The authors use a nearest neighbor rule to classify conducting plates with different shapes based on their RCS histogram. Findings This setup is considered as a simple model of traffic road sign classification by millimeter-wavelength radar. The performance and limitations of the algorithm are demonstrated through a set of representative numerical examples. Originality/value The proposed method extends the existing tools by using near-field RCS histograms as target features to achieve a classification algorithm.

Analysis of data-driven approaches for radar target classification

“…Histogram statistics are becoming a hot topic as they offer a reduction in the computation associated with deep learning algorithms (Shuai et al , 2020). Prior work (Coskun and Bilicz, 2023a) has primarily used the K-nearest neighbour (KNN) algorithm for the classification of targets, and the artificial neural network (ANN) (Coskun and Bilicz, 2023b) has been explored for classifying targets based on the RCS.…”