A new approach for classifying targets based on their radar cross section (RCS) is discussed. The RCS presents unique statistical features depending on the target's shape, while an incident angle with small random fluctuation is considered. Data sets are generated utilizing Physical Optics simulation of the RCS, and the classification of targets with different shapes is performed by Artificial Neural Network (ANN). The algorithm's performance is evaluated, especially regarding the robustness against noise on the RCS data, as in practice measurement data are corrupted by noise. Numerical examples motivated by mm-wave radar applications in driving assistance systems are presented. The results show that the classification algorithm performs promising results and ensures the robustness of the features extracted from histogram definitions of RCS.

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.