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Purpose: This study focuses on the classification of targets with varying shapes using radar cross section (RCS), which is influenced by the target's shape. This study aims to develop a robust classification method by considering an incident angle with minor random fluctuations and using a physical optics simulation to generate data sets. Design/methodology/approach: The approach involves several supervised machine learning and classification methods, including traditional algorithms and a deep neural network classifier. It uses histogram-based definitions of the RCS for feature extraction, with an emphasis on resilience against noise in the RCS data. Data enrichment techniques are incorporated, including the use of noise-impacted histogram data sets. Findings: The classification algorithms are extensively evaluated, highlighting their efficacy in feature extraction from RCS histograms. Among the studied algorithms, the K-nearest neighbour is found to be the most accurate of the traditional methods, but it is surpassed in accuracy by a deep learning network classifier. The results demonstrate the robustness of the feature extraction from the RCS histograms, motivated by mm-wave radar applications. Originality/value: This study presents a novel approach to target classification that extends beyond traditional methods by integrating deep neural networks and focusing on histogram-based methodologies. It also incorporates data enrichment techniques to enhance the analysis, providing a comprehensive perspective for target detection using RCS. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is the development of an analytic computational model for electromagnetic (EM) wave scattering from spherical objects. The main application field is the modeling of electrically large objects, where the standard numerical techniques require huge computational resources. An example is full-wave modeling of the human head in the millimeter-wave regime. Hence, an approximate model or analytical approach is used. Design/methodology/approach: The Mie–Debye theorem is used for calculating the EM scattering from a layered dielectric sphere. The evaluation of the analytical expressions involved in the infinite sum has several numerical instabilities, which makes the precise calculation a challenge. The model is validated through an application example with comparing results to numerical calculations (finite element method). The human head model is used with the approximation of a two-layer sphere, where the brain tissues and the cranial bones are represented by homogeneous materials. Findings: A significant improvement is introduced for the stable calculation of the Mie coefficients of a core–shell stratified sphere illuminated by a linearly polarized EM plane wave. Using this technique, a semi-analytical expression is derived for the power loss in the sphere resulting in quick and accurate calculations. Originality/value: Two methods are introduced in this work with the main objective of estimating the final precision of the results. This is an important aspect for potentially unstable calculations, and the existing implementations have not included this feature so far. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is performing a global sensitivity analysis for automotive electromagnetic compatibility (EMC) measurements related to the CISPR 25 setup in order to examine the effect of the setup uncertainties on the resonance phenomenon. Design/methodology/approach: An integral equation formulation is combined with Darwin model and special Green's functions to model the configuration. The method of Sobol' indices is used to gain sensitivity factors enhanced with a polynomial chaos metamodel. Findings: The proposed model resulted in by orders of magnitude lower number of degrees of freedom and runtime compared to popular numerical methods, e.g. finite element method. The result of the sensitivity study is in good agreement with the underlying physical phenomena and improves the understanding of the resonances. Practical implications: The fast model supplemented by the sensitivity factors can be used in EMC design and optimization. Originality/value: The proposed method is original in the sense of combining a polynomial chaos metamodel with a low-cost integral equation model to reduce the computational demand for the sensitivity study. [ABSTRACT FROM AUTHOR]