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1 – 2 of 2This 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…
Abstract
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.
Details
Keywords
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…
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.
Details