A Target Detection Algorithm of Neural Network Based on Histogram Statistics

Jiang, Shuai; Pang, Yalong; Wang, Luyuan; Yu, Jiahui; Cheng, Bowen; Li, Zongling

doi:10.1109/igarss39084.2020.9323126

Cited by 1 publication

(1 citation statement)

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“…This shift prioritises the use of machine learning applications in the field (Cai et al , 2021; Arab et al , 2021), also considering different signal pre-processing (Kanona et al , 2022) in radar systems. 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.…”

Section: Introductionmentioning

confidence: 99%

Analysis of data-driven approaches for radar target classification

Coşkun,

Bilicz

2024

COMPEL

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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.

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