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New class separability measure for radar signals
Author(s) -
Jeong SeongJae,
Lee SeungJae,
Kim KyungTae
Publication year - 2019
Publication title -
microwave and optical technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 76
eISSN - 1098-2760
pISSN - 0895-2477
DOI - 10.1002/mop.31513
Subject(s) - radar , inverse synthetic aperture radar , radar imaging , measure (data warehouse) , computer science , correlation coefficient , synthetic aperture radar , artificial intelligence , continuous wave radar , range (aeronautics) , pulse doppler radar , signal (programming language) , pattern recognition (psychology) , mathematics , engineering , statistics , telecommunications , data mining , aerospace engineering , programming language
Abstract We propose a novel class separability measure for radar signals. To evaluate the discriminatory power of the radar signals, the proposed method first either calculates the correlation coefficients between two radar cross sections (RCSs), linearly shifts one‐dimensional (1‐D) high‐resolution‐range profiles (HRRPs), or rotates two 2‐D inverse synthetic aperture radar (ISAR) images. Then, it uses the correlation coefficient when the two radar signals are best aligned. Next, the proposed method obtains new correlation‐ based discriminant matrices (CDMs) using the correlation coefficients. The mean value of the CDMs precisely represents the discriminatory power of the radar signal. Our experimental results show that the proposed method can accurately measure target separability.