Use of ICA to Separate Micro-Doppler Signatures in ISAR Images of Aircraft That Has Fast-Rotating Parts

In this article, we propose a new signal-decomposition algorithm composed of three steps: principal components analysis to yield high-resolution range profiles with improved signal-to-noise ratio; estimation of whitening and mixing matrices using independent component analysis in distributed radar n...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2022-02, Vol.58 (1), p.234-246
Main Authors: Choi, Inoh, Kang, Kibong, Kim, Kyungtae, Park, Sanghong
Format: Article
Language:English
Subjects:
Aircraft
Algorithms
Angular velocity
Blurring
Decomposition
Distributed radar network
Imaging
Independent component analysis
independent component analysis (ICA)
Inverse synthetic aperture radar
inverse synthetic aperture radar (ISAR)
Noise sensitivity
principal component analysis (PCA)
Principal components analysis
Radar
Radar cross-sections
Radar imaging
Radar networks
range migration
Rigid structures
Rotating bodies
Rotation
Signal resolution
Signal to noise ratio
Signatures
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Summary:In this article, we propose a new signal-decomposition algorithm composed of three steps: principal components analysis to yield high-resolution range profiles with improved signal-to-noise ratio; estimation of whitening and mixing matrices using independent component analysis in distributed radar network; and signal decomposition to obtain inverse synthetic aperture radar (ISAR) images that correspond to rigid body and fast-rotating parts by using estimated two matrices regardless of complicated range migration. The proposed method efficiently removes image blur caused by micro-Doppler (MD) signals of rotating parts and reduces the sensitivity to noise. In simulations, our proposed method could perform accurate and robust removal of the MD signatures to obtain a focused ISAR image of an aircraft with fast-rotating parts.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2021.3098110