Paper Info
Title
Time-Frequency Feature Enhancement of Moving Target Based on Adaptive Short-Time Sparse Representation
Abstract
Accurate time-frequency (TF) feature extraction of moving target is a challenging task due to the poor resolution and serious cross-terms of the conventional TF analysis (TFA) methods. In this letter, an effective TFA algorithm based on the adaptive short-time sparse representation (ASTSR) is proposed to enhance the TF feature of moving target. First, the limitation of the Fourier-transform-based short-time TFA is revealed from the motion approximation perspective. Then, to achieve accurate motion approximation, the width of the analysis window is determined adaptively by minimizing the bandwidth of each short-time signal individually. Finally, TF representation (TFR) with high energy concentration is obtained using the sparsity of these signal segments in the chirp dictionary. Comparisons indicate that the ASTSR provides high-resolution TFRs without producing interference terms at an acceptable computational cost while performing well in weak component expressing and signal denoising. Furthermore, an ISAR imaging example confirms the potential of the proposed method.
Year
DOI
Venue
2022
10.1109/LGRS.2022.3194552
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Keywords
DocType
Volume
Dictionaries, Frequency modulation, Time-frequency analysis, Chirp, Radar, Bandwidth, Radar imaging, Adaptive signal processing, feature enhancement, moving target, sparse representation (SR), time-frequency analysis (TFA)
Journal
19
ISSN
Citations 
PageRank 
1545-598X
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Yang Yang1612174.82
Yongqiang Cheng202.03
Wu Hao35037.39
Zheng Yang400.68
Hongqiang Wang510623.75