Supervised synthetic aperture radar (SAR) automatic target recognition (ATR) methods rely on a closed-set assumption and struggle to recognize unseen target categories without labeled SAR samples. Zero-shot learning (ZSL) offers a promising solution by transferring knowledge from seen classes and external semantic prio...
Rui Zhu, Tian-Wen Zhang, Xiao-Ling Zhang et al.· IEEE Geoscience and Remote S...· 1 citation
Motion-error-induced phase errors in forward-looking multichannel synthetic aperture radar (FLMC-SAR) exhibit 2-D space-variant characteristics, which present a critical challenge for high-resolution imaging. Traditional space-variant autofocus (SVAF) methods, which rely on separable echoes, are ineffective for FLMC-SA...
Zhe Liu, Hao-Ran Qi, Fu-Jie Zhang et al.· IEEE Transactions on Geoscie...· 0 citations
Generating synthetic aperture radar (SAR) images at unknown azimuth angles under limited sample conditions remains a challenging task, since target scattering characteristics vary significantly with observation angle and are difficult to model effectively using only image-domain priors. To address this issue, this pape...
The rapiddevelopment of frequency-modulated continuous wave (FMCW) radar has introduced critical mutual interference challenges. Currently, compressed sensing (CS) and deep learning offer promising interference suppression capabilities, while conventional CS implementations face computational bottlenecks and hyperparam...
Hao Zhang, Shun-Jun Wei, R. Min et al.· IEEE Transactions on Aerospa...· 0 citations
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