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Zhaocheng Yang

Researcher at Shenzhen University

Publications -  63
Citations -  1047

Zhaocheng Yang is an academic researcher from Shenzhen University. The author has contributed to research in topics: Space-time adaptive processing & Radar. The author has an hindex of 13, co-authored 60 publications receiving 775 citations. Previous affiliations of Zhaocheng Yang include National University of Defense Technology.

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Orbital-Angular-Momentum-Based Electromagnetic Vortex Imaging

TL;DR: In this article, a novel radar imaging technique based on orbital angular momentum (OAM) modulation is presented, which can benefit the development of novel information-rich radar based on OAM, as well as radar target recognition.
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$L_1$ -Regularized STAP Algorithms With a Generalized Sidelobe Canceler Architecture for Airborne Radar

TL;DR: Novel l1-regularized space-time adaptive processing algorithms with a generalized sidelobe canceler architecture for airborne radar applications with a sparse regularization to the minimum variance criterion are proposed.
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On Clutter Sparsity Analysis in Space–Time Adaptive Processing Airborne Radar

TL;DR: The clutter sparsity observed by STAP radar systems is detailed and a theoretical analysis on clutterSparsity for a side-looking uniform linear array with constant pulse repetition frequency, constant velocity, and no crab is performed.
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Sparsity-aware space-time adaptive processing algorithms with L 1 -norm regularisation for airborne radar

TL;DR: Novel sparsity-aware space-time adaptive processing algorithms with L 1 -norm regularisation for airborne phased-array radar applications with fast signal-to-interference-plus-noise-ratio convergence and good performance of the proposed algorithms are achieved.
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Adaptive clutter suppression based on iterative adaptive approach for airborne radar

TL;DR: A novel IAA scheme to adaptively suppress the ground clutter by using the secondary training data (STD) and a modified IAA algorithm employing a soft-thresholding to adaptors determine the entries of each iteration that should be updated to reduce the computational complexity.