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Huichang Zhao

Researcher at Nanjing University of Science and Technology

Publications -  43
Citations -  307

Huichang Zhao is an academic researcher from Nanjing University of Science and Technology. The author has contributed to research in topics: Synthetic aperture radar & Frequency modulation. The author has an hindex of 5, co-authored 36 publications receiving 144 citations.

Papers
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A New Imaging Algorithm for Forward-Looking Missile-Borne Bistatic SAR

TL;DR: An extended nonlinear chirp scaling (NLCS) algorithm for the forward-looking missile-borne bistatic SAR (FLMB-SAR) is proposed and the simulation results are exhibited to validate the correctness of the analysis and prove the two-dimensional (2-D) imaging ability of the FLMB- SAR.
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A New Chirp Scaling Algorithm for Highly Squinted Missile-Borne SAR Based on FrFT

TL;DR: A new fractional chirp scaling algorithm (FrCSA) for highly squinted missile-borne SAR is proposed from high resolution point of view and results indicate that the FrCSA offers better focusing capabilities, greater peak sidelobe ratio (PSLR), and integrated sidelobe ratios (ISLR) by appropriately choosing the rotation angles for the range and azimuth fractional Fourier transform (FrFT).
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An extended nonlinear chirp scaling algorithm for missile borne SAR imaging

TL;DR: An extended nonlinear chirp scaling algorithm for focusing missile borne synthetic aperture radar data is proposed, compensating the azimuth dependent characteristic of the Azimuth FM rate and adopting higher order approximation processing, which means easier implementation and higher efficiency.
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Study of ultra-wideband fuze signal processing method based on wavelet transform

TL;DR: The simulation results show that this signal processing method can effectively estimate the distance and velocity of target, also it can reduce data volume of signal processing greatly without impacting the measurement which is good to the real-time processing and engineering applications.
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Classification of UAV-to-Ground Targets Based on Micro-Doppler Fractal Features Using IEEMD and GA-BP Neural Network

TL;DR: Comparison with current algorithms under various signal-to- noise ratios (SNRs) demonstrates that method in this paper has higher accuracy and better anti-noise performance.