Wenqing Cui

Wenqing Cui

Undergraduate in Harbin Institute of Technology, Weihai, China

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Radar Waveform Design with Cognitive Local Low Range Sidelobes Based on Particles Swarm Assisted Projection Optimization

Published in Remote Sensing, 2022

In modern electronic warfare, cognitive radar with knowledge-aided waveforms would show significant flexibility in anti-interference. In this paper, a novel method, named particle swarm-assisted projection optimization (PSAP), is introduced to design phase-coded waveforms with multi-level low range sidelobes, which mainly considers the stability for randomized initialization under the unimodular constraint. Firstly, the mathematical problem corresponding to avoid the range sidelobe masking from multiple non-cooperative targets or interference is formulated by giving different threat levels. Then, based on the alternating direction decomposition idea, the original problem is divided into triple-variable ones where these non-linear approximations can be solved via alternating projections along with FFT. Furthermore, the PSAP method with swarm intelligence, learning factor, and particle-assisted projection could ensure the optimization convergence in a parallel way, which could relax the non-convex constraint and enhance the global exploiting performance. Finally, simulations for several typical scenarios and numerical results are all provided to assess the waveforms generated by PSAP and other prevalent ones.

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Handwriting Number Recognition Based on Millimeter-wave Radar with Dual-view Feature Fusion Network

Published in , 2022

Against the epidemic background, the contactless human-computer interaction has great application prospects in the medical and health field. Among them, using gesture recognition method to realize non-contact instrument control is becoming the hotspot. To improve the robustness and accuracy, a method is proposed to realize the digital gesture recognition based on dual-view sequential feature fusion of millimeter-wave radars in this paper. Firstly, time series echo data of gesture numbers 0～9 from positive and side perspectives are collected synchronously. Secondly, datasets from different perspectives are preprocessed by implementing clutter suppression and data compression. Furthermore, the Attention embedded Dual View Fusion Network (ADVFNet) is constructed based on the intrinsic correlation of temporal features. Finally, using the collected dataset, the task of training network, fusing sequential feature, and recognizing digital gesture could be completed. Experimental results show that the recognition accuracy of proposed method is about 95%, which has faster network convergence and better model generalization ability compared with several existing methods. Moreover, the method could provide a new idea for future human-computer interaction of millimeter-wave radars. Key words: Millimeter-wave radar; Gesture number recognition; Dual-View Fusion Network (DVFNet); Attention mechanism

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CVEN3501 Water Resources Engineering

Undergraduate course, School of Civil and Environmental Engineering, UNSW Sydney, 2019

  • Teaching period: T1, 2019
  • Position: Teaching Assistant
  • Role: Demonstrator and Tutor
  • Number of students: 610
  • Course Profile: Download

CVEN9625 Fundamentals of Water Engineering

Masters course, School of Civil and Environmental Engineering, UNSW Sydney, 2019

  • Teaching period: S2, 2018; T1&3, 2019.
  • Position: Teaching Assistant
  • Role: Demonstrator and Tutor
  • Number of students: ~200
  • Course Profile: Download