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Creators/Authors contains: "Zheng, Ruxin"

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  1. ; ; ; ; (, IEEE Transactions on Radar Systems)
    Free, publicly-accessible full text available April 22, 2026
  2. ; ; ; (, IEEE)
    Free, publicly-accessible full text available April 6, 2026
  3. ; ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available April 6, 2026
  4. ; ; ; ; ; (, IEEE)
    Full Text Available 
  5. ; ; ; ; (, IEEE Sensors Journal)
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  6. ; ; ; ; (, IEEE)
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  7. ; ; ; (, IEEE)
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  8. ; ; ; (, IEEE Transactions on Aerospace and Electronic Systems)
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  9. ; ; (, IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM))
    We present a deep reinforcement learning approach to design an automotive radar system with integrated sensing and communication. In the proposed system, sparse transmit arrays with quantized phase shifter are used to carry out transmit beamforming to enhance the performance of both radar sensing and communication. Through interaction with environment, the automotive radar learns a reward that reflects the difference between mainlobe peak and the peak sidelobe level in radar sensing mode or communication user feedback in communication mode, and intelligently adjust its beamforming vector. The Wolpertinger policy based action-critic network is introduced for beamforming vector learning, which solves the dimension curse due to huge beamforming action space. 
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  10. ; ; ; (, IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM))
    The potentials of automotive radar for autonomous driving have not been fully exploited due to the difficulty of extracting targets' information from the radar signals and the lack of radar datasets. In this paper, a novel signal processing pipeline is proposed to address the max ambiguous velocity reduction issue introduced by staggered time division multiplexing (TDM) scheme of high resolution imaging radar system with a large number of transmit antennas. A dataset of 1,410 synchronized frames (stereo cameras, LiDAR, radar) with three classes, i.e., bus, car, and people, is constructed from field experiments. Next, we implement a vanilla SpectraNet and show its promising performance on moving object detection and classification with a mean average precision (mAP) of 81.9% at an intersection over union (IoU) of 0.5. 
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