Enhanced Automotive Radar Coexistence and Performance

增强的汽车雷达共存性和性能

• 批准号: 1708509
• 负责人: Jian Li
• 金额: $ 30万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1708509&HistoricalAwards=false
• 关键词: Enhanced; Automotive; Radar; Coexistence; Performance

The automotive radar market is already a multi-billion dollar industry and is growing rapidly. Due to its capabilities for all weather and day-and-night operations, automotive radar can be used to provide multiple safety functions for a vehicle. It is also a key enabling technology for autonomous driving. With many vehicles equipped with automotive radars, however, this technology can become the victim of its own success, due to the inevitable mutual interferences among nearby radars. Moreover, the direct blast from the transmitter of one radar to the receiver of another radar is much stronger than the reflected signal from a pedestrian. Therefore, there is an urgent need to address mutual interference problems before too many vehicles and other automobiles are equipped with automotive radars. The goal of this project is to provide transformative solutions that are affordable, technically feasible, easy to apply, and consistent with the technological trends via exploiting the full potential of spectrum sharing in the automotive radar frequency bands.This project involves advancing fundamental knowledge in new technologies for spectrum access in the automotive radar industry and applies engineering principles to address the needs of future automotive radar systems, while advancing engineering knowledge in multiple fronts. Specifically, this project involves investigating practically feasible and flexible probing waveforms for improved mutual interference suppression, devising advanced signal processing algorithms for enhanced automotive radar coexistence and improved resolution, enhancing spatial resolution and coexistence while reducing cost and power consumption through developing one-bit automotive radar receivers, and developing mutual cooperation strategies and signal processing algorithms by taking advantage of vehicle-to-vehicle communications so that automotive radar systems in close proximities can share sufficient information to form a multi-static multiple-input multiple-output radar network.

汽车雷达市场已经是一个数十亿美元的行业，并且正在迅速发展。由于其适用于所有天气和日夜操作的功能，汽车雷达可用于为车辆提供多个安全功能。它也是用于自动驾驶的关键促进技术。但是，由于许多车辆配备了汽车雷达，因此由于附近雷达之间不可避免的相互干扰，这项技术可能成为其自身成功的受害者。此外，从一个雷达的发射机到另一个雷达的接收器的直接爆炸要比行人反射的信号强得多。因此，在太多的车辆和其他汽车配备了汽车雷达之前，迫切需要解决相互干扰问题。该项目的目的是提供可负担得起，技术上可行，易于应用的变革性解决方案，并通过利用在汽车雷达频率频段中的频谱共享的全部潜力来与技术趋势一致多个方面。具体而言，该项目涉及调查实际可行和灵活的探测波形，以改善相互干扰的抑制，从而设计高级信号处理算法，以增强自动雷达的共存并改善解决方案，并改善空间分辨率，并增强空间分辨率，并通过发展策略和相互作用的策略，并通过降低策略和功能来降低策略和功能，从而提高策略和电力，并通过降低策略和电力。车辆到车辆通信，以使近距离的汽车雷达系统可以共享足够的信息以形成多静电多输入多输入雷达网络。

项目成果

Tunable Adaptive Target Detection With Kernels in Colocated MIMO Radar

• DOI: 10.1109/tsp.2020.2975371
• 发表时间: 2020
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: A. Zaimbashi;Jian Li

One-Bit Radar Processing With Time-Varying Sampling Thresholds

• DOI: 10.1109/tsp.2019.2939086
• 发表时间: 2019-10
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: A. Ameri;Arindam Bose;Jian Li;M. Soltanalian

One-Bit Compressive Radar Sensing in the Presence of Clutter

• DOI: 10.1109/taes.2019.2916532
• 发表时间: 2020-02
• 期刊: IEEE Transactions on Aerospace and Electronic Systems
• 影响因子: 4.4
• 作者: Sayed Jalal Zahabi;M. M. Naghsh-M.;M. Modarres-Hashemi;Jian Li

Sinusoidal Parameter Estimation From Signed Measurements Via Majorization–Minimization Based RELAX

• DOI: 10.1109/tsp.2019.2899804
• 发表时间: 2019-02
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Jiaying Ren;Tianyi Zhang;Jian Li;P. Stoica

One-bit digital radar

• DOI: 10.1109/acssc.2017.8335529
• 发表时间: 2017-10
• 期刊: 2017 51st Asilomar Conference on Signals, Systems, and Computers
• 作者: Jiaying Ren;Jian Li