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价值数十亿美元的行业，并且正在迅速增长。由于其全天候和昼夜运行的能力，汽车雷达可用于为车辆提供多种安全功能。它也是自动驾驶的关键技术。然而，随着许多车辆配备了汽车雷达，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牺牲品。此外，从一个雷达的发射器到另一个雷达的接收器的直接爆炸比行人的反射信号强得多。因此，在太多的车辆和其他汽车配备汽车雷达之前，迫切需要解决相互干扰问题。该项目的目标是提供经济实惠、技术可行、易于应用的变革性解决方案，该项目涉及推进汽车雷达行业频谱接入新技术的基础知识，并应用工程原理来满足未来汽车雷达的需求系统，同时在多个方面推进工程知识。具体而言，该项目涉及调查实际可行和灵活的探测波形，以改善相互干扰抑制，设计先进的信号处理算法，以增强汽车雷达共存性和提高分辨率，提高空间分辨率和共存性，同时通过开发一位汽车雷达接收器降低成本和功耗，利用车对车通信的优势，开发相互协作的策略和信号处理算法，使近距离的汽车雷达系统能够共享足够的信息，形成多基地多站通信网络，输入多输出雷达网络。

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