MIMO Radar With Sparse Linear Arrays - Theory, Implementation and Applications

稀疏线性阵列 MIMO 雷达 - 理论、实现和应用

• 批准号: 2033433
• 负责人: Athina Petropulu
• 金额: $ 45万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033433&HistoricalAwards=false
• 关键词: MIMO; Radar; Sparse; Linear; Arrays

Multiple-input multiple-output (MIMO) radars have several advantages as compared to traditional phased arrays. They can achieve higher resolution with the same number of antennas. They can also achieve wide field of view, illuminating multiple targets at the same time, which translates to faster detection time. Reduction of the number of active antennas without hurting the radar performance would reduce the cost of the radar, while a low-cost, high resolution radar would advance the state-of-art of autonomous driving, smart environment, smart home, and IoT sensing, and would enable applications such as smart patient care, elderly monitoring, fitness assistant, etc., that rely on sensing. In an era where COVID-19 forced home isolation with limited supervision of vulnerable segments of the population, a radar device could provide information on vital signs, or detect falls without invading people's privacy in the way surveillance cameras would. MIMO radar using specially designed Sparse Linear Arrays (SLAs) can enjoy reduced hardware cost without losing the MIMO radar advantages. An SLA can be thought of as a uniform linear array with only a small number of active antennas. By careful selection of the active antennas and optimal design of transmit waveforms, one can maintain a radar performance close to that of the fully populated array. However, finding an optimal sparse array geometry in terms of the fewest antennas is a difficult combinatorial problem. The proposed project will advance the state-of-art of SLA based MIMO radar as a cost-effective imaging radar by (i) providing a novel framework for antenna selection, (ii) developing an SLA MIMO radar prototype based on frequency-scanning metamaterial (MTM) antennas, and (iii) developing real-time activity monitoring and user identification schemes that leverage the high resolution and wide field of view of MIMO SLA radar.There are several novel aspects in the proposed work. (i) A novel machine learning approach for antenna selection is proposed, which offers a unifying framework for dealing with any performance metric. The novelty of the proposed approach lies in its ability to get multiple softmax models to work together. (ii) The use of MTM antennas brings in the added advantage of allowing for easy change of the beam elevation by varying the antenna frequency. That advantages will be exploited to look for targets in the 3-D space while still using a linear array. By varying the frequency of the MTM antennas, one can select the elevation direction of the transmit beam, and by applying the proposed SLA design method, one can design the beam pattern in the 2-D space corresponding to the selected elevation direction. The frequency scanning capability resulting from the dispersive nature of MTM allows a real time and low complexity beam scanning mechanism, whereas the SLA MIMO radar with proper waveform engineering will generate a large scale virtual array with enhanced angular resolution. As such, the combination of SLA MIMO radar with MTM antennas will enable an unprecedented radar architecture with larger field of view, finer resolution, and small number of antenna RF fronts. (iii) Low-latency signal processing algorithms will be developed for leveraging the large field of view and high angle resolution, that will have the capability to construct 3D user models and identify multiple targets simultaneously. Innovative neural network structures will be devised to enable device-free user activity monitoring. It is expected that the multi-user identification mechanisms will reveal unique user-specific activity characteristics embedded in the movements of high-resolution point clouds, facilitating a broad range of emerging mobile applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

多输入多输出（MIMO）雷达与传统相控阵相比具有若干优点。它们可以用相同数量的天线实现更高的分辨率。它们还可以实现宽视场，同时照亮多个目标，这意味着更快的检测时间。在不影响雷达性能的情况下减少有源天线的数量将降低雷达的成本，而低成本、高分辨率的雷达将推动自动驾驶、智能环境、智能家居和物联网传感的发展，并将实现智能病人护理、老年人监测、健身助理等应用。依赖于感知。在一个新冠肺炎迫使人们居家隔离，对弱势群体的监督有限的时代，雷达设备可以提供有关生命体征的信息，或者检测福尔斯，而不会像监控摄像头那样侵犯人们的隐私。采用特殊设计的稀疏线性阵列（SLA）的MIMO雷达可以在不失去MIMO雷达优势的情况下降低硬件成本。SLA可以被认为是只有少量有源天线的均匀线性阵列。通过仔细选择有源天线和优化设计的发射波形，可以保持雷达性能接近的满布阵列。然而，找到一个最佳的稀疏阵列的几何条件下，最少的天线是一个困难的组合问题。拟议的项目将推进最先进的基于SLA的MIMO雷达作为一种具有成本效益的成像雷达，通过（i）提供一个新的天线选择框架，（ii）开发一个基于频率扫描超材料（MTM）天线的SLA MIMO雷达原型，及（iii）发展真实的-利用MIMO SLA雷达的高分辨率和宽视场的时间活动监视和用户识别方案。建议的工作。(i)提出了一种新的机器学习方法的天线选择，它提供了一个统一的框架，处理任何性能指标。所提出的方法的新奇在于它能够让多个softmax模型一起工作。(ii)MTM天线的使用带来了允许通过改变天线频率来容易地改变波束仰角的附加优点。这一优势将被利用来寻找三维空间中的目标，同时仍然使用线性阵列。通过改变MTM天线的频率，可以选择发射波束的仰角方向，并且通过应用所提出的SLA设计方法，可以设计与所选择的仰角方向相对应的2-D空间中的波束图案。由MTM的色散性质产生的频率扫描能力允许真实的波束扫描机制和低复杂度，而具有适当波形工程的SLA MIMO雷达将生成具有增强的角分辨率的大规模虚拟阵列。因此，SLA MIMO雷达与MTM天线的结合将实现前所未有的雷达架构，具有更大的视场，更高的分辨率和更少的天线RF前端。(iii)将开发低延迟信号处理算法，以利用大视场和高角度分辨率，从而能够构建3D用户模型并同时识别多个目标。将设计创新的神经网络结构，以实现无设备用户活动监控。预计多用户识别机制将揭示嵌入在高分辨率点云运动中的独特的用户特定活动特征，促进广泛的新兴移动的应用。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Optm3sec: Optimizing Multicast Irs-Aided Multiantenna Dfrc Secrecy Channel With Multiple Eavesdroppers

• DOI: 10.1109/icassp43922.2022.9747551
• 发表时间: 2022-01
• 期刊: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: K. Mishra;Arpan Chattopadhyay;Siddharth Sankar Acharjee;A. Petropulu

A Bandwidth Efficient Dual-Function Radar Communication System Based on a MIMO Radar Using OFDM Waveforms

• DOI: 10.1109/tsp.2023.3241779
• 发表时间: 2023
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Zhaoyi Xu;A. Petropulu

DFRC with Improved Communication-Sensing Trade-off via Private Subcarrier Permutations and Pairing with Antennas

• DOI: 10.1109/wcnc51071.2022.9771743
• 发表时间: 2022-04
• 期刊: 2022 IEEE Wireless Communications and Networking Conference (WCNC)
• 作者: Zhaoyi Xu;A. Petropulu

Editorial: Introduction to the Issue on Joint Communication and Radar Sensing for Emerging Applications

社论：关于新兴应用的联合通信和雷达传感问题的介绍

• DOI: 10.1109/jstsp.2021.3119395
• 发表时间: 2021
• 期刊: IEEE Journal of Selected Topics in Signal Processing
• 影响因子: 7.5
• 作者: Masouros, Christos;Heath, Robert;Zhang, J. Andrew;Feng, Zhiyong;Zheng, Le;Petropulu, Athina
• 通讯作者: Petropulu, Athina

IEEE Signal Processing Society PROGRESS: Support for Underrepresented Talent in the Field of Signal Processing [Conference Highlights]

IEEE 信号处理协会 PROGRESS：为信号处理领域代表性不足的人才提供支持 [会议亮点]

• DOI: 10.1109/msp.2021.3067588
• 发表时间: 2021
• 期刊: IEEE Signal Processing Magazine
• 影响因子: 14.9
• 作者: Petropulu, Athina