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

与传统相控阵相比，多输入多输出(MIMO)雷达具有许多优点。它们可以在相同数量的天线下实现更高的分辨率。它们还可以实现大视场，同时照亮多个目标，这意味着更快的检测时间。在不损害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、高分辨率的雷达将促进自动驾驶、智能环境、智能家居和物联网传感的发展，并将使智能患者护理、老年监测、健身助手等依赖于传感的应用成为可能。在新冠肺炎强制家庭隔离、对弱势群体的监管有限的时代，雷达设备可以提供生命体征信息，或者检测跌倒，而不会像监控摄像头那样侵犯人们的隐私。使用特殊设计的稀疏线性阵列(SLA)的MIMO雷达可以在不损失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发射波形的优化设计，可以使雷达性能保持在接近全填充阵列的水平。然而，根据最少的天线数来寻找最优的稀疏阵结构是一个困难的组合问题。该项目将通过(I)提供一种新的天线选择框架，(Ii)开发基于频率扫描超材料(MTM)天线的SLA MIMO雷达原型，以及(Iii)开发利用MIMO SLA雷达的高分辨率和大视场的实时活动监测和用户识别方案，从而推动基于SLA的MIMO雷达作为一种高性价比成像雷达的最新发展。(I)提出了一种新的用于天线选择的机器学习方法，它为处理任何性能指标提供了一个统一的框架。提出的方法的新颖性在于它能够让多个Softmax模型一起工作。(2)MTM天线的使用带来了额外的好处，即允许通过改变天线频率来容易地改变波束仰角。这种优势将被利用来在3-D空间中寻找目标，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，通过应用所提出的SLA设计方法，可以在对应于所选仰角方向的二维空间中设计波束方向图。MTM的色散特性提供了一种实时、低复杂度的波束扫描机制，而采用适当的波形工程技术的SLA MIMO雷达将产生具有更高角度分辨率的大规模虚拟阵列。因此，SLA MIMO雷达与MTM天线的结合将实现一种前所未有的雷达架构，具有更大的视场、更精细的分辨率和更少的天线射频阵面。(3)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。创新的神经网络结构将被设计出来，以实现无需设备的用户活动监控。预计多用户识别机制将揭示嵌入高分辨率点云移动中的独特用户特定活动特征，促进广泛的新兴移动应用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.