CRII: CIF: A Sparse Framework Based Automotive Radar Sensing for Autonomous Vehicles

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。毫米波汽车雷达已成为自动驾驶的关键技术，以便在所有天气条件下提供环境感知。然而，成功部署面临着若干挑战。首先，要求汽车雷达在方位角和仰角方向上都具有高的角分辨率，以便产生表示物体形状的点云并实现目标识别。通过简单地增加天线阵列元件的数量来扩大天线阵列孔径涉及巨大的成本和大的形状因子，并且在汽车雷达应用中是不可行的。此外，随着越来越多的车辆配备雷达，相互雷达干扰的可能性增加。该项目旨在探索一种新的联合稀疏频率和稀疏阵列信号处理框架，使自动驾驶汽车能够以低成本，小外形和低相互干扰概率实现高分辨率的环境感知。该项目将产生适用于各种雷达传感应用的算法，包括远程医疗中患者生命体征的遥感，这是COVID-19大流行期间的关键需求。拟议的教育计划创造了机会，以指导高级顶点设计，丰富了雷达信号处理课程的课程，并通过现有的多元文化工程计划促进少数民族学生的推广。采用多输入多输出雷达技术合成的稀疏阵列实现高角度分辨率具有挑战性，因为稀疏阵列的高旁瓣会导致角度模糊。此外，传统的雷达啁啾占据了一个恒定的脉冲重复频率的大带宽，大大增加了相互干扰的机会。该项目的技术目标分为两个任务。第一个任务研究了一种基于矩阵补齐的阵列插值方法来填充一维和二维稀疏阵列的空洞。将研究低秩雷达数据矩阵的可恢复性与不规则稀疏阵列几何形状之间的关系。为了有效地完成不规则稀疏阵列，高效的迭代硬阈值矩阵完成算法将利用底层低秩Hankel矩阵和块Hankel矩阵的结构和性质。第二个任务研究的认知方法稀疏分配的雷达啁啾在频域和时域，以合成一个高分辨率的距离像，同时显着降低相互干扰的概率。这项任务将设计新颖的优化方法，通过放宽整数变量以实现高效计算，从而在干扰和距离-多普勒峰值旁瓣约束下动态分配发射啁啾。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Millimeter-wave automotive radar has emerged as a key technology in autonomous driving in order to provide environmental perception under all weather conditions. However, successful deployment is facing several challenges. First, automotive radars are required to have high angular resolution in both azimuth and elevation directions in order to produce point clouds representing the shapes of objects and enable target identification. Enlarging antenna array apertures by simply increasing the number of antenna array elements involves both a huge cost and a large form factor, and is not feasible in automotive radar applications. Furthermore, as more vehicles are equipped with radar, the probability of mutual radar interference increases. This project aims to explore a novel joint sparse-frequency and sparse-array signal-processing framework that enables high-resolution environment perception for autonomous vehicles with low-cost, small form factor and low probability of mutual interference. The project will result in algorithms that are applicable to various radar-sensing applications, including the remote sensing of vital signs of patients in telemedicine, a crucial need during the COVID-19 pandemic. The proposed educational plan creates opportunities to guide senior Capstone designs, enriches curriculum in radar-signal-processing courses, and facilitates outreach for minority students through an existing multicultural engineering program. It is challenging to achieve high angular resolution by adopting sparse arrays synthesized via multiple-input and multiple-output radar techniques because the high side lobe associated with sparse arrays would result in angle ambiguity. In addition, conventional radar chirps occupying a large bandwidth with a constant pulse-repetition frequency greatly increase the chance of mutual interference. The technical aims of the project are organized into two tasks. The first task investigates a matrix completion-based array interpolation approach to fill the holes of both one- and two-dimensional sparse arrays. The relationship between the recoverability of low-rank radar data matrices and the irregular sparse-array geometry will be investigated. In order to effectively complete irregular sparse arrays, efficient iterative hard thresholding matrix-completion algorithms will exploit the structures and properties of the underlying low-rank Hankel and block Hankel matrices. The second task investigates a cognitive approach to sparsely allocate the radar chirps in both frequency and temporal domains in order to synthesize a high-resolution range profile while significantly reducing the probability of mutual interference. This task will design novel optimization methods to dynamically allocate the transmit chirps under both interference and range-Doppler peak side lobe constraints by relaxing integer variables for efficient computations.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.