CAREER: Towards Fundamentals of Adaptive, Collaborative and Intelligent Radar Sensing and Perception

汽车雷达成像代表了汽车主动安全和自动驾驶感知的迫切技术需求。在毫米波频率（通常在77 GHz左右）下工作的汽车雷达是必不可少的，因为它在测量范围和速度方面具有卓越的能力，并且在所有天气条件下的遮挡情况下提供更好的感知性能，而且成本比激光雷达低得多。最先进的汽车雷达容易受到相互干扰和多路径问题，其有限的角度分辨率（约13度）不足以促进完全自动驾驶的感知任务。该CAREER项目旨在通过探索自适应、协同传感和雷达成像物理原理，创新汽车雷达感知，服务于社会个人福祉，减少美国高速公路上的致命事故。它加深了我们对这种传感如何增强动态范围和解决雷达成像逆问题的病态性质的理解。解决汽车雷达成像的挑战需要学术界和工业界之间的合作。通过与汽车行业的合作，该项目将使研究更具影响力和针对性，加快研究成果向汽车行业应用的转变。自动驾驶汽车研究有助于吸引代表性不足的研究人员。通过K-12外展活动和招募工程领域代表性不足的群体和女性，该项目将促进STEM研究的多样性，激励他们在雷达领域追求先进的研究。这个CAREER项目通过研究基于学习的自适应和协作方法来解决汽车雷达成像中固有的不适定逆问题。该项目通过结合雷达成像物理学来推动创新机器学习算法的设计，解决了自动驾驶汽车的强大雷达感知问题。智能信号处理和机器学习技术将在多个层面上发展，包括1)基于学习的自适应雷达传输参数调整，2)通过利用建设性干扰来增强汽车雷达感知动态范围的迭代优化算法，3)基于模型的学习框架，用于汽车雷达网络中的协作高分辨率雷达成像。4)用于鲁棒环境感知的物理感知雷达机器学习算法。这些新技术将展示科学见解如何推进高分辨率雷达成像，并在高度动态的自动驾驶环境中稳健地检测和分类物体。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Automotive radar imaging represents a pressing technological need in perception for automotive active safety and autonomous driving. Automotive radar operating at millimeter-wave frequencies (typically around 77 GHz) is indispensable due to its superior capability in measuring range, velocity, and offering better perception performance in occlusion situations under all weather conditions and much lower cost than Lidar. The state-of-the-art automotive radar is prone to mutual interference and multipath issues, and its limited angular resolution—approximately 13 degrees—is inadequate for facilitating perception tasks for fully autonomous driving. This CAREER project aims to innovate the automotive radar perception in service of well-being of individuals in society and reduction of fatal accidents on U.S. highways by exploring adaptive, collaborative sensing and radar imaging physics principles. It deepens our understanding of how such sensing can enhance the dynamic range and address the ill-posed nature of the radar imaging inverse problem. Addressing the challenges in automotive radar imaging necessitates collaboration between academia and industry. Through collaboration with automotive industry sector, this project will make the research more impactful and pertinent, hastening the transition of research findings into automotive industry applications. Autonomous vehicle research helps attract underrepresented researchers. Through K-12 outreach activities and recruiting underrepresented groups and women in engineering, this project will promote diversity in the STEM research, inspiring them to pursue advanced research in radar field. This CAREER project addresses the ill-posed inverse problem inherent in automotive radar imaging by investigation of learning based adaptive and collaborative methodologies. The project tackles the robust radar perception for autonomous vehicles problem through incorporation of the radar imaging physics to drive the design of innovative machine learning algorithms. Intelligent signal processing and machine learning techniques will be developed at multiple layers, including 1) learning-based adaptive radar transmit parameter adjustment, 2) iterative optimization algorithms to enhance the dynamic range of automotive radar sensing by exploiting constructive interference, 3) model-based learning framework for collaborative high-resolution radar imaging in an automotive radar network, 4) physics-aware radar machine learning algorithms for robust environment perception. These new techniques will demonstrate how the science insights advance high resolution radar imaging, and robustly detect and classify objects in the highly dynamic autonomous driving environment.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.