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

职业：探索自适应、协作和智能雷达传感和感知的基础知识

• 批准号: 2340029
• 负责人: Shunqiao Sun
• 金额: $ 50.68万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-08-15 至 2029-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340029&HistoricalAwards=false
• 关键词: CAREER; Towards; Fundamentals; Adaptive; Collaborative; CAREER; Towards; Fundamentals; Adaptive; Collaborative

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.

汽车雷达成像代表了对汽车主动安全性和自动驾驶的迫切技术需求。在毫米波频率下运行的汽车雷达（通常约为77 GHz）是必不可少的，这是由于其在所有天气条件下的遮挡情况下的测量范围，速度，速度的卓越能力，在遮挡情况下提供更好的感知性能，并且比LIDAR低得多。最先进的汽车雷达容易出现相互干扰和多径问题，并且其有限的角度分辨率（约13度）不足以支持完全自主驾驶的感知任务。该职业项目旨在通过探索适应性，协作敏感性和雷达成像物理学原理来创新汽车雷达感知，以服务社会中的个人福祉和减少美国高速公路的致命事故。它加深了我们对这种敏感性如何增强动态范围并解决雷达成像逆问题的性质的理解。解决了学术界与行业之间必要的合作中汽车雷达成像中所面临的挑战。通过与汽车行业的合作，该项目将使研究更具影响力和相关性，从而加快研究结果向汽车行业应用的过渡。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的宣传活动和招募人数不足的群体和工程女性，该项目将促进STEM研究的多样性，激发他们在雷达领域进行先进研究。该职业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逆问题。该项目通过雷达成像物理学的工业系统来解决自动驾驶汽车问题的强大雷达感知，以推动创新的机器学习算法的设计。智能信号处理和机器学习技术将在多层开发，包括1）基于学习的自适应雷达传输参数调整，2）迭代优化算法，以增强基于建设性干扰的自动雷达敏感性的动态范围，3）基于基于Mangial Imental Imental Imental Imental Imental-Network radar radar radar radar radar radar，4鲁棒环境感知的算法。这些新技术将展示科学见解如何提高高分辨率雷达成像，并在高度动态的自主驾驶环境中稳健地检测和分类对象。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准通过评估来通过评估来获得的支持。