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

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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.