基于单成像雷达的目标瞬时速度与航向并行估计
Parallelized Instantaneous Velocity and Heading Estimation of Objects using Single Imaging Radar
AI总结：
针对自动驾驶等场景下高分辨率成像雷达对目标瞬时速度和航向的估计需求，提出基于FMCW雷达的并行计算方案，在保证精度的同时大幅提升运算速度。
摘要：
高分辨率成像雷达的发展催生了大量实用应用，尤其在汽车领域。随着主动交通安全与自动驾驶受到越来越多的关注，这类成像雷达将成为自动驾驶系统的核心组件。此类高分辨率雷达最重要的任务之一是估计被检测目标（车辆、行人等）的瞬时速度和航向角。可行的估计方法在实时场景中需具备足够快的速度、无偏差，且对微多普勒效应、噪声及其他系统变化具有鲁棒性。本研究提出一种并行计算方案，利用调频连续波（FMCW）雷达实现实时、精准的矢量速度测定。研究采用中心频率78.6 GHz、带宽4 GHz的FMCW雷达采集的交通数据对所提方案进行测试。实验表明，所提出的并行算法运行速度远快于传统同类算法，且精度无任何损失。
英文摘要：
The development of high-resolution imaging radars introduce a plethora of useful applications, particularly in the automotive sector. With increasing attention on active transport safety and autonomous driving, these imaging radars are set to form the core of an autonomous engine. One of the most important tasks of such high-resolution radars is to estimate the instantaneous velocities and heading angles of the detected objects (vehicles, pedestrians, etc.). Feasible estimation methods should be fast enough in real-time scenarios, bias-free and robust against micro-Dopplers, noise and other systemic variations. This work proposes a parallel-computing scheme that achieves a real-time and accurate implementation of vector velocity determination using frequency modulated continuous wave (FMCW) radars. The proposed scheme is tested against traffic data collected using an FMCW radar at a center frequency of 78.6 GHz and a bandwidth of 4 GHz. Experiments show that the parallel algorithm presented performs much faster than its conventional counterparts without any loss in precision.
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