汽车MIMO SAR中的运动估计和补偿
Motion Estimation and Compensation in Automotive MIMO SAR
摘要：
随着自动驾驶汽车的出现，自动驾驶系统将不得不依赖大量的异构传感器来对周围环境进行动态感知。合成孔径雷达（SAR）系统通过利用车辆的自我运动来提高传统大众市场雷达的分辨率，需要非常准确的轨迹知识，通常与汽车级导航系统不兼容。针对这一问题，本文对汽车SAR系统中的轨迹估计误差进行了分析、估计和补偿，提出了完整的残差运动估计和补偿工作流程。我们首先定义的几何采集和基本的处理步骤的多输入多输出（MIMO）SAR系统。然后，我们解析地推导出典型的运动误差在汽车SAR成像的影响。根据推导出的模型，详细介绍了该程序，概述了其实际实施的指导方针。我们所提出的技术的有效性，通过收集的实验数据由77 GHz的雷达安装在一个前瞻性的配置。
英文摘要：
With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle's ego-motion, requiring a very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this regard, this paper deals with the analysis, estimation and compensation of trajectory estimation errors in automotive SAR systems, proposing a complete residual motion estimation and compensation workflow. We start by defining the geometry of the acquisition and the basic processing steps of Multiple-Input Multiple-Output (MIMO) SAR systems. Then, we analytically derive the effects of typical motion errors in automotive SAR imaging. Based on the derived models, the procedure is detailed, outlining the guidelines for its practical implementation. We show the effectiveness of the proposed technique by means of experimental data gathered by a 77 GHz radar mounted in a forward looking configuration.
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