论文标题
移位的传感器汽车雷达成像
Displaced Sensor Automotive Radar Imaging
论文作者
论文摘要
流离失所的汽车传感器成像利用了从多个雷达单元获得的数据的关节处理，每个雷达单元可能具有有限的个人资源，以提高本地化精度。先前的工作要么考虑传感器之间的完美同步，要么采用单个天线雷达，需要高处理成本或缺乏性能分析。与这些作品相反，我们开发了一个流离失所的多输入多输出（MIMO）频率调节连续波（FMCW）雷达信号模型，在粗同步下仅使用框架级比对。我们为常见的汽车雷达处理模式（例如基于点云的融合以及基于原始信号的非连接和相干成像）提供了贝叶斯性能界限。对于非连锁模式，在低计算负载和改善本地化之间提供了妥协，我们利用信号重建的范围谱图的块稀疏性，以避免使用大量数据进行直接的计算成像。对于高分辨率相干成像，我们开发了一种方法，该方法可以自动估计同步误差，并通过利用稀疏驱动的恢复模型来执行位移雷达成像。我们广泛的数值实验证明了这些优势。我们提出的对流离失所的MIMO FMCW雷达的非连通处理可改善位置估计，而不是常规点云融合的顺序。
Displaced automotive sensor imaging exploits joint processing of the data acquired from multiple radar units, each of which may have limited individual resources, to enhance the localization accuracy. Prior works either consider perfect synchronization among the sensors, employ single antenna radars, entail high processing cost, or lack performance analyses. Contrary to these works, we develop a displaced multiple-input multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) radar signal model under coarse synchronization with only frame-level alignment. We derive Bayesian performance bounds for the common automotive radar processing modes such as point-cloud-based fusion as well as raw-signal-based non-coherent and coherent imaging. For the non-coherent mode, which offers a compromise between low computational load and improved localization, we exploit the block sparsity of range profiles for signal reconstruction to avoid direct computational imaging with massive data. For the high-resolution coherent imaging, we develop a method that automatically estimates the synchronization error and performs displaced radar imaging by exploiting sparsity-driven recovery models. Our extensive numerical experiments demonstrate these advantages. Our proposed non-coherent processing of displaced MIMO FMCW radars improves position estimation by an order over the conventional point-cloud fusion.
