Facilitated by the recent emergence of radio frequency (RF) modeling and simulation tools purposed for adaptive radar processing applications, data-driven approaches to classical problems in radar have rapidly grown in popularity over the past decade. Despite this surge, limited focus has been directed toward the theoretical foundations of these data-driven approaches. In this regard, using adaptive radar processing techniques, we propose a data-driven approach in this work to address the classical problem of radar target localization post adaptive radar detection. To give context to the performance of this data-driven approach, we first analyze the asymptotic breakdown signal-to-clutter-plus-noise ratio (SCNR) threshold of the normalized adaptive matched filter (NAMF) test statistic within the context of radar target localization, and augment this analysis through our proposed deep learning framework for target location estimation. In this procedure, we generate comprehensive datasets by randomly placing targets of variable strengths in predetermined constrained areas using RFView, a site-specific, digital twin, RF modeling and simulation tool. For each radar return from these predefined constrained areas, we generate heatmap tensors in range, azimuth, and elevation of the NAMF test statistic, and of the output power of a generalized sidelobe canceller (GSC). Using our deep learning framework, we estimate target locations from these heatmap tensors to demonstrate the feasibility of and significant improvements provided by our data-driven approach across matched and mismatched settings.

翻译：近年来，随着专为自适应雷达处理应用设计的射频建模与仿真工具的兴起，数据驱动方法在经典雷达问题中的应用在过去十年间迅速普及。尽管这一趋势蓬勃发展，但针对这些数据驱动方法理论基础的关注仍十分有限。为此，本文利用自适应雷达处理技术，提出一种数据驱动方法来解决自适应雷达检测后的经典雷达目标定位问题。为阐明该数据驱动方法的性能，我们首先分析了归一化自适应匹配滤波器（NAMF）检验统计量在雷达目标定位中的渐近击穿信号-杂波-噪声比（SCNR）阈值，并通过所提出的深度学习框架增强该分析以用于目标位置估计。在此过程中，我们利用场地特定数字孪生射频建模与仿真工具RFView，在预设受限区域内随机布设不同强度目标，生成综合数据集。针对这些预设受限区域的每个雷达回波，我们生成包含距离、方位和俯仰维度的NAMF检验统计量热力图张量，以及广义旁瓣对消器（GSC）输出功率热力图张量。通过所提出的深度学习框架，我们利用这些热力图张量估计目标位置，验证了数据驱动方法在匹配与非匹配场景下的可行性及其显著性能提升。