Data-Driven Target Localization Using Adaptive Radar Processing and Convolutional Neural Networks

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.