Toward Data-Driven STAP Radar
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Using an amalgamation of techniques from classical radar, deep learning, and computer vision, we characterize our ongoing data-driven approach to space-time adaptive processing (STAP) radar. We generate a rich dataset of received radar signals for random target locations and strengths via the RFView simulation software, and generate heatmap image tensors in range, azimuth, and elevation of this received signal data. We present a regression convolutional neural network (CNN) for estimating target locations from these image tensors, and conclude by demonstrating the feasibility of and significant improvements provided by our data-driven approach.