Web: http://arxiv.org/abs/2209.02890

Sept. 23, 2022, 1:15 a.m. | Shyam Venkatasubramanian, Sandeep Gogineni, Bosung Kang, Ali Pezeshki, Muralidhar Rangaswamy, Vahid Tarokh

cs.CV updates on arXiv.org arxiv.org

Catalyzed by the recent emergence of site-specific, high-fidelity radio
frequency (RF) modeling and simulation tools purposed for radar, data-driven
formulations of classical methods 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 classical methods. In this regard,
as part of our ongoing data-driven approach to radar space-time adaptive
processing (STAP), we analyze the asymptotic performance guarantees of select
subspace separation methods in the context …

arxiv data data-driven radar

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