_:nbd894d390d0b4fa6b8ec65697213d6f1xb0
<http://schema.org/description>
"Joint radar-communications (JRC) has emerged as a promising technology for\nefficiently using the limited electromagnetic spectrum. In JRC applications\nsuch as secure military receivers, often the radar and communications signals\nare overlaid in the received signal. In these passive listening outposts, the\nsignals and channels of both radar and communications are unknown to the\nreceiver. The ill-posed problem of recovering all signal and channel parameters\nfrom the overlaid signal is terms as dual-blind deconvolution (DBD). In this\nwork, we investigate a more challenging version of DBD with a multi-antenna\nreceiver. We model the radar and communications channels with a few (sparse)\ncontinuous-valued parameters such as time delays, Doppler velocities, and\ndirections-of-arrival (DoAs). To solve this highly ill-posed DBD, we propose to\nminimize the sum of multivariate atomic norms (SoMAN) that depends on the\nunknown parameters. To this end, we devise an exact semidefinite program using\ntheories of positive hyperoctant trigonometric polynomials (PhTP). Our\ntheoretical analyses show that the minimum number of samples and antennas\nrequired for perfect recovery is logarithmically dependent on the maximum of\nthe number of radar targets and communications paths rather than their sum. We\nshow that our approach is easily generalized to include several practical\nissues such as gain/phase errors and additive noise. Numerical experiments show\nthe exact parameter recovery for different JRC"
<https://cdnjs.deepai.org/publication/multi-antenna-dual-blind-deconvolution-for-joint-radar-communications-via-soman-minimization>