  We consider a general spectral coexistence scenario, wherein the channels and
transmit signals of both radar and communications systems are unknown at the
receiver. In this \textit{dual-blind deconvolution} (DBD) problem, a common
receiver admits the multi-carrier wireless communications signal that is
overlaid with the radar signal reflected-off multiple targets. When the radar
receiver is not collocated with the transmitter, such as in passive or
multistatic radars, the transmitted signal is also unknown apart from the
target parameters. Similarly, apart from the transmitted messages, the
communications channel may also be unknown in dynamic environments such as
vehicular networks. As a result, the estimation of unknown target and
communications parameters in a DBD scenario is highly challenging. In this
work, we exploit the sparsity of the channel to solve DBD by casting it as an
atomic norm minimization problem. Our theoretical analyses and numerical
experiments demonstrate perfect recovery of continuous-valued range-time and
Doppler velocities of multiple targets as well as delay-Doppler communications
channel parameters using uniformly-spaced time samples in the dual-blind
receiver.
