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.

翻译：我们认为,一般的光谱共存情景,即接收器不知道雷达和通信系统的频道和信号,在接收器中,雷达和通信系统的频道和信号的传输都不为人知。在这种“光谱变异”问题中,一个普通接收器承认多载载无线通信信号,该信号与雷达信号反射的多个目标覆盖在一起。当雷达接收器没有与发射器合用同一地点时,例如在被动或多静态雷达中,除了目标参数之外,传送的信号也是未知的。同样,除了传送的信息之外,通信频道也可能在诸如电视网络等动态环境中不为人知。因此,对DBD情景中未知的目标和通信参数的估计是极具挑战性的。在这项工作中,我们利用该频道的广度来解析DBD,将其作为一个原子规范的最大限度最小化问题。我们的理论分析和数字实验表明,对连续定值的射程时间和多普勒速度的多个目标以及使用双视接收器统一空间时间样本的延迟多普勒通信频道参数进行完美恢复。