论文标题
多维双盲反卷积方法
Multi-dimensional dual-blind deconvolution approach toward joint radar-communications
论文作者
论文摘要
在共存的情况下,我们考虑了一个联合多 - 安特纳纳雷达沟通系统。与常规应用相反,其中至少雷达波形和通信通道是已知或估计的\ textit {a先验},我们调查了两个系统的通道和传输信号尚不清楚的情况。在雷达应用中,此问题是在未知传输信号的多主管或被动系统中产生的。同样,高度动态的车辆或移动通信可能会使无线通道的先前估计无助。特别是,雷达信号反射多个目标被多载波通信信号覆盖。为了提取未知的连续价值目标参数(范围,多普勒速度和到达方向)和通信消息,我们将问题作为稀疏的双盲反卷积提出,并使用原子规范最小化来解决它。数值实验验证了我们提出的方法,并表明对连续值的通道参数,雷达波形和通信消息的精确估计是可能以缩放歧义的。
We consider a joint multiple-antenna radar-communications system in a co-existence scenario. Contrary to conventional applications, wherein at least the radar waveform and communications channel are known or estimated \textit{a priori}, we investigate the case when the channels and transmit signals of both systems are unknown. In radar applications, this problem arises in multistatic or passive systems, where transmit signal is not known. Similarly, highly dynamic vehicular or mobile communications may render prior estimates of wireless channel unhelpful. In particular, the radar signal reflected-off multiple targets is overlaid with the multi-carrier communications signal. In order to extract the unknown continuous-valued target parameters (range, Doppler velocity, and direction-of-arrival) and communications messages, we formulate the problem as a sparse dual-blind deconvolution and solve it using atomic norm minimization. Numerical experiments validate our proposed approach and show that precise estimation of continuous-valued channel parameters, radar waveform, and communications messages is possible up to scaling ambiguities.