Integrated sensing and communications (ISAC) systems have gained significant interest because of their ability to jointly and efficiently access, utilize, and manage the scarce electromagnetic spectrum. The co-existence approach toward ISAC focuses on the receiver processing of overlaid radar and communications signals coming from independent transmitters. A specific ISAC coexistence problem is dual-blind deconvolution (DBD), wherein the transmit signals and channels of both radar and communications are unknown to the receiver. Prior DBD works ignore the evolution of the signal model over time. In this work, we consider a dynamic DBD scenario using a linear state space model (LSSM) such that, apart from the transmit signals and channels of both systems, the LSSM parameters are also unknown. We employ a factor graph representation to model these unknown variables. We avoid the conventional matrix inversion approach to estimate the unknown variables by using an efficient expectation-maximization algorithm, where each iteration employs a Gaussian message passing over the factor graph structure. Numerical experiments demonstrate the accurate estimation of radar and communications channels, including in the presence of noise.

翻译：集成感知与通信系统因其能够联合高效地访问、利用和管理稀缺电磁频谱而备受关注。面向ISAC的共存方法聚焦于来自独立发射机的重叠雷达与通信信号在接收端的处理技术。一个特定的ISAC共存问题是双盲解卷积，其中雷达和通信系统的发射信号及信道对接收机均未知。现有双盲解卷积研究忽略了信号模型随时间的演化。本文采用线性状态空间模型构建动态双盲解卷积场景，使得除双系统发射信号与信道外，线性状态空间模型参数同样未知。我们利用因子图表示对这些未知变量进行建模。通过采用高效的期望最大化算法，每次迭代基于因子图结构执行高斯消息传递，避免了传统矩阵求逆方法对未知变量的估计。数值实验表明，本文方法能够在含噪环境下实现对雷达与通信信道的精确估计。