This paper considers multi-view imaging in a sixth-generation (6G) integrated sensing and communication network, which consists of a transmit base-station (BS), multiple receive BSs connected to a central processing unit (CPU), and multiple extended targets. Our goal is to devise an effective multi-view imaging technique that can jointly leverage the targets' echo signals at all the receive BSs to precisely construct the image of these targets. To achieve this goal, we propose a two-phase approach. In Phase I, each receive BS recovers an individual image based on the sample covariance matrix of its received signals. Specifically, we propose a novel covariance-based imaging framework to jointly estimate effective scattering intensity and grid positions, which reduces the number of estimated parameters leveraging channel statistical properties and allows grid adjustment to conform to target geometry. In Phase II, the CPU fuses the individual images of all the receivers to construct a high-quality image of all the targets. Specifically, we design edge-preserving natural neighbor interpolation (EP-NNI) to map individual heterogeneous images onto common and finer grids, and then propose a joint optimization framework to estimate fused scattering intensity and BS fields of view. Extensive numerical results show that the proposed scheme significantly enhances imaging performance, facilitating high-quality environment reconstruction for future 6G networks.

翻译：本文研究第六代（6G）集成感知与通信网络中的多视角成像问题，该网络由一个发射基站（BS）、多个连接至中央处理单元（CPU）的接收基站以及多个扩展目标组成。我们的目标是设计一种有效的多视角成像技术，能够联合利用所有接收基站处目标的回波信号，精确构建这些目标的图像。为实现这一目标，我们提出了一种两阶段方法。在第一阶段，每个接收基站基于其接收信号的样本协方差矩阵恢复出独立的图像。具体而言，我们提出了一种新颖的基于协方差的成像框架，用于联合估计有效散射强度和网格位置；该框架利用信道统计特性减少待估参数数量，并允许调整网格以符合目标几何结构。在第二阶段，CPU融合所有接收机的独立图像，构建所有目标的高质量图像。具体来说，我们设计了边缘保持自然邻域插值（EP-NNI）方法，将异构的独立图像映射到公共且更精细的网格上，进而提出一个联合优化框架来估计融合散射强度与基站视场。大量数值结果表明，所提方案显著提升了成像性能，有助于为未来6G网络实现高质量的环境重建。