Near-field tomographic imaging for uplink communication and coordinate reconstruction algorithm
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摘要: 面对日益增长的高带宽、低时延需求，6G网络技术正迅速发展， ISAC系统逐渐成为热门研究方向。环境重构作为该系统的核心环节，在实际应用中仍面临诸多挑战。本文针对以下三个问题进行了研究：(1) 在6G系统中，基站的密集部署使得建筑目标处于成像系统的近场区域，从而导致距离、方位和高度三个维度严重耦合；(2)由于终端的定位误差远大于信号波长，SAR成像中的自聚焦算法失效；(3)传统层析合成孔径雷达中各通道补偿的平动相位补偿不一致，在高度域聚焦过程中产生虚假目标。本文首先将逆合成孔径雷达成像中的非参数平动补偿方法用于单视复数图像生成，并基于双基SAR的成像场景推导出层析成像结果与目标真实空间坐标之间的数学映射关系，将坐标重构问题建模为非线性方程组，采用粒子群优化算法进行求解，实现对目标真实几何形状的精确恢复；其次，为了解决传统分通道处理中平动补偿不一致的问题，提出了一种联合相位校正的层析成像框架，可有效消除通道间的差异，显著提升了高度维聚焦效果和整体成像质量。仿真实验结果证明了本文算法的有效性。Abstract:
Objective With the rapid evolution of 6G network technology, communication systems are evolving toward high bandwidth, low latency, and massive connectivity. Against this backdrop, integrated sensing and communications (ISAC), as a novel system architecture, enables wireless signals to perform dual functions—transmitting information while simultaneously sensing the environment—thereby providing more intelligent and efficient services for 6G networks. Environmental reconstruction, a core component of ISAC systems, aims to restore the true spatial structure of targets and scenes using echo signals. However, current environmental reconstruction techniques in practical applications still face the following three major challenges: First, in 6G communication systems, the dense deployment of base stations (BS) causes building targets to reside in the near-field region of the imaging system, leading to severe coupling among the range, azimuth, and elevation dimensions in tomographic imaging and resulting in significant discrepancies between the reconstructed target geometry and the actual shape. Second, because the positioning error of user equipment (UE) far exceeds the wavelength used by existing communication systems, traditional SAR imaging autofocus algorithms become ineffective, necessitating the development of new methods to circumvent the issues posed by positioning errors. Finally, conventional TomoSAR algorithms adopt a per-channel processing framework by independently generating SLC images for each channel; however, when each channel employs ISAR techniques to generate SLC images, inherent data discrepancies among the channels result in inconsistent translational compensation, which introduces phase errors during the elevation focusing process and ultimately leads to the occurrence of spurious targets in the imaging outcomes. Methods In this paper, we first propose applying the nonparametric translational compensation method originally developed for ISAR imaging to the generation of single-look complex (SLC) images, thereby effectively circumventing the adverse effects introduced by positioning errors. Existing ISAR-related literature typically assumes that the target adheres to a turntable model, yet the actual SAR imaging geometry diverges significantly from this idealized assumption. Based on the SAR imaging scenario, we have rederived the mathematical mapping that links the ISAR tomographic imaging results to the target’s true spatial coordinates. Leveraging this mapping, we formulate the coordinate reconstruction challenge as a system of nonlinear equations and subsequently propose a novel coordinate reconstruction method that integrates a particle swarm optimization (PSO) algorithm, ultimately achieving an accurate restoration of the target's genuine geometric shape. Furthermore, in order to address the inherent issue of inconsistent translational compensation among channels within traditional per-channel processing frameworks, we have designed a joint phase calibration tomographic imaging algorithm that employs a unified phase calibration strategy to eliminate inter-channel phase discrepancies, thereby markedly improving both the elevation focusing performance and the overall imaging quality. Results and Discussions We validate the proposed methods through simulation experiments on complex building targets under both ideal and non-ideal trajectory conditions, using the CD distance as the evaluation metric for coordinate reconstruction accuracy. The experimental results demonstrate that the CD distances under ideal and non-ideal trajectories are 1.34 and 1.54, respectively, indicating only a slight performance degradation under non-ideal conditions. Notably, imaging point clouds obtained under non-ideal trajectories exhibit evident point dropout. A comparative analysis of the cumulative probability distribution curves of distance errors under the two trajectory conditions reveals that the overall distribution trends are very similar; significant differences in the probability distributions emerge only when the distance error exceeds 2 m. This observation indicates that, in terms of the CD distance evaluation metric, the primary discrepancies between imaging results obtained under ideal and non-ideal trajectories are concentrated in regions exhibiting point cloud dropout and in areas outside the main target. Hence, the influence of non-ideal trajectories is mainly manifested in the variation of scattering intensity distribution. Moreover, comparative experiments between the joint phase calibration framework and traditional algorithm frameworks show that conventional tomographic imaging methods exhibit marked stacking effects at different elevations, with false targets appearing at incorrect elevation levels. This behavior suggests that independently compensating for translational motion in each channel is prone to inducing inter-channel phase discrepancies, thereby severely impairing elevation focusing performance. In contrast, the incorporation of joint phase calibration yields a substantial improvement in imaging quality. Conclusions The experimental results validate the effectiveness of the proposed methods: by adopting the ISAR nonparametric translational compensation and the PSO-based coordinate reconstruction techniques, the true geometric shape of the target is successfully recovered. Moreover, the joint phase calibration strategy effectively eliminates the issue of false targets in elevation focusing that arises from conventional per-channel processing, thereby significantly enhancing both the elevation focusing capability and the overall image quality. -
表 1 通信系统参数和对应的成像参数
参数 数值 资源块数 272 一个资源块含有的子载波数 12 载频 5 GHz 子载波间隔 120 kHz SRS符号的数量 1 带宽 391.68 MHz 采样时间 1.017 ns PRF 200 Hz PRT 40 slots=5 subframes 波长 0.06 m 频域梳状结构 2 循环移位 正常 观测时间 0.5 s 脉宽 $ {8.93}_{}\text{μs} $ 
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