Near-Field Tomographic Imaging and Coordinate Reconstruction Algorithm for Uplink Communication

YIN Lannuo; WANG Yong

doi:10.11999/JEIT250715

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YIN Lannuo, WANG Yong. Near-Field Tomographic Imaging and Coordinate Reconstruction Algorithm for Uplink Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250715

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YIN Lannuo, WANG Yong. Near-Field Tomographic Imaging and Coordinate Reconstruction Algorithm for Uplink Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250715

YIN Lannuo, WANG Yong. Near-Field Tomographic Imaging and Coordinate Reconstruction Algorithm for Uplink Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250715

Citation:

YIN Lannuo, WANG Yong. Near-Field Tomographic Imaging and Coordinate Reconstruction Algorithm for Uplink Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250715

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Near-Field Tomographic Imaging and Coordinate Reconstruction Algorithm for Uplink Communication

doi: 10.11999/JEIT250715 cstr: 32379.14.JEIT250715

YIN Lannuo,
WANG Yong^,

Harbin Institute of Technology, Harbin 150001, China

Funds: The National Science Fund for Distinguished Young Scholars(62325104)

Received Date: 2025-07-31
Accepted Date: 2026-04-08
Rev Recd Date: 2026-04-08

Available Online: 2026-04-26

Abstract

Abstract

Objective With the rapid development of 6G networks, communication systems are moving toward high bandwidth, low latency, and massive connectivity. In this context, Integrated Sensing and Communication (ISAC) allows wireless signals to transmit information and sense the environment. It provides a basis for more intelligent and efficient 6G services. Environmental reconstruction is a core task in ISAC systems. It aims to recover the true spatial structure of targets and scenes from echo signals. However, practical environmental reconstruction still faces three main challenges. First, dense Base Station (BS) deployment in 6G communication systems places building targets in the near-field region of the imaging system. This causes strong coupling among the range, azimuth, and elevation dimensions in tomographic imaging, resulting in clear deviations between the reconstructed target geometry and its true shape. Second, the positioning error of User Equipment (UE) is much larger than the signal wavelength. Therefore, conventional Synthetic Aperture Radar (SAR) autofocus algorithms become ineffective, and new methods are needed to avoid the effects of positioning errors. Third, conventional Tomographic Synthetic Aperture Radar (TomoSAR) algorithms use a per-channel processing framework, in which Single-Look Complex (SLC) images are generated independently for each channel. When Inverse Synthetic Aperture Radar (ISAR) techniques are used to generate SLC images, data differences among channels cause inconsistent translational phase compensation. These inconsistencies introduce inter-channel phase errors during elevation focusing and produce false targets in the imaging results. Methods This paper first applies the nonparametric translational motion compensation method developed for ISAR imaging to SLC image generation. This strategy avoids the adverse effects caused by UE positioning errors. Existing ISAR studies usually assume that the target satisfies a turntable model. However, the actual SAR imaging geometry differs from this ideal assumption. Therefore, the mathematical mapping between the TomoSAR imaging results and the true spatial coordinates of the target is rederived under the SAR imaging geometry. Based on this mapping, the coordinate reconstruction problem is formulated as a system of nonlinear equations. A coordinate reconstruction method based on Particle Swarm Optimization (PSO) is then proposed to accurately recover the true geometric shape of the target. In addition, to address inconsistent translational phase compensation among channels in conventional per-channel processing frameworks, a joint phase correction TomoSAR framework is designed. A unified phase correction strategy is used to remove inter-channel phase errors, thereby improving elevation focusing and overall imaging quality. Results and Discussions The proposed methods are verified through simulation experiments on complex building targets under ideal and non-ideal trajectory conditions. Chamfer Distance (CD) is used to evaluate coordinate reconstruction accuracy. The CD values under ideal and non-ideal trajectories are 1.62 and 1.68, respectively, which indicates only slight performance degradation under non-ideal conditions. The imaging point cloud under the non-ideal trajectory shows clear point dropout. A comparison of the empirical Cumulative Distribution Function (CDF) curves of distance errors under the two trajectory conditions shows that the overall trends are similar. Clear differences appear only when the distance error exceeds 2 m. This finding indicates that, under the CD metric, the main differences between the imaging results for ideal and non-ideal trajectories are concentrated in point-cloud dropout regions and areas outside the main target. Thus, non-ideal trajectories mainly affect the scattering-intensity distribution. Comparative experiments between the joint phase correction framework and the conventional framework further show that conventional TomoSAR methods produce clear stacking effects at different elevations, with false targets appearing at incorrect elevation levels. This result suggests that independent translational phase compensation in each channel can induce inter-channel phase errors and seriously degrade elevation focusing. By contrast, joint phase correction improves imaging quality. Conclusions The experimental results verify the effectiveness of the proposed methods. By combining ISAR-based nonparametric translational motion compensation with PSO-based coordinate reconstruction, the true geometric shape of the target is successfully recovered. The joint phase correction strategy also eliminates false targets in elevation focusing caused by conventional per-channel processing. It therefore improves elevation focusing capability and overall image quality.
- 6G,
- ISAC,
- Near-field,
- Bistatic,
- TomoSAR,
- PSO

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References(29)

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