Near-field tomographic imaging for uplink communication and coordinate reconstruction algorithm

YIN Lannuo; WANG Yong

doi:10.11999/JEIT250715

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2026 >

YIN Lannuo, WANG Yong. Near-field tomographic imaging for uplink communication and coordinate reconstruction algorithm[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250715

Citation:

YIN Lannuo, WANG Yong. Near-field tomographic imaging for uplink communication and coordinate reconstruction algorithm[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250715

YIN Lannuo, WANG Yong. Near-field tomographic imaging for uplink communication and coordinate reconstruction algorithm[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250715

Citation:

YIN Lannuo, WANG Yong. Near-field tomographic imaging for uplink communication and coordinate reconstruction algorithm[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250715

PDF( 7317 KB)

Near-field tomographic imaging for uplink communication and coordinate reconstruction algorithm

doi: 10.11999/JEIT250715 cstr: 32379.14.JEIT250715

YIN Lannuo,
WANG Yong^,

Harbin Institute of Technology, Harbin, 150001, China

Funds: National Science Fund for Distinguished Young Scholars under grant 62325104

Accepted Date: 2026-04-08
Rev Recd Date: 2026-04-08

Available Online: 2026-04-26

Abstract

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.
- 6G,
- ISAC,
- XX,
- bistatic,
- TomoSAR,
- PSO

FullText(HTML)

References(29)

References

[1]	LIU Fan, CUI Yuanhao, MASOUROS C, et al. Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond[J]. IEEE Journal on Selected Areas in Communications, 2022, 40(6): 1728–1767. doi: 10.1109/JSAC.2022.3156632.
[2]	CHENG Xiang, DUAN Dongliang, GAO Shijian, et al. Integrated sensing and communications (ISAC) for vehicular communication networks (VCN)[J]. IEEE Internet of Things Journal, 2022, 9(23): 23441–23451. doi: 10.1109/JIOT.2022.3191386.
[3]	ZHANG J A, LIU Fan, MASOUROS C, et al. An overview of signal processing techniques for joint communication and radar sensing[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(6): 1295–1315. doi: 10.1109/JSTSP.2021.3113120.
[4]	WANG Chengxiang, YOU Xiaohu, GAO Xiqi, et al. On the road to 6G: Visions, requirements, key technologies, and testbeds[J]. IEEE Communications Surveys & Tutorials, 2023, 25(2): 905–974. doi: 10.1109/COMST.2023.3249835.
[5]	GONZÁLEZ-PRELCIC N, TAGLIAFERRI D, KESKIN M F, et al. Six integration avenues for ISAC in 6G and beyond[J]. IEEE Vehicular Technology Magazine, 2025, 20(1): 18–39. doi: 10.1109/MVT.2025.3529403.
[6]	JIANG Wei, HAN Bin, HABIBI M A, et al. The road towards 6G: A comprehensive survey[J]. IEEE Open Journal of the Communications Society, 2021, 2: 334–366. doi: 10.1109/OJCOMS.2021.3057679.
[7]	MENG Kaitao, WU Qingqing, MA Shaodan, et al. Throughput maximization for UAV-enabled integrated periodic sensing and communication[J]. IEEE Transactions on Wireless Communications, 2023, 22(1): 671–687. doi: 10.1109/TWC.2022.3197623.
[8]	BAMLER R. A comparison of range-Doppler and wavenumber domain SAR focusing algorithms[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(4): 706–713. doi: 10.1109/36.158864.
[9]	DESAI M D and JENKINS W K. Convolution backprojection image reconstruction for spotlight mode synthetic aperture radar[J]. IEEE Transactions on Image Processing, 1992, 1(4): 505–517. doi: 10.1109/83.199920.
[10]	RANEY R K, RUNGE H, BAMLER R, et al. Precision SAR processing using chirp scaling[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(4): 786–799. doi: 10.1109/36.298008.
[11]	JIANG Changhui, CHEN Yuwei, CHEN Chen, et al. Smartphone PDR/GNSS integration via factor graph optimization for pedestrian navigation[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 8504112. doi: 10.1109/TIM.2022.3186082.
[12]	SAYED A H, TARIGHAT A, and KHAJEHNOURI N. Network-based wireless location: Challenges faced in developing techniques for accurate wireless location information[J]. IEEE Signal Processing Magazine, 2005, 22(4): 24–40. doi: 10.1109/MSP.2005.1458275.
[13]	YIN Lu, NI Qiang, and DENG Zhongliang. A GNSS/5G integrated positioning methodology in D2D communication networks[J]. IEEE Journal on Selected Areas in Communications, 2018, 36(2): 351–362. doi: 10.1109/JSAC.2018.2804223.
[14]	CALLOWAY T M and DONOHOE G W. Subaperture autofocus for synthetic aperture radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(2): 617–621. doi: 10.1109/7.272285.
[15]	EICHEL P H and JAKOWATZ C V. Phase-gradient algorithm as an optimal estimator of the phase derivative[J]. Optics Letters, 1989, 14(20): 1101–1103. doi: 10.1364/OL.14.001101.
[16]	WAHL D E, EICHEL P H, GHIGLIA D C, et al. Phase gradient autofocus-a robust tool for high resolution SAR phase correction[J]. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(3): 827–835. doi: 10.1109/7.303752.
[17]	ASH J N. An autofocus method for backprojection imagery in synthetic aperture radar[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(1): 104–108. doi: 10.1109/LGRS.2011.2161456.
[18]	HU Kebin, ZHANG Xiaoling, HE Shufeng, et al. A less-memory and high-efficiency autofocus back projection algorithm for SAR imaging[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(4): 890–894. doi: 10.1109/LGRS.2014.2365612.
[19]	ZHANG Tao, LIAO Guisheng, LI Yachao, et al. An improved time-domain autofocus method based on 3-D motion errors estimation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5223816. doi: 10.1109/TGRS.2021.3137422.
[20]	李浩林, 陈露露, 张磊, 等. 快速分解后向投影SAR成像的自聚焦算法研究[J]. 电子与信息学报, 2014, 36(4): 938–945. doi: 10.3724/SP.J.1146.2013.00011. LI Haolin, CHEN Lulu, ZHANG Lei, et al. Study of autofocus method for SAR imagery created by fast factorized backprojection[J]. Journal of Electronics & Information Technology, 2014, 36(4): 938–945. doi: 10.3724/SP.J.1146.2013.00011.
[21]	ZHANG Tao, LIAO Guisheng, LI Yachao, et al. A two-stage time-domain autofocus method based on generalized sharpness metrics and AFBP[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5205413. doi: 10.1109/TGRS.2021.3068789.
[22]	LOU Yishan, LIN Hao, LI Ning, et al. A prior 2-D autofocus algorithm with ground cartesian BP imaging for curved trajectory SAR[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 2422–2436. doi: 10.1109/JSTARS.2023.3346942.
[23]	LIU Yanqi, FAN Jixia, TAO Manyi, et al. Modified time-domain backprojection algorithm for SAR frequency-domain autofocus[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 5208116. doi: 10.1109/TGRS.2025.3547912.
[24]	AUSHERMAN D A, KOZMA A, WALKER J L, et al. Developments in radar imaging[J]. IEEE Transactions on Aerospace and Electronic Systems, 1984, AES-20(4): 363–400. doi: 10.1109/TAES.1984.4502060.
[25]	庞怡杰, 王国林, 许荣庆. 一种改进的ISAR运动补偿方法[J]. 系统工程与电子技术, 1998(6): 39–43. PANG Yijie, WANG Guolin, and XU Rongqing. An improved method of motion compensation for ISAR[J]. Systems Engineering and Electronics, 1998(6): 39–43.
[26]	朱兆达, 邱晓晖, 佘志舜. 用改进的多普勒中心跟踪法进行ISAR运动补偿[J]. 电子学报, 1997, 25(3): 65–69. ZHU Zhaoda, QIU Xiaohui, and SHE Zhishun. ISAR motion compensation using modified Doppler centroid tracking method[J]. Acta Electronica Sinica, 1997, 25(3): 65–69.
[27]	CAI T T and WANG Lie. Orthogonal matching pursuit for sparse signal recovery with noise[J]. IEEE Transactions on Information Theory, 2011, 57(7): 4680–4688. doi: 10.1109/TIT.2011.2146090.
[28]	PATI Y C, REZAIIFAR R, and KRISHNAPRASAD P S. Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition[C]. Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, United States, 1993: 40–44. doi: 10.1109/ACSSC.1993.342465.
[29]	YIN Lannuo and WANG Yong. Tomographic bistatic 3-D imaging and coordinate reconstruction method based on uplink communication process[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 5208316. doi: 10.1109/TGRS.2025.3556011.