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CHENG Ziyang, TAN Siyuan, RAN Changkun, et al. Over-the-air computation aided joint beamforming design for distributed radar[J]. Journal of Radars, in press. doi: 10.12000/JR26055
Citation: CHENG Ziyang, TAN Siyuan, RAN Changkun, et al. Over-the-air computation aided joint beamforming design for distributed radar[J]. Journal of Radars, in press. doi: 10.12000/JR26055
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Over-the-air Computation Aided Joint Beamforming Design for Distributed Radar

DOI: 10.12000/JR26055 CSTR: 32380.14.JR26055

  • School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds:  The National Natural Science Foundation of China (62371096), Open Research Project of the State Key Laboratory of Industrial Control Technology, China (ICT2026B59)
More Information
  • Corresponding author: CHENG Ziyang, zycheng@uestc.edu.cn
  • Received Date: 2026-03-09
  • Rev Recd Date: 2026-09-04
    • Available Online: 2026-09-08
    Abstract
  • Aiming at the problems of high data backhaul cost and low efficiency in the traditional Distributed Radar Network (DRN), this paper investigates a novel architecture of distributed radar networks assisted by Over-the-Air Computation (AirComp), so as to improve the performance of cooperative target detection and the efficiency of data fusion. Firstly, a cooperative detector applicable to distributed radar networks is derived at the sensing level, which reveals that its detection statistic inherently takes a summation form and is highly mathematically consistent with the linear superposition mechanism of AirComp. On the basis of this inherent correlation, a joint beamforming design method is proposed for AirComp-assisted distributed radar networks, which simultaneously optimizes the radar transmit beamforming matrix, the receive filter, and the Hybrid Beamforming (HBF) receiver at the Fusion Center (FC). Furthermore, an efficient solution algorithm based on alternating optimization is devised. Simulation results demonstrate that, under the same Signal to Interference-plus-Noise Ratio (SINR) and false alarm probability, the proposed AirComp-aided Distributed Radar Network (AirDRN) architecture achieves superior target detection performance compared with the conventional Time Division Duplex (TDD) mode. Specifically, when the SINR is 7 dB and 10 dB, respectively, the detection performance is improved by approximately 3.17 and 1.52 times.

     

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    • References(31)
  • [1]
    GOGINENI S and NEHORAI A. Polarimetric MIMO radar with distributed antennas for target detection[J]. IEEE Transactions on Signal Processing, 2010, 58(3): 1689–1697. doi: 10.1109/TSP.2009.2036472.
    [2]
    HAIMOVICH A M, BLUM R S, and CIMINI L J. MIMO radar with widely separated antennas[J]. IEEE Signal Processing Magazine, 2008, 25(1): 116–129. doi: 10.1109/MSP.2008.4408448.
    [3]
    程子扬, 何子述, 王智磊, 等. 分布式MIMO雷达目标检测性能分析[J]. 雷达学报, 2017, 6(1): 81–89. doi: 10.12000/JR16147.

    CHENG Ziyang, HE Zishu, WANG Zhilei, et al. Detection performance analysis for distributed MIMO radar[J]. Journal of Radars, 2017, 6(1): 81–89. doi: 10.12000/JR16147.
    [4]
    何子述, 程子扬, 李军, 等. 集中式MIMO雷达研究综述[J]. 雷达学报, 2022, 11(5): 805–829. doi: 10.12000/JR22128.

    HE Zishu, CHENG Ziyang, LI Jun, et al. A survey of collocated MIMO radar[J]. Journal of Radars, 2022, 11(5): 805–829. doi: 10.12000/JR22128.
    [5]
    MENG Kaitao, MASOUROS C, PETROPULU A P, et al. Cooperative ISAC networks: Opportunities and challenges[J]. IEEE Wireless Communications, 2025, 32(3): 212–219. doi: 10.1109/MWC.008.2400151.
    [6]
    SHI Chenguang, WANG Yijie, SALOUS S, et al. Joint transmit resource management and waveform selection strategy for target tracking in distributed phased array radar network[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(4): 2762–2778. doi: 10.1109/TAES.2021.3138869.
    [7]
    杨诗兴, 张国鑫, 梁雲飞, 等. 动平台分布式雷达系统动目标低比特数据检测算法[J]. 雷达学报(中英文), 2024, 13(3): 584–600. doi: 10.12000/JR23240.

    YANG Shixing, ZHANG Guoxin, LIANG Yunfei, et al. Moving targets detection with low-bit quantization in distributed radar on moving platforms[J]. Journal of Radars, 2024, 13(3): 584–600. doi: 10.12000/JR23240.
    [8]
    ASHRAF M, TAN Bo, MOLTCHANOV D, et al. Joint optimization of radar and communications performance in 6G cellular systems[J]. IEEE Transactions on Green Communications and Networking, 2023, 7(1): 522–536. doi: 10.1109/TGCN.2023.3234258.
    [9]
    NAZER B and GASTPAR M. Computation over multiple-access channels[J]. IEEE Transactions on Information Theory, 2007, 53(10): 3498–3516. doi: 10.1109/TIT.2007.904785.
    [10]
    ŞAHIN A and YANG Rui. A survey on over-the-air computation[J]. IEEE Communications Surveys & Tutorials, 2023, 25(3): 1877–1908. doi: 10.1109/COMST.2023.3264649.
    [11]
    曹晓雯, 莫小鹏, 许杰. 面向边缘智能的空中计算[J]. 中兴通讯技术, 2020, 26(4): 31–37. doi: 10.12142/ZTETJ.202004007.

    CAO Xiaowen, MO Xiaopeng, and XU Jie. Over-the-air computation for edge intelligence[J]. ZTE Technology Journal, 2020, 26(4): 31–37. doi: 10.12142/ZTETJ.202004007.
    [12]
    谈玲, 许海, 刘玉风, 等. 基于多无人机的空中计算网络资源分配算法[J]. 电子学报, 2023, 51(11): 3070–3078. doi: 10.12263/DZXB.20230513.

    TAN Ling, XU Hai, LIU Yufeng, et al. Resource allocation algorithm of AirComp network based on multiple UAVs[J]. Acta Electronica Sinica, 2023, 51(11): 3070–3078. doi: 10.12263/DZXB.20230513.
    [13]
    LI Xiaoyang, ZHU Guangxu, GONG Yi, et al. Wirelessly powered data aggregation for IoT via over-the-air function computation: Beamforming and power control[J]. IEEE Transactions on Wireless Communications, 2019, 18(7): 3437–3452. doi: 10.1109/TWC.2019.2914046.
    [14]
    ZHAI Xiongfei, HAN Guojun, CAI Yunlong, et al. Beamforming design based on two-stage stochastic optimization for RIS-assisted over-the-air computation systems[J]. IEEE Internet of Things Journal, 2022, 9(7): 5474–5488. doi: 10.1109/JIOT.2021.3108894.
    [15]
    LI Xiaoyang, LIU Fan, ZHOU Ziqin, et al. Integrated sensing, communication, and computation over-the-air: MIMO beamforming design[J]. IEEE Transactions on Wireless Communications, 2023, 22(8): 5383–5398. doi: 10.1109/TWC.2022.3233795.
    [16]
    DONG Kai, VOROBYOV S A, YU Hao, et al. Beamforming design for integrated sensing, over-the-air computation, and communication in internet of robotic things[J]. IEEE Internet of Things Journal, 2024, 11(20): 32478–32489. doi: 10.1109/JIOT.2024.3433390.
    [17]
    SOHRABI F and YU Wei. Hybrid digital and analog beamforming design for large-scale antenna arrays[J]. IEEE Journal of Selected Topics in Signal Processing, 2016, 10(3): 501–513. doi: 10.1109/JSTSP.2016.2520912.
    [18]
    WANG Zihuan, LI Ming, LIU Qian, et al. Hybrid precoder and combiner design with low-resolution phase shifters in mmWave MIMO systems[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12(2): 256–269. doi: 10.1109/JSTSP.2018.2819129.
    [19]
    WANG Bowen, LI Hongyu, LIU Fan, et al. Cooperative integrated sensing and communication networks: Analysis and distributed design[EB/OL]. arXiv:2407.13401, 2024[2026-09-03]. https://arxiv.org/abs/2407.13401.
    [20]
    DENG Weicao, LI Min, ZHAO Mingmin, et al. CSI transfer from sub-6G to mmWave: Reduced-overhead multi-user hybrid beamforming[J]. IEEE Journal on Selected Areas in Communications, 2025, 43(3): 973–987. doi: 10.1109/JSAC.2025.3536539.
    [21]
    BOYD S, PARIKH N, CHU E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends in Machine Learning, 2011, 3(1): 1–122. doi: 10.1561/2200000016.
    [22]
    CHENG Ziyang, HE Zishu, and LIAO Bin. Hybrid beamforming design for OFDM dual-function radar-communication system[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(6): 1455–1467. doi: 10.1109/JSTSP.2021.3118276.
    [23]
    CHENG Ziyang, HE Zishu, and LIAO Bin. Hybrid beamforming for multi-carrier dual-function radar-communication system[J]. IEEE Transactions on Cognitive Communications and Networking, 2021, 7(3): 1002–1015. doi: 10.1109/TCCN.2021.3063110.
    [24]
    LI Rencang. Rayleigh Quotient Based Optimization Methods for Eigenvalue Problems[M]. BAI Zhaojun, GAO Weiguo, and SU Yangfeng. Matrix Functions and Matrix Equations. Singapore: World Scientific, 2015: 76–108.
    [25]
    BOYD S and VANDENBERGHE L. Convex Optimization[M]. Cambridge: Cambridge University Press, 2004: 127–272.
    [26]
    SUN Ying, BABU P, and PALOMAR D P. Majorization-minimization algorithms in signal processing, communications, and machine learning[J]. IEEE Transactions on Signal Processing, 2017, 65(3): 794–816. doi: 10.1109/TSP.2016.2601299.
    [27]
    GRANT M C and BOYD S P. CVX: Matlab software for disciplined convex programming, version 2.2[EB/OL]. https://cvxr.com/cvx/, 2020.
    [28]
    BOUMAL N. An Introduction to Optimization on Smooth Manifolds[M]. Cambridge: Cambridge University Press, 2023: 51–78.
    [29]
    ABSIL P A, MAHONY R, and SEPULCHRE R. Optimization Algorithms on Matrix Manifolds[M]. Princeton, NJ, USA: Princeton University Press, 2008: 53–90.
    [30]
    UDRIŞTE C. Convex Functions and Optimization Methods on Riemannian Manifolds[M]. Dordrecht, The Netherlands: Springer, 1994: 56–107. doi: 10.1007/978-94-015-8390-9.
    [31]
    CHENG Ziyang and LIAO Bin. QoS-aware hybrid beamforming and DOA estimation in multi-carrier dual-function radar-communication systems[J]. IEEE Journal on Selected Areas in Communications, 2022, 40(6): 1890–1905. doi: 10.1109/JSAC.2022.3155529.
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