Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2025  > Proofreading [3rd]
DU Qunfeng, CHENG Xu, WANG Fulai, et al. Moving target detection using distributed MIMO radar based on low-bit quantization under the background of generalized gaussian noise[J]. Journal of Radars, in press. doi: 10.12000/JR25129
Citation: DU Qunfeng, CHENG Xu, WANG Fulai, et al. Moving target detection using distributed MIMO radar based on low-bit quantization under the background of generalized gaussian noise[J]. Journal of Radars, in press. doi: 10.12000/JR25129
shu

Moving Target Detection Using Distributed MIMO Radar Based on Low-bit Quantization Under the Background of Generalized Gaussian noise

DOI: 10.12000/JR25129 CSTR: 32380.14.JR25129
  • 1.

    School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China

  • 2.

    College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds:  The National Natural Science Foundation of China (62401589), Natural Science Foundation of Hubei Province (2024AFB653), Key Project of Scientific Research Program of Hubei Provincial Department of Education (D20241503)
More Information
  • Corresponding author: CHENG Xu, xu.cheng@wit.edu.cn
  • Received Date: 2025-07-21
  • Rev Recd Date: 2025-11-14
    • Available Online: 2025-11-19
    Abstract
  • Signal-level cooperative detection based on multichannel observations is a pivotal technique in distributed Multiple-Input Multiple-Output (MIMO) radar for probing targets via the joint processing of multiple echo channels. However, such cooperative processing imposes substantial demands on computational and communication resources. To address this challenge, moving target detection using distributed MIMO radar with low-bit quantization in the presence of generalized Gaussian noise was investigated herein. In particular, the detectors were designed based on the Generalized Likelihood Ratio Test (GLRT) and the Generalized Rao (G-Rao) test. The maximum likelihood of the target reflection coefficient and Doppler frequency is estimated using the GLRT, whereas the G-Rao test directly constructs statistics based on a score function. These methods avoid redundant parameter searches and effectively reduce computational complexity. A Dynamic Programming (DP) algorithm was used to optimize the quantization threshold and improve the detection performance. Experimental results demonstrate that the G-Rao test is more computationally efficient than the GLRT method. In addition, threshold optimization considerably improves target detection performance compared with a uniform quantization threshold, and DP exhibits lower computational complexity than existing algorithms, such as Particle Swarm Optimization (PSOA).

     

    • FullText(HTML)
  • loading
    • References(37)
  • [1]
    YANG Shixing, YI Wei, and JAKOBSSON A. Multitarget detection strategy for distributed MIMO radar with widely separated antennas[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5113516. doi: 10.1109/TGRS.2022.3175046.
    [2]
    WANG Mingxing, LI Xiaolong, GAO Longji, et al. Signal accumulation method for high-speed maneuvering target detection using airborne coherent MIMO radar[J]. IEEE Transactions on Signal Processing, 2023, 71: 2336–2351. doi: 10.1109/TSP.2023.3286954.
    [3]
    张国鑫, 易伟, 孔令讲. 基于1比特量化的大规模MIMO雷达系统直接定位算法[J]. 雷达学报, 2021, 10(6): 970–981. doi: 10.12000/JR21062.

    ZHANG Guoxin, YI Wei, and KONG Lingjiang. Direct position determination for massive MIMO system with one-bit quantization[J]. Journal of Radars, 2021, 10(6): 970–981. doi: 10.12000/JR21062.
    [4]
    FISHLER E, HAIMOVICH A, BLUM R S, et al. Spatial diversity in radars–models and detection performance[J]. IEEE Transactions on Signal Processing, 2006, 54(3): 823–838. doi: 10.1109/TSP.2005.862813.
    [5]
    KANG B S and LEE K. Ultra-high-resolution spaceborne and squint SAR imaging for height-variant geometry using polynomial range model[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(1): 375–393. doi: 10.1109/TAES.2022.3187383.
    [6]
    DE S and BAZIL RAJ A A. A survey on photonics technologies for radar applications[J]. Journal of Optics, 2023, 52(1): 90–119. doi: 10.1007/s12596-022-00897-x.
    [7]
    HAN K, KANG S, and HONG S. Sub-Nyquist sampling OFDM radar[J]. IEEE Transactions on Radar Systems, 2023, 1: 669–680. doi: 10.1109/TRS.2023.3333430.
    [8]
    JIN Benzhou, ZHU Jiang, WU Qihui, et al. One-bit LFMCW radar: Spectrum analysis and target detection[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(4): 2732–2750. doi: 10.1109/TAES.2020.2978374.
    [9]
    JING Xinchen, SU Hongtao, JIA Congyue, et al. Fusion detection in distributed MIMO radar under hybrid-order Gaussian model[J]. Signal Processing, 2024, 214: 109256. doi: 10.1016/j.sigpro.2023.109256.
    [10]
    WANG Mingxing, LI Xiaolong, ZHANG Zerui, et al. Coherent integration and parameter estimation for high-speed target detection with bistatic MIMO radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5107915. doi: 10.1109/TGRS.2023.3298825.
    [11]
    PARK J, PARK S, KIM D H, et al. Leakage mitigation in heterodyne FMCW radar for small drone detection with stationary point concentration technique[J]. IEEE Transactions on Microwave Theory and Techniques, 2019, 67(3): 1221–1232. doi: 10.1109/TMTT.2018.2889045.
    [12]
    XIAO Yuhang, RAMÍREZ D, SCHREIER P J, et al. One-bit target detection in collocated MIMO radar and performance degradation analysis[J]. IEEE Transactions on Vehicular Technology, 2022, 71(9): 9363–9374. doi: 10.1109/TVT.2022.3178285.
    [13]
    VISWANATHAN R and VARSHNEY P K. Distributed detection with multiple sensors Part I. Fundamentals[J]. Proceedings of the IEEE, 1997, 85(1): 54–63. doi: 10.1109/5.554208.
    [14]
    KAY S M. Fundamentals of Statistical Signal Processing, Volume II: Detection Theory[M]. Hoboken, USA: Prentice Hall, 1998: 1545–1597.
    [15]
    ZAYYANI H, HADDADI F, and KORKI M. One-bit spectrum sensing in cognitive radio sensor networks[J]. Circuits, Systems, and Signal Processing, 2020, 39(5): 2730–2743. doi: 10.1007/s00034-019-01274-z.
    [16]
    YANG Shixing, YI Wei, JAKOBSSON A, et al. Weak signal detection with low-bit quantization in colocated MIMO radar[J]. IEEE Transactions on Signal Processing, 2023, 71: 447–460. doi: 10.1109/TSP.2023.3246233.
    [17]
    XI Feng, XIANG Yijian, ZHANG Zhen, et al. Joint angle and doppler frequency estimation for MIMO radar with one-bit sampling: A maximum likelihood-based method[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(6): 4734–4748. doi: 10.1109/TAES.2020.3000841.
    [18]
    NI Lihua, ZHANG Di, SUN Yimao, et al. Detection and localization of one-bit signal in multiple distributed subarray systems[J]. IEEE Transactions on Signal Processing, 2023, 71: 2776–2791. doi: 10.1109/TSP.2023.3300634.
    [19]
    GAO Fei, GUO Lili, LI Hongbin, et al. Quantizer design for distributed GLRT detection of weak signal in wireless sensor networks[J]. IEEE Transactions on Wireless Communications, 2015, 14(4): 2032–2042. doi: 10.1109/TWC.2014.2379279.
    [20]
    CHENG Xu, CIUONZO D, and ROSSI P S. Multibit decentralized detection through fusing smart and dumb sensors based on Rao test[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(2): 1391–1405. doi: 10.1109/TAES.2019.2936777.
    [21]
    CHENG Xu, CIUONZO D, ROSSI P S, et al. Multi-bit & sequential decentralized detection of a noncooperative moving target through a generalized Rao test[J]. IEEE Transactions on Signal and Information Processing over Networks, 2021, 7: 740–753. doi: 10.1109/TSIPN.2021.3126930.
    [22]
    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.
    [23]
    JING Xinchen, SU Hongtao, LI Ze, et al. Weak moving target detection in distributed MIMO radar with hybrid data[J]. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(6): 9291–9306. doi: 10.1109/TAES.2024.3442777.
    [24]
    LU Jing, ZHOU Shenghua, and PENG Xiaojun. Distributed detection with generalized locally most powerful fusion of compressed local multiframe test statistics[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(5): 5342–5362. doi: 10.1109/TAES.2023.3257823.
    [25]
    ZHANG Qian, VARSHNEY P K, and WESEL R D. Optimal bi-level quantization of i.i.d. sensor observations for binary hypothesis testing[J]. IEEE Transactions on Information Theory, 2002, 48(7): 2105–2111. doi: 10.1109/TIT.2002.1013153.
    [26]
    WANG Zhen, HE Qian, and BLUM R S. Target detection using quantized cloud MIMO radar measurements[J]. IEEE Transactions on Signal Processing, 2022, 70: 1–16. doi: 10.1109/TSP.2021.3129364.
    [27]
    YANG Shixing, LAI Yangming, JAKOBSSON A, et al. Hybrid quantized signal detection with a bandwidth-constrained distributed radar system[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(6): 7835–7850. doi: 10.1109/TAES.2023.3296344.
    [28]
    FAUZI N I H, MUSA Z, and ZULKIFLI N S A. Midrange exploration exploitation searching particle swarm optimization in dynamic environment[C]. The 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM), Pekan, Malaysia, 2021: 649–654. doi: 10.1109/ICSECS52883.2021.00124.
    [29]
    MENG Zhenyu, ZHONG Yuxin, MAO Guojun, et al. PSO-sono: A novel PSO variant for single-objective numerical optimization[J]. Information Sciences, 2022, 586: 176–191. doi: 10.1016/j.ins.2021.11.076.
    [30]
    S. M. Kay Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory[M]. Englewood Cliffs, NJ, USA: Prentice-Hall, Jan. 1998.
    [31]
    DAVIES R B. Hypothesis testing when a nuisance parameter is present only under the alternative[J]. Biometrika, 1987, 74(1): 33–43. doi: 10.1093/biomet/74.1.33.
    [32]
    CIUONZO D, ROSSI P S, and WILLETT P. Generalized Rao test for decentralized detection of an uncooperative target[J]. IEEE Signal Processing Letters, 2017, 24(5): 678–682. doi: 10.1109/LSP.2017.2686377.
    [33]
    CIUONZO D, CHAWLA A, and ROSSI P S. Non-cooperative distributed detection via federated sensor networks[C]. The 2023 IEEE Radar Conference (RadarConf23), San Antonio, USA, 2023: 1–6. doi: 10.1109/RadarConf2351548.2023.10149573.
    [34]
    LIU Weijian, LIU Jun, HAO Chengpeng, et al. Multichannel adaptive signal detection: Basic theory and literature review[J]. Science China Information Sciences, 2022, 65(2): 121301. doi: 10.1007/s11432-020-3211-8.
    [35]
    CUI Guolong, LI Hongbin, and RANGASWAMY M. MIMO radar waveform design with constant modulus and similarity constraints[J]. IEEE Transactions on Signal Processing, 2014, 62(2): 343–353. doi: 10.1109/TSP.2013.2288086.
    [36]
    STORN R and PRICE K. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4): 341–359. doi: 10.1023/A:1008202821328.
    [37]
    MIRJALILI S, MIRJALILI S M, and LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46–61. doi: 10.1016/j.advengsoft.2013.12.007.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(39) PDF downloads(0) Cited by()
    Proportional views
    Related

    京ICP备20021838号-14   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return