动平台分布式雷达系统动目标低比特数据检测算法

杨诗兴 张国鑫 梁雲飞 易伟 孔令讲

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), 待出版. doi: 10.12000/JR23240
引用本文: 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), 待出版. 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, in press. doi: 10.12000/JR23240
Citation: 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, in press. doi: 10.12000/JR23240

动平台分布式雷达系统动目标低比特数据检测算法

doi: 10.12000/JR23240
基金项目: 国家自然科学基金(62231008, U19B2017),中央高校基本科研业务费专项资金(ZYGX2020ZB029)
详细信息
    作者简介:

    杨诗兴,博士,研究方向为统计信号处理、多雷达协同探测与参数估计等

    张国鑫,博士生,研究方向为目标定位与雷达信号处理等

    梁雲飞,硕士生,研究方向为复杂环境目标定位与雷达信号处理等

    易 伟,博士,教授,研究方向为低可观测目标检测跟踪、多雷达协同探测等

    孔令讲,博士,教授,研究方向为雷达信号处理、新体制雷达、统计信号处理等

    通讯作者:

    易伟 kusso@uestc.edu.cn

  • 责任主编:李杨 Corresponding Editor: LI Yang
  • 11) 为了文章的表达简洁直观,此处仅考虑匀速直线运动,其余运动方式的目标可对相应算法进行简单扩展后适配。2) 该技术同时被称为去斜处理(deramp processing)、接收去斜(deramp on receive)、解线性调频(dechirp)或单路处理(one-pass processing)。
  • 中图分类号: TN95

Moving Targets Detection with Low-bit Quantization in Distributed Radar on Moving Platforms

Funds: The National Natural Science Foundation of China (62231008, U19B2017), The Fundamental Research Funds for the Central Universities (ZYGX2020ZB029)
More Information
  • 摘要: 动平台分布式雷达系统可有效提升系统的生存能力和探测性能,但运动平台之间通常采用无线传输方式,难以配备大通信带宽以传输完整的信号数据,给雷达系统的高性能检测带来极大挑战。由于低比特量化技术可显著降低分布式系统的通信传输代价和计算资源消耗,该文针对低信噪比弱信号环境下提出了动平台分布式雷达系统的低比特量化运动目标检测算法。首先,根据系统资源将各节点的多脉冲观测数据选择对应位数的低比特量化器进行量化,推导了关于量化器和多个目标状态的似然函数。其次,证明了低比特量化数据对应似然函数关于未知目标反射系数的凸性,并基于该特性设计了多普勒频移和反射系数的联合估计器。然后,针对探测区域中存在的多个状态未知目标设计了多目标检测器,推导了其恒虚警率门限。最后,通过推导系统的渐近性能设计了最优低比特量化器,在保证系统鲁棒性的同时有效提升了系统的检测性能。仿真实验分析了所提算法的检测与估计性能,结果证明了所提算法在低信噪比弱信号环境下的有效性,同时表明低比特量化数据可在仅占用低于20%通信带宽的基础上实现接近高精度(16比特量化)数据对应的检测和估计性能,且2比特量化策略可作为检测性能和雷达系统资源消耗的折中选择。

     

  • 图  1  动平台分布式雷达系统相较于传统固定节点的性能得益示意

    Figure  1.  Illustration of the performance benefits of a distributed radar system adopting moving platforms compared to the fixed nodes

    图  2  分布式雷达系统低比特量化示意图

    Figure  2.  Schematic of low-bit quantization for distributed radar systems

    图  3  观测数据3比特量化示意图

    Figure  3.  Schematic of 3-bit quantization of observational data

    图  4  1比特、2比特和3比特量化器对应的最优量化门限

    Figure  4.  Optimal quantization levels for the 1-bit, 2-bit, and 3-bit quantizers

    图  5  仿真场景示意(箭头表征雷达节点或者目标运动速度)

    Figure  5.  An illustration of the simulation scenario (arrows represent the velocities of nodes or targets)

    图  6  观测数据量化示意

    Figure  6.  The quantization of the observation data

    图  7  不同量化位数对应各栅格的检测统计量

    Figure  7.  The test statistic for each grid cell with different quantization bits

    图  8  不同量化位数对应的系统性能

    Figure  8.  System performance corresponding to different quantization bits

    图  9  不同SNR下的ROC曲线

    Figure  9.  ROC curves for different SNRs

    图  10  系统针对不同反射强度目标的检测和估计性能

    Figure  10.  Detection and estimation performance of the system for the targets with different power

    1  基于BGDA的联合估计器设计

    1.   BGDA-based joint estimator design

     输入:量化数据$ \widetilde {\boldsymbol{Y}}_l^{\left( q \right)} $,初始状态${\widetilde {\boldsymbol{\alpha}} _{l\mathcal{G}}}\left( 0 \right) = {\left[ {\widetilde \alpha _{l\mathcal{G}}^{\text{R}},\widetilde \alpha _{l\mathcal{G}}^{\text{I}}} \right]^{\rm T} }$,学习率${\beta _l}$,容忍精度${\eta _l}$。
     输出:l个通道的检测统计量$ {\varLambda _{l\mathcal{G}}}\left( {\widetilde {\boldsymbol{Y}}_l^{\left( q \right)}} \right) $,未知参数的估计$ \left( {\hat f_l^{\text{d}},{{\hat {\boldsymbol{\alpha}} }_{l\mathcal{G}}}} \right) $。
     For $\widetilde f_l^{\text{d}} \in \left[ {f_{\min }^{\text{d}},f_{\max }^{\text{d}}} \right]$
       初始赋值:${\widetilde {\boldsymbol{\alpha }}_{l\mathcal{G}}}\left( {\widetilde f_l^{\text{d}}} \right) \leftarrow {\widetilde {\boldsymbol{\alpha }}_{l\mathcal{G}}}\left( 0 \right)$
       While ${\left\| {\nabla {{\widetilde \ell }_1}\left( {\widetilde {\boldsymbol{Y}}_l^{\left( q \right)};\widetilde f_l^{\text{d}},{{\widetilde {\boldsymbol{\alpha }}}_{l\mathcal{G}}}\left( {\widetilde f_l^{\text{d}}} \right)} \right)} \right\|_2} > {\eta _l}$
         ${\widetilde {\boldsymbol{\alpha }}_{l\mathcal{G}}}\left( {\widetilde f_l^{\text{d}}} \right) \leftarrow {\widetilde {\boldsymbol{\alpha }}_{l\mathcal{G}}}\left( {\widetilde f_l^{\text{d}}} \right) + \beta _l^{\rm T} \nabla {\widetilde \ell _1}\left( {\widetilde {\boldsymbol{Y}}_l^{\left( q \right)};\widetilde f_l^{\text{d}},{{\widetilde {\boldsymbol{\alpha }}}_{l\mathcal{G}}}\left( {\widetilde f_l^{\text{d}}} \right)} \right)$
       end
       $ {\varLambda _{l\mathcal{G}}}\left( {\widetilde {\boldsymbol{Y}}_l^{\left( q \right)};\widetilde f_l^{\text{d}},{{\widetilde {\boldsymbol{\alpha }}}_{l\mathcal{G}}}\left( {\widetilde f_l^{\text{d}}} \right)} \right) = {\widetilde \ell _1}\left( {\widetilde {\boldsymbol{Y}}_l^{\left( q \right)};\widetilde f_l^{\text{d}},{{\widetilde {\boldsymbol{\alpha }}}_{l\mathcal{G}}}\left( {\widetilde f_l^{\text{d}}} \right)} \right) $
     end
     $ \left\{ \begin{gathered} \left( {\hat f_l^{\text{d}},{{\hat {\boldsymbol{\alpha}} }_{l\mathcal{G}}}} \right) = \mathop {\arg \max }\limits_{\left\{ {\widetilde f_l^{\text{d}},{{\widetilde {\boldsymbol{\alpha }}}_{l\mathcal{G}}}\left( {\widetilde f_l^{\text{d}}} \right)} \right\}} \left\{ {{\varLambda _{l\mathcal{G}}}\left( {\widetilde {\boldsymbol{Y}}_l^{\left( q \right)};\widetilde f_l^{\text{d}},{{\widetilde {\boldsymbol{\alpha }}}_{l\mathcal{G}}}\left( {\widetilde f_l^{\text{d}}} \right)} \right)} \right\} \\ {\varLambda _{l\mathcal{G}}}\left( {{{\boldsymbol{Y}}_l}} \right) = \max \left\{ {{\varLambda _{l\mathcal{G}}}\left( {\widetilde {\boldsymbol{Y}}_l^{\left( q \right)};\widetilde f_l^{\text{d}},{{\widetilde {\boldsymbol{\alpha }}}_{l\mathcal{G}}}\left( {\widetilde f_l^{\text{d}}} \right)} \right)} \right\} \\ \end{gathered} \right. $
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  • 收稿日期:  2023-12-19
  • 修回日期:  2024-02-24
  • 网络出版日期:  2024-03-13

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