基于1比特量化的大规模MIMO雷达系统直接定位算法

张国鑫 易伟 孔令讲

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, 待出版. doi: 10.12000/JR21062
引用本文: 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, 待出版. 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, in press. doi: 10.12000/JR21062
Citation: ZHANG Guoxin, YI Wei, and KONG Lingjiang. Direct position determination for massive MIMO system with one-bit quantization[J]. Journal of Radars, in press. doi: 10.12000/JR21062

基于1比特量化的大规模MIMO雷达系统直接定位算法

doi: 10.12000/JR21062
基金项目: 国家自然科学基金(61771110),长江学者奖励计划(B17008),中央高校基本科研基金(ZYGX2016J031)
详细信息
    作者简介:

    张国鑫(1996–),男,山西大同人。现为电子科技大学信息与通信工程学院博士研究生。研究方向为雷达信号处理及目标定位

    易伟:易 伟(1983–),男,四川雅安人。现为电子科技大学教授,教育部青年长江学者。研究方向为雷达信号处理、微弱目标探测技术、目标跟踪、多传感器数据融合及资源智能管控

    孔令讲(1974–),男,河南南阳人。现为电子科技大学教授,博士生导师,长江学者特聘教授。研究方向为新体制雷达、统计信号处理、优化理论和算法、雷达信号处理、非合作信号处理技术和自适应阵列信号处理

    通讯作者:

    易伟 kusso@uestc.edu.cn

  • 责任主编:王鼎 Corresponding Editor: WANG Ding
  • 中图分类号: TN953

Direct Position Determination for Massive MIMO System with One-bit Quantization

Funds: The National Natural Science Foundation of China (61771110), The Chang Jiang Scholars Program (B17008), The Fundamental Research Funds of Central Universities (ZYGX2016J031)
More Information
  • 摘要: 1比特量化技术在大规模MIMO雷达系统中的应用使得系统成本、功耗及传输带宽显著降低。但这同时也对如何从1比特量化后的数据中提取目标高精度信息提出了严峻挑战。针对基于1比特量化的二次定位算法在低信噪比下定位精度低、鲁棒性差的问题,该文提出了一种基于1比特量化的大规模MIMO雷达系统目标直接定位算法。首先,通过将接收信号进行1比特量化,并推导基于1比特信号的概率分布,建立了关于目标位置的代价函数;其次,通过证明代价函数的凸性,利用梯度下降算法求解了回波中未知的信号参数;最后,根据最大似然估计实现了目标直接定位。仿真实验分析了所提算法的定位性能,结果表明,所提算法仅需传输相较于高精度采样(16比特为例)直接定位算法6.25%的通信带宽,同时其功耗仅为前者的0.1%。此外,与基于1比特量化的二次定位算法相比,所提算法在低信噪比下便可实现对目标位置的有效估计,并且其定位性能在低信噪比和低MIMO天线数量下均明显优于前者。同时,其性能会随着过采样技术的应用进一步提升。

     

  • 图  1  基于广域节点分布的大规模MIMO雷达系统定位场景

    Figure  1.  Location diagram of massive MIMO radar system based on wide-area node distribution

    图  2  1比特数字雷达射频采样前端

    Figure  2.  RF sampling front end of one-bit digital radar

    图  3  1bit-DPD算法流程图

    Figure  3.  Flow chart for 1bit-DPD algorithm

    图  4  不同信噪比下1bit-DPD算法代价函数平面

    Figure  4.  1bit-DPD cost function plane under different SNR

    图  5  不同信噪比下1bit-DPD与1bit-IDP有效估计率曲线

    Figure  5.  Effective estimation rate curves of 1bit-DPD and 1bit-IDP under different SNR

    图  6  不同信噪比下1bit-DPD与1bit-IDP以及16bit-DPD的定位性能对比图

    Figure  6.  Comparison of localization performance of 1bit-DPD, 1bit-IDP and 16bit-DPD under different SNR

    图  7  不同过采样因子v下1bit-DPD和1bit-IDP的定位性能对比图

    Figure  7.  Localization performance of 1bit-DPD and 1bit-IDP under different over-sampling factors v

    图  8  不同天线数量下1bit-DPD和1bit-IDP的定位性能

    Figure  8.  Localization performance of 1bit-DPD and 1bit-IDP under different number of base stations

    表  1  式(25)的梯度下降求解步骤

    Table  1.   The gradient descent solution for Eq. (25)

     初始化:$\widetilde \alpha _{{\rm{mn}}}^0$,$0 < \xi \le 1$,$0 < \zeta \le 1$
     迭代:对于$l = 0 \to {l_{\max }}$
     1:计算代价函数$\Xi \left( {\widetilde \alpha _{{\rm{mn}}}^l} \right)$和$\nabla \left( {\Xi \left( {\widetilde \alpha _{{\rm{mn}}}^l} \right)} \right)$;
     2:初始化步长${u^l} = {u_{{\rm{init}}} }$,更新
       $\widetilde \alpha _{{\rm{mn}}}^{l + 1} = \widetilde \alpha _{{\rm{mn}}}^l - {u^l}\nabla \left( {\Xi \left( {{{\widetilde \alpha }_{{\rm{mn}}}}} \right)} \right)$;
     3:当$\Xi \left( {\widetilde \alpha _{{\rm{mn}}}^{l + 1}} \right) > \Xi \left( {\widetilde \alpha _{{\rm{mn}}}^l} \right) - \xi {u^l}\left\| {\nabla \left( {\Xi \left( {\widetilde \alpha _{{\rm{mn}}}^l} \right)} \right)} \right\|_2^2$,令
       ${u^l} = \zeta {u_{{\rm{init}}} }$
     更新$\widetilde \alpha _{{\rm{mn}}}^{l + 1} = \widetilde \alpha _{{\rm{mn}}}^l - {u^l}\nabla \left( {\Xi \left( {{{\widetilde \alpha }_{{\rm{mn}}}}} \right)} \right)$;
     4:满足停止条件,退出
    下载: 导出CSV

    表  2  数据量及功耗对比

    Table  2.   Comparison of data volume and power consumption

    算法单个采样点数据量功耗
    16bit-DPD2 Byte(实部/虚部)约为几瓦
    1bit-DPD1 bit(实部/虚部)约几毫瓦
    下载: 导出CSV
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  • 收稿日期:  2021-05-13
  • 修回日期:  2021-09-01
  • 网络出版日期:  2021-09-08

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