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面向射频隐身的组网雷达多目标跟踪下射频辐射资源优化分配算法

时晨光 丁琳涛 汪飞 周建江

时晨光, 丁琳涛, 汪飞, 周建江. 面向射频隐身的组网雷达多目标跟踪下射频辐射资源优化分配算法[J]. 电子与信息学报, 2021, 43(3): 539-546. doi: 10.11999/JEIT200636
引用本文: 时晨光, 丁琳涛, 汪飞, 周建江. 面向射频隐身的组网雷达多目标跟踪下射频辐射资源优化分配算法[J]. 电子与信息学报, 2021, 43(3): 539-546. doi: 10.11999/JEIT200636
Chenguang SHI, Lintao DING, Fei WANG, Jianjiang ZHOU. Radio Frequency Stealth-based Optimal Radio Frequency Resource Allocation Algorithm for Multiple-target Tracking in Radar Network[J]. Journal of Electronics and Information Technology, 2021, 43(3): 539-546. doi: 10.11999/JEIT200636
Citation: Chenguang SHI, Lintao DING, Fei WANG, Jianjiang ZHOU. Radio Frequency Stealth-based Optimal Radio Frequency Resource Allocation Algorithm for Multiple-target Tracking in Radar Network[J]. Journal of Electronics and Information Technology, 2021, 43(3): 539-546. doi: 10.11999/JEIT200636

面向射频隐身的组网雷达多目标跟踪下射频辐射资源优化分配算法

doi: 10.11999/JEIT200636
基金项目: 国家自然科学基金(61801212),国防科技创新特区资助,中国博士后科学基金面上项目(2019M650113),江苏省自然科学基金青年基金项目(BK20180423)
详细信息
    作者简介:

    时晨光:男,1989年生,副教授,博士,研究方向为飞行器射频隐身技术,组网雷达资源管理,多平台传感器协同等

    丁琳涛:男,1997年生,硕士生,研究方向为组网雷达资源管理

    汪飞:男,1976年生,副教授,博士,研究方向为飞行器射频隐身技术、雷达信号处理、阵列信号处理等

    周建江:男,1962年生,教授,博士,研究方向为飞行器射频隐身技术、雷达目标特性分析、航空电子信息系统设计等

    通讯作者:

    时晨光 scg_space@163.com

  • 中图分类号: TN953

Radio Frequency Stealth-based Optimal Radio Frequency Resource Allocation Algorithm for Multiple-target Tracking in Radar Network

Funds: The National Natural Science Foundation of China (61801212), The National Defense Science and Technology Innovation Special Zones, China Postdoctoral Science Foundation (2019M650113), The Natural Science Foundation of Jiangsu Province (BK20180423)
  • 摘要: 针对组网雷达系统多目标跟踪场景,该文提出一种面向射频(RF)隐身的组网雷达射频辐射资源优化分配算法。首先,采用目标跟踪误差的贝叶斯克拉美-罗下界(BCRLB)作为目标跟踪性能指标。其次,以各雷达照射目标的驻留时间资源和辐射功率资源加权和为优化目标,以BCRLB不大于给定目标跟踪精度阈值及系统射频辐射资源作为约束条件,建立了包含雷达节点分配方式、驻留时间和辐射功率3个优化变量的优化模型。然后,采用两步分解法对上述优化模型进行了求解,即先固定雷达节点选择,利用内点法对简化后的非凸非线性优化模型进行求解,之后再通过匈牙利算法确定最佳雷达节点分配方式。仿真结果表明,相较于辐射资源均匀分配算法,所提算法可以有效降低组网雷达的射频资源消耗,提升系统射频隐身性能。
  • 图  1  目标轨迹与雷达组网分布图

    图  2  目标RCS时变模型

    图  3  RCS起伏场景下两种算法目标跟踪RMSE对比

    图  4  RCS起伏场景下各目标的雷达选择及驻留时间优化分配结果

    图  5  RCS起伏场景下各目标的雷达选择及辐射功率优化分配结果

    图  6  RCS起伏场景下两种算法的总驻留时间对比

    图  7  RCS起伏场景下两种算法的总辐射功率对比

    图  8  RCS起伏场景下驻留时间资源节省率

    图  9  RCS起伏场景下辐射功率资源节省率

    图  10  RCS起伏场景下目标函数值减小率

    表  1  固定雷达分配方式的辐射资源优化控制算法

     步骤 1 令${g_1} = {\kern 1pt} F_{k\left| {k - 1} \right.}^s - {F_{\max }}$,${g_2} = {\kern 1pt} {T_{{\rm{d,min}}}} - T_{{\rm{d}},1,k}^s$, ${g_3} = {\kern 1pt} {T_{{\rm{d,min}}}} - T_{{\rm{d}},2,k}^s$,···, ${g_{M + 1}} = {T_{{\rm{d,min}}}} - {\kern 1pt} T_{{\rm{d}},M,k}^s$, ${g_{M + 2}} = {\kern 1pt} T_{{\rm{d}},1,k}^s - {T_{{\rm{d,max}}}}$,
     ${g_{M + 3}} = {\kern 1pt} T_{{\rm{d}},2,k}^s - {T_{{\rm{d,max}}}}$,···, ${g_{2M + 1}} = {\kern 1pt} T_{{\rm{d}},M,k}^s - {T_{{\rm{d,max}}}}$, ${g_{2M + 2}} = {\kern 1pt} {P_{\min }} - P_{1,k}^s$, ${g_{2M + 3}} = {\kern 1pt} {P_{\min }} - P_{2,k}^s$,···, ${g_{3M + 1}} = {\kern 1pt} {P_{\min }} - P_{M,k}^s$,
     ${g_{3M + 2}} = P_{1,k}^s - {P_{\max }}$,···, ${g_{4M + 1}} = P_{M,k}^s - {P_{\max }}$,设置可行域:$D = \left\{ T_{ {\rm{d} },m,k}^s,P_{m,k}^s\left| {g_a}\left( {T_{ {\rm{d} },m,k}^s,P_{m,k}^s} \right) \le 0,a = 1,2, ··· ,4M + 1,\right.\right.$
     $\left.1 \le m \leq M \right\}$其中,${g_a}\left( {T_{{\rm{d}},m,k}^s,P_{m,k}^s} \right) = {g_a},a = 1,2, ··· ,4M + 1$,取${\left( {T_{{\rm{d}},m,k}^s,P_{m,k}^s} \right)^{\left( 0 \right)}} \in D\left( {1 \le m \le M} \right)$为初始点,$\varepsilon > 0$为算法终
     止指标,${\xi _1} > 0$, $c \ge 2$,令$l = 1$;
     步骤 2 以${\left( {T_{{\rm{d}},m,k}^s,P_{m,k}^s} \right)^{\left( {l - 1} \right)}}$为初始点求解如下子问题:$\min {\kern 1pt} {F_1} - {\xi _1}\left[ {\frac{1}{ { {g_1} } } + \frac{1}{ { {g_2} } } + ··· + \frac{1}{ { {g_{2M + 2} } } } } \right], {\rm{s} }{\rm{.t} }{\rm{.} }{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} T_{ {\rm{d} },m,k}^s,P_{m,k}^s \in D$,其中,${F_1}$表
     示式(7)中的优化目标函数,令上述问题的极小值点为${\left( {T_{{\rm{d}},m,k}^s,P_{m,k}^s} \right)^{\left( l \right)}}$;
     步骤 3 检验终止条件,若$- {\xi _l}\left[ {\dfrac{1}{ { {g_1} } } + \dfrac{1}{ { {g_2} } } + ··· + \dfrac{1}{ { {g_{4M + 1} } } } } \right] < \varepsilon$,算法终止;否则,令${\xi _{l + 1} } \leftarrow \dfrac{ { {\xi _l} } }{c}$, $l \leftarrow l + 1$,转入步骤2。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-07-30
  • 修回日期:  2020-12-09
  • 网络出版日期:  2020-12-22
  • 刊出日期:  2021-03-22

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