面向重大活动电磁安全管控的协同电磁压制方法

石嘉; 李安童; 李赞; 肖什贵; 魏庆

doi:10.11999/JEIT231318

面向重大活动电磁安全管控的协同电磁压制方法

doi: 10.11999/JEIT231318

西安电子科技大学ISN国家重点实验室西安 710071

基金项目: 国家重点研发计划(SQ2022YFC3300019)

详细信息

作者简介:
石嘉：男，副教授、博士生导师，主要研究方向为无线系统资源分配、毫米波通信、隐蔽通信等

李安童：女，硕士生，研究方向为电子对抗、无线电通信干扰

李赞：女，教授、博士生导师，研究方向为突发通信、数字信号处理、无线通信系统等

肖什贵：男，硕士生，研究方向为频谱分配、无线电干扰

魏庆：女，博士生，研究方向为无线电资源管理、多目标优化

通讯作者:
李赞　zanli@xidian.edu.cn

中图分类号: TN975
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- 被引次数: 0
出版历程
- 收稿日期: 2023-11-30
- 修回日期: 2024-04-28
- 网络出版日期: 2024-05-07
- 刊出日期: 2024-05-30

Collaborative Electromagnetic Suppression Method for Electromagnetic Security Control of Major Events

State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China

Funds: The National Key R&D Program of China (SQ2022YFC3300019)

摘要

摘要: 该文研究了复杂环境条件下重大活动安保区域的电磁安全协同压制技术问题。首先采用了城市环境电波传播模型，建模分析了城市复杂电磁环境的特征。其次，针对高效电磁压制与有效避免有害干扰问题，利用势博弈方法设计电磁压制设备协同部署算法，基于此，提出了基于遗传算法的压制设备功率优化方法，实现了电磁压制设备协同工作下干扰功率的高效投送。仿真结果表明，所提出的电磁压制设备位置部署算法可获得与理论最优方法(即遍历算法)近似的优异性能，且运算复杂度更低；在保证相同干扰效果下，所提功率优化算法相较于传统干扰功率分配方法的传输功率降低了50%以上，实现了精准协同管控。
- 通信干扰 /
- 电磁管控 /
- 重大安保活动 /
- 博弈论 /
- 遗传算法
Abstract: The electromagnetic security cooperative suppression technology in the security area of major events under the complex urban environment is presented in this paper. Firstly, the complex urban electromagnetic environment is modeled by using the radio wave propagation model suitable for dense urban environment. Secondly, aiming at the problem of efficient electromagnetic suppression and effective avoidance of harmful interference, the potential game method is used to design the cooperative deployment algorithm of electromagnetic suppression equipment. Building upon this, the power optimization method of suppression equipment based on genetic algorithm is proposed to achieve the efficient delivery of interference power under the cooperative work of electromagnetic suppression equipment. The simulation results indicate that the proposed electromagnetic suppression equipment deployment algorithm can obtain outstanding performance similar to the theoretical optimal method (i.e., traversal algorithm), with lower computational complexity. Moreover, under identical interference effectiveness, the proposed power optimization algorithm reduces transmission power by over 50% compared to the traditional interference power allocation methods, thereby achieving precise collaborative control.
- Communication interference /
- Electromagnetic control /
- Major security activities /
- Game theory /
- Genetic algorithm

HTML全文

图 1 通信干扰示意图

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图 2 典型场景示意图

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图 3 遗传算法实现流程

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图 4 不同设备数下4种算法的典型点标准差对比

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图 5 不同设备数下4种算法的白名单误伤数对比

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图 6 不同设备数下的遗传算法收敛情况

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图 7 不同设备数下固定发射功率与遗传算法的均方误差

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表 1 符号的定义

符号	定义
$i$	可部署设备点索引 $,i \in [1,M]$
$j$	典型点索引 $,j \in [1,N]$
$k$	白名单索引 $,k \in [1,P]$
$q$	管制台站索引 $,q \in \left[ {1,Q} \right]$
$P_{{t_j}}^{{\text{Th}}}$	典型点 ${t_j}$ 接收机正常工作的阈值
$P_{{w_k}}^{{\text{Th}}}$	白名单设备 ${w_k}$ 能允许的最大压制强度
$P_{{c_q}}^{{\text{Th}}}$	管制台站 ${c_q}$ 接收机正常工作的阈值
$d_{{s_i},{t_j}}^{{\text{2D}}}$	设备点 ${s_i}$ 到典型点 ${t_j}$ 的2维距离
${d_{{s_i},{t_j}}}$	设备点 ${s_i}$ 到典型点 ${t_j}$ 的3维距离

下载: 导出CSV

1 基于势博弈的压制设备位置部署算法

输入： ${{\mathbf{P}}_{{\text{rx}}}}$ , ${\bf{P}}{{\bf{C}}_{{\text{rx}}}}$ , ${\bf{P}}{{\bf{W}}_{{\text{rx}}}}$ , $P_{{t_j}}^{{\text{th}}}$ , $P_{{c_q}}^{{\text{th}}}$ , $P_{{w_k}}^{{\text{th}}}$
初始化：
(1) 设置收敛阈值 $\varepsilon$
(2) 初始化效用函数最小值 ${U_{\min }} = \infty$ ，迭代变量 $r = 1$
Repeat
随机产生一组可部署设备方案 ${V^r} = \{ v_{{d_1}}^r,v_{{d_2}}^r, \cdots ,v_{{d_I}}^r\}$
$P_{{t_j}}^{{\text{sum}},r} \leftarrow$ 利用式(22)计算叠加在典型点的接收干扰功率
$P_{{c_q}}^{{\text{sum}},r} \leftarrow$ 利用式(23)计算叠加在管制台站的接收干扰功率
for $t = 1:N,q = 1:Q$ do
if $P_{{t_j}}^{{\text{sum}},r} \ge P_{{t_j}}^{{\text{th}}}$ ， $P_{{c_q}}^{{\text{sum}}} \ge P_{{c_q}}^{{\text{th}}}$ then
${U^r} \leftarrow$ 利用式(18)，式(19)，式(20)，式(24)，式(25)计算　　　　效用函数
${U_{\min }} \leftarrow$ 更新效用函数最小值
else
$r = r + 1$
end if
end for
Until $\left\| {{U^r} - {U_{\min }}} \right\| < \varepsilon$
输出： ${V^*} \leftarrow$ 根据最小效用函数得到最优设备方案

下载: 导出CSV

2 基于遗传算法的协同压制功率优化方法

输入： ${{\mathbf{P}}_{{\text{rx}}}}$ , ${\bf{P}}{{\bf{C}}_{{\text{rx}}}}$ , ${\bf{P}}{{\bf{W}}_{{\text{rx}}}}$ , $P_{{t_j}}^{{\text{th}}}$ , $P_{{c_q}}^{{\text{th}}}$ , $P_{{w_k}}^{{\text{th}}}$
初始化：
(1) 设置最大进化代数 $T$ ，交叉概率 ${P_{\text{c}}}$ ，变异概率 ${P_{\text{m}}}$ ，种群规模 $M$
(2) 初始化种群 ${P_0}$ 并计算当前种群中各个体的适应度，迭代变量 $t = 0$
while $t < T$ do
$t = t + 1$
${P_{\text{s}}} \leftarrow$ 利用式(29)，式(30)计算适应度得到每个个体的选择概率
${\mathrm{chroms}} \leftarrow$ 根据 ${P_s}$ 随机选择个体
$({{\mathrm{chrom}}_1},{{\mathrm{chrom}}_2}) \leftarrow$ 根据交叉概率 ${P_{\text{c}}}$ 随机生成需要交叉的个体
$({{\mathrm{child}}_1},{{\mathrm{child}}_2}) \leftarrow$ 利用式(31)生成新的交叉子代个体
${\mathrm{chrom}} \leftarrow$ 根据变异概率 ${P_{\text{m}}}$ 随机生成需要变异的个体
${\mathrm{child}} \leftarrow$ 利用式(32)生成新的变异子代个体
end while
输出： ${P_{{T_{{\text{opt}}}}}} \leftarrow$ 最大适应度个体

下载: 导出CSV

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