基于反障碍距离加权的复杂场景电磁频谱地图构建方法

陶诗飞; 吴昱江; 罗佳; 丁浩; 王元贺

doi:10.11999/JEIT231374

基于反障碍距离加权的复杂场景电磁频谱地图构建方法

doi: 10.11999/JEIT231374 cstr: 32379.14.JEIT231374

1.
南京理工大学电子工程与光电技术学院南京 210094
2.
电磁空间安全全国重点实验室成都 610036
3.
南京理工大学工程训练中心南京 210094

基金项目: 电磁空间安全全国重点实验室开放基金

详细信息

作者简介:
陶诗飞：男，副研究员，研究方向为电子侦查、目标识别、电磁隐身技术

吴昱江：男，硕士生，研究方向为电磁态势感知、电磁频谱地图构建

罗佳：男，高级工程师，研究方向为电子对抗技术

丁浩：男，高级工程师，研究方向为频谱管理、频谱监测、频谱作战

王元贺：男，硕士生，研究方向为电磁态势感知

通讯作者:
陶诗飞　s.tao@njust.edu.cn

中图分类号: TN97
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- 被引次数: 0
出版历程
- 收稿日期: 2023-12-13
- 修回日期: 2024-02-28
- 网络出版日期: 2024-03-08
- 刊出日期: 2024-08-30

Radio Environment Map Construction Method for Complex Scenes Based on Inverse Obstacle Distance Weighted

1.
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, NanJing 210094, China
2.
National Key Laboratory of Electromagnetic Space Security, Chengdu 610036, China
3.
Engineering Training Center, Nanjing University of Science and Technology, NanJing 210094, China

Funds: The National Key Laboratory of Electromagnetic Space Secunrity

摘要

摘要: 针对复杂场景中存在电磁波不可穿透的障碍物导致电磁频谱地图(REMs)构建性能不佳、反距离加权(IDW)算法受限于插值邻域的人工选择等问题，该文提出一种基于Voronoi图的反障碍距离加权(VIODW)的复杂场景电磁频谱地图构建算法。该算法通过创建包含障碍物的Voronoi图，为每一个待插值点自适应选定插值邻域用于电磁频谱数据构建，并利用任意角度路径寻优(ANYA)算法计算得到待插值点与插值邻域内每个监测站点之间的障碍距离，最后以障碍距离的反幂次作为权重加权获得待插值点处的电磁频谱数据，实现高精度的复杂场景电磁频谱地图构建。理论分析和仿真结果表明，该方法具有良好的构建精度，能够准确拟合出电磁波在复杂场景中的功率分布情况，为复杂场景下电磁频谱地图高精度构建提供了一种有效方法。
- 电磁频谱地图 /
- 反距离加权插值 /
- 障碍距离 /
- 复杂场景
Abstract: Addressing the issues of inadequate performance in constructing Radio Environment Maps (REMs) in complex scenarios due to non-penetrable obstacles for electromagnetic waves, and the arbitrary selection of interpolation neighborhoods imposed by Inverse Distance Weighted (IDW), a Voronoi-based Inverse Obstacle Distance Weighted algorithm (VIODW) is proposed in this paper. This algorithm adaptively defines interpolation neighborhoods for each interpolation point by creating Voronoi diagrams incorporating obstacles for numerical computation. Then, using the ANY-Angle (ANYA) Algorithm to calculate the obstacle distance between the interpolation point and each monitoring station within the interpolation neighborhood. Finally, by calculating the weighted mean with the inverse power of the obstacle distance as the weight, the value at the point is obtained, achieving high-precision construction of REMs in complex scenarios. Both theoretical analysis and simulation results demonstrate that this method offers excellent construction accuracy and can accurately model the power distribution of electromagnetic waves in complex scenarios. Hence, it provides an effective approach for high-precision REM construction in complex scenarios.
- Radio Environment Map(REM) /
- Inverse Distance Weighted(IDW) interpolation /
- Obstacle distance /
- Complex scenarios

HTML全文

图 1 基于反障碍距离加权的复杂场景电磁频谱地图构建流程

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图 2 障碍距离与欧式距离的示意图

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图 3 结晶生成法示意图

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图 4 障碍Voronoi图结晶生成法流程框图

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图 5 区域覆盖指标中3种区域位置示意图

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图 6 城市环境复杂场景示意图

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图 7 不同数量监测站点下各算法的区域覆盖指标性能对比

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图 8 监测站点数量为500时各算法重构得到的REM占用轮廓可视化效果

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图 9 监测站点数量为500时各算法重构得到的REM可视化效果

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表 1 不同数量电磁监测站点下各算法的RMSE对比

算法	50	100	200	500	1 000	2 000	3 000
IDW1	9.235 6	8.386 5	8.436 8	8.350 7	7.840 8	7.471 4	6.665 9
IDW2	6.662 7	4.748 4	4.513 7	4.053 2	3.458 2	3.268 8	2.773 6
IDW3	5.619 2	3.625 4	3.279 7	2.362 6	1.784 6	1.258 6	0.961 8
MSM	6.295 3	3.610 5	3.441 1	2.137 6	1.663 8	0.973 1	0.653 1
VIODW	4.761 9	3.185 5	2.518 8	1.689 5	1.332 0	0.846 7	0.623 1

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