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协调无线电发射器检测过程使用无人驾驶飞行器组

Maciej Mazuro¹, Paweł Skokowski¹, Jan M Kelner¹

¹Institute of Communications Systems, Faculty of Electronics, Military University of Technology, 00-908 Warsaw, Poland.

Sensors (Basel, Switzerland)

|December 11, 2025

概括

无人机组为实时电磁频谱监测提供了强大的解决方案. 配合Dempster-Shafer理论 (DST) 数据融合的合作无人机系统在动态环境中提高了检测准确性.

科学领域:

电气工程电气工程
计算机科学计算机科学
航空航天工程航空航天工程

背景情况:

随着电磁频谱需求的增加和干扰,需要先进的监控解决方案.
传统的静态频谱监控系统在复杂,动态的环境中扎.
无人驾驶飞行器 (UAV) 是适应性频谱传感的潜在平台.

研究的目的:

调查合作无人机组对实时频谱监测的有效性.
为了评估使用Dempster-Shafer理论 (DST) 整合无人机传感器数据的数据融合方法.
评估系统在各种环境和移动条件下的性能.

主要方法:

开发一个配合多个无人机配备软件定义无线电 (SDR) 的合作监控系统.
在中央数据融合中心实现Dempster-Shafer理论 (DST) 用于数据融合.
在MATLAB中模拟无人机移动性,通信延迟和传播效应.

主要成果:

合作无人机频谱监测与DST数据融合显著提高了检测稳定性.
与单个传感器方法相比,该系统对噪音和干扰的敏感性降低.
即使在具有挑战性的传播条件下,也保持了可靠的性能,DST融合提供了决策支持.

结论:

无人机组是实时频谱监测的可扩展和适应性工具.
拟议的方法提升了认知无线电网络的智能监控架构.
基于DST的数据融合有效地处理来自分布式无人机传感器的不确定性和冲突数据.

关键词:

数据融合数据融合无人机群是无人机群的一群.传感器网络传感器网络传感器网络频谱监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱的监测频谱无人驾驶飞行器是一种无人驾驶飞行器.

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