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基于特征分布和隔离森林的辐射源个体的新型类型的检测方法

Qiang Pan¹, Lei Shi¹, Changzhao Feng¹

¹School of Aerospace Science and Technology, Xidian University, Xi'an 710126, China.

Sensors (Basel, Switzerland)

|September 27, 2025

概括

本研究引入了一种集成深度特征表示和隔离森林 (IDFIF) 方法,以准确检测新型辐射发射器. 这种新的方法实现了超过94%的准确性,改进了传统的特定排放者识别系统.

科学领域:

电气工程电气工程
计算机科学计算机科学
信号处理信号处理

背景情况:

传统的特定发射者识别 (SEI) 系统在遇到新信号源时,与性能退化作斗争.
准确检测和拒绝新类实例对于强大的SEI系统至关重要.
现有的方法缺乏有效地对以前未见的信号类进行概括的能力.

研究的目的:

提出一个综合深度特征表示和隔离森林 (IDFIF) 方法,用于识别新型类辐射发射者.
提高SEI系统对未知信号源的检测和拒绝能力.
在开放条件下提高SEI的稳定性和普遍性.

主要方法:

使用卷积神经网络 (CNN) 来从In-phase/Quadrature (IQ) 信号中提取深层嵌入特征.
在提取的深层特征上使用无监督的隔离森林 (iForest),以建模已知的信号类的分布.
实施基于值的评分机制,用于检测异常,以确定新一类排放者.

主要成果:

拟议的IDFIF方法在现实世界ADS-B数据集上实现了超过94%的新型类检测准确度.
在识别以前未见的信号源方面,IDFIF显著优于现有的比较方法.
该方法对已知类样本的敏感性较低,确保在开放式环境中稳定性.

结论:

综合深度特征表示和隔离森林 (IDFIF) 方法有效地解决了新型类排放者识别的挑战.
在复杂的电磁环境中,IDFIF为特定的发射体识别提供了强大的和可通用的解决方案.
拟议的方法显示了在现实世界SEI应用中实际部署的重大前景.

关键词:

这是一个ADS-B信号.异常歧视异常一个孤立的森林.这是一个新型类型的检测检测.特定的排放者识别标识.

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