.png){kind=icon}
返回
Specific Emitter Identification Using Adaptive Signal Feature Embedded Knowledge Graph
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2023.3300689.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Specific emitter identification (SEI) plays an important role in secure Industrial Internet of Things (IIoT). In recent years, many SEI methods based on machine learning (ML) and deep learning (DL) have been proposed due to their great performance. However, DL-based SEI methods are accompanied by huge computation overhead, which is not suitable for IIoT applications. In addition, the existing ML-based SEI methods rely on feature extraction and a heavy and redundant classifier, which do not ensure optimal feature combination and efficient computation. To solve the above problem, we propose an improved DL-based SEI method using a signal feature embedded knowledge graph (KG) composed of universal features. To the best of our knowledge, this is the first attempt to apply KG for SEI technology. Specifically, we explore an adaptive feature combination (AFC) strategy through the attention mechanism to realize an efficient SEI classifier. The simulation results show that the proposed KG-AFC algorithm outperforms existing SEI methods in identification performance and computation overhead. At the same time, under the optimal compression rate, the average accuracy of the proposed SEI algorithm is higher than 99.2% and can effectively reduce complexity.
Keyword:
Adaptive feature combination (AFC) strategy
attention mechanism
knowledge graph (KG)
specific emitter identification (SEI)
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
.png){kind=icon}
Knowledge Graphs for Social Good: An Entity-Centric Search Engine for the Human Trafficking Domain社会公益知识图谱: 人口贩运领域以实体为中心的搜索引擎
PREAI
分享收藏分享收藏
查看更多内容