A complex unknown radar signal deinterleaving method based on selective deep embedding clustering

DING Chencong

doi:10.11959/j.issn.2096-6652.202529

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A complex unknown radar signal deinterleaving method based on selective deep embedding clustering

Papers and Reports | 更新时间：2025-11-18

- A complex unknown radar signal deinterleaving method based on selective deep embedding clustering
- Chinese Journal of Intelligent Science and Technology Vol. 7, Issue 3, Pages: 329-337(2025)
- 作者机构：
  
  中国人民解放军92728部队，上海 200436
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202529
  CLC： TP39
- Received：14 January 2025，
  
  Revised：2025-06-11，
  
  Accepted：16 July 2025，
  
  Published：15 September 2025
- 稿件说明：
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丁宸聪.基于选择性深度嵌入聚类的复杂未知雷达信号分选方法[J].智能科学与技术学报,2025,07(03):329-337.

DING Chencong.A complex unknown radar signal deinterleaving method based on selective deep embedding clustering[J].Chinese Journal of Intelligent Science and Technology,2025,07(03):329-337.
丁宸聪.基于选择性深度嵌入聚类的复杂未知雷达信号分选方法[J].智能科学与技术学报,2025,07(03):329-337. DOI： 10.11959/j.issn.2096-6652.202529.

DING Chencong.A complex unknown radar signal deinterleaving method based on selective deep embedding clustering[J].Chinese Journal of Intelligent Science and Technology,2025,07(03):329-337. DOI： 10.11959/j.issn.2096-6652.202529.

摘要

随着现代电子战与雷达技术的不断发展，战场环境中的雷达信号日趋复杂多样，这给信号分选带来了严峻挑战。传统分选方法通常依赖先验知识并预先设定类别数量，难以应对实际应用中可能出现的未知信号源与动态变化特征。针对这一问题，提出一种基于选择性深度嵌入聚类的复杂未知雷达信号分选方法。该方法结合深度学习在特征提取方面的优势与基于密度的聚类算法的灵活性，且无须依赖特定先验知识，实现对雷达信号参数的自适应嵌入与动态聚类；通过神经网络自动学习信号特征，并与基于密度的聚类算法相结合，有效克服了人工定义特征泛化能力不足的缺陷。实验结果表明，在复杂环境下的雷达信号数据集中，所提方法的聚类纯度达99.46%，具备良好的适应性与扩展性，能够准确识别未知或动态变化的信号特征。与传统方法相比，基于选择性深度嵌入聚类的复杂未知雷达信号分选方法不仅为海量复杂雷达信号处理提供了更具通用性与可靠性的解决方案，也为其他涉及未知信号的大规模数据挖掘任务提供了有益参考。

Abstract

With the continuous advancement of electronic warfare and radar technologies

radar signal in battlefield environments has become increasingly complex and diverse

which poses severe challenges to signal deinterleaving. Traditional deinterleaving methods typically rely on prior knowledge and a preset number of categories

making them unsuitable for handling unknown signal sources and dynamically changing features that may arise in real-world applications. To address this issue

a complex unknown radar signal deinterleaving method based on selective deep embedding clustering was proposed. By integrating the feature extraction capabilities of deep learning with the flexibility of a density-based clustering algorithm

this method achieved adaptive embedding and clustering of radar signal parameters without relying on specific prior knowledge. Through automatic feature learning via neural networks coupled with a density-based clustering algorithm

the need for manually defined features with limited generalizability was avoided. Experimental results on radar signal datasets from complex environments indicate that the proposed method achieves a cluster purity of up to 99.46%

demonstrating robust adaptability and extensibility in identifying unknown or dynamically changing signal characteristics. Compared with traditional methods

selective deep embedding clustering provides a more universal and reliable solution for large-scale

complex radar signal processing and offers valuable references for other extensive data mining tasks involving unknown signals.

关键词

Keywords

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