系统工程与电子技术 ›› 2023, Vol. 45 ›› Issue (3): 708-716.doi: 10.12305/j.issn.1001-506X.2023.03.11

• 传感器与信号处理 • 上一篇    下一篇

基于自注意力的双波段预警雷达微动融合识别

赵庆媛, 赵志强, 叶春茂, 鲁耀兵   

  1. 北京无线电测量研究所, 北京 100854
  • 收稿日期:2022-04-01 出版日期:2023-02-25 发布日期:2023-03-09
  • 通讯作者: 叶春茂
  • 作者简介:赵庆媛(1986—), 女, 高级工程师, 硕士,主要研究方向为雷达智能化应用、目标识别
    赵志强(1992—), 男, 工程师, 硕士, 主要研究方向为雷达目标识别
    叶春茂(1981—), 男, 研究员, 博士, 主要研究方向为雷达系统设计及应用技术
    鲁耀兵(1965—), 男, 研究员, 博士, 主要研究方向为雷达系统总体设计、新体制雷达技术

Micro-motion fusion recognition of double band early warning radar based on self-attention mechanism

Qingyuan ZHAO, Zhiqiang ZHAO, Chunmao YE, Yaobing LU   

  1. Beijing Institute of Radio Measurement, Beijing 100854, China
  • Received:2022-04-01 Online:2023-02-25 Published:2023-03-09
  • Contact: Chunmao YE

摘要:

针对预警雷达对气动目标协同识别的需求, 提出一种自适应权重双输入自注意力残差融合识别方法。通过分析不同波段雷达对气动目标的微动差异性, 在传统卷积块注意力模块(convolutional block attention module, CBAM)残差网络的基础上进行针对性改进, 设计加权双输入CBAM(weighted double input-CBAM, WDI-CBAM)残差结构, 对两种波段的时频图浅层特征自动分配权重并融合, 从而均衡不同波段对目标识别的贡献度。仿真和实测数据处理结果表明, WDI-CBAM残差网络训练代价小, 在信噪比较低及驻留时间较短的情况下识别率高。可视化结果进一步证明了所提方法能够合理分配不同波段输入对气动目标分类的重要性。

关键词: 自注意力机制, 权重自适应, 双波段融合, 气动目标识别

Abstract:

In response to the demand of early warning radar to aerodynamic target cooperative identification, an adaptive weight double-input self-attention residual fusion recognition method is proposed. By analyzing the fretting differences of different band radars on aerodynamic targets, the fusion neural network is modified on the basis of convolutional block attention module (CBAM) residual network. Weighted double input-CBAM (WDI-CBAM) residual structure is designed to automatically assign weight and merge the shallow features of time-frequency maps of two frequencies, so as to balance the contribution of different bands to target recognition. Simulation and measured data processing results show that the WDI-CBAM residual network has a lower training cost and a higher recognition rate under the condition of low signal to noise ratio and short resident time. The visualization results further prove that the reasonably importance of different band inputs is allocated for aerodynamic target classification by the proposed method.

Key words: self-attention mechanism, adaptive weight, double band fusion, aerodynamic target recognition

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