一种基于双通道神经网络的直角频率多输入多输出雷达的新浪形状优化方法
Meng Xia1, Wenrong Gong1, Lichao Yang2
1School of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
这项研究涉及与线性频率调制 (LFM) 雷达波形的直角频率分割复合 (OFDM) 中的高侧叶水平. 深度神经网络方法优化了OFDM-LFM信号,显著提高了雷达检测性能.
科学领域:
- 雷达系统工程 雷达系统工程
- 信号处理 信号处理
- 在工程领域的人工智能.
背景情况:
- 在多输入多输出 (MIMO) 雷达中,与线性频率调制 (LFM) 的直角频率分割复合 (OFDM) 是常见的.
- 脉冲压缩后OFDM-LFM波形中的高侧叶水平降低了目标检测能力.
研究的目的:
- 调查OFDM-LFM波形中高侧叶水平的原因.
- 提出使用深度神经网络的新浪形状优化设计方法.
- 通过优化OFDM-LFM波形来提高雷达目标检测性能.
主要方法:
- 理论分析以确定高侧叶水平的来源.
- 深度神经网络设计使用ResNeXt架构用于双通道网络.
- 开发一种新的损失函数,以优化OFDM-LFM波形相位和带宽.
- 应用优化因子来管理峰值侧叶水平 (PSL) 和整体侧叶水平 (ISL).
主要成果:
- 数字结果证实了侧叶水平原因的理论分析.
- 提出的深度神经网络方法有效优化了OFDM-LFM波形.
- 设计的波形显示出优越的脉冲压缩特性.
- 观察到雷达检测性能显著改善.
结论:
- 理论分析准确地解释了OFDM-LFM波形中的高侧叶水平.
- 基于深度神经网络的优化为波形设计提供了有效的解决方案.
- 优化的OFDM-LFM波形增强了雷达系统的能力,特别是在目标检测方面.
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