GS-16QAM OFDM与ANN方案结合LFM信号,用于联合通信和雷达传感系统
Optics letters
|June 30, 2023
概括
这项研究引入了一种新的集成波形,用于同时通信和雷达检测. 人工神经网络 (ANN) 显著提高了几何形状的16平方振幅调制 (QAM) 直角频率分割复杂化 (OFDM) 信号的性能.
科学领域:
- 无线通信是一种无线通信.
- 雷达传感器传感雷达的感应.
- 信号处理 信号处理
背景情况:
- 联合通信和雷达传感系统面临错误传播和信号干扰的挑战.
- 优化信号传输性能对于集成系统至关重要.
研究的目的:
- 提出并通过实验证明一种集成波形,用于W频段的同时通信和雷达检测.
- 为了提高使用人工神经网络 (ANN) 的几何形状的16平方幅度调制 (QAM) 直角频率分割复杂化 (OFDM) 信号的性能.
主要方法:
- 一个几何形状 (GS) 16-QAM OFDM通信信号与一个线性频率调制 (LFM) 雷达信号的集成.
- 人工神经网络 (ANN) 的应用,以减轻GS-16QAM OFDM信号中的性能限制.
- 在0.8米无线传输的W频段通信和雷达检测系统中进行实验验证.
主要成果:
- 与统一的16QAM OFDM相比,提出的GS-16QAM OFDM信号显示了改进的接收器灵敏度和正常化的一般相互信息 (NGMI).
- 在前进错误校正 (FEC) 值为3.8 × 10-3时观察到性能增强.
- 成功实现了多目标雷达检测,距离准确度达到厘米级.
结论:
- 综合波形方法可以实现同时通信和雷达传感.
- 基于ANN的方案有效地提高了联合通信和雷达系统的性能.
- 该系统实现了高通信质量和精确的雷达检测能力.
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