基于知识增强压缩测量和深度学习的FHSS信号的自适应性联合载体和DOA估计
Yinghai Jiang1, Feng Liu1,2
1College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China.
Entropy (Basel, Switzerland)
|July 26, 2024
概括
本研究介绍了一种适应性压缩传感方法,用于频率跳跃的扩散频谱 (FHSS) 信号. 这种新的方法改善了对不合作的接收者的运营商和到达方向 (DOA) 估计.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 通信工程 通信工程
背景情况:
- 频率跳跃传播频谱 (FHSS) 对于安全通信至关重要,但由于采样率高的要求,不合作的接收器面临着挑战.
- 传统方法需要捕获整个FHSS跳跃范围,遵守尼奎斯特采样定理,这是低效的.
研究的目的:
- 开发一种适应性压缩传感方法,用于对FHSS信号的联合载体和到达方向 (DOA) 估计.
- 为了使FHSS信号的高效非合作处理能够在没有事先了解载波频率的情况下实现.
- 改进现有的压缩传感技术,用于FHSS信号分析.
主要方法:
- 提出了一种适应式压缩传感方法,利用测量内核优化后置信号知识和任务特定信息.
- 使用深度神经网络来提高测量内核设计过程的效率.
- 从压缩的测量数据中估计信号载体和DOA.
主要成果:
- 与随机测量内核相比,自适应测量内核显示出更高的性能.
- 拟议的自适应压缩传感方法的性能优于文献中现有的压缩方法.
- 成功地实现了对FHSS信号的载波频率和DOA的联合估计.
结论:
- 适应式压缩传感方法为非合作的FHSS信号处理提供了显著的改进.
- 深度神经网络辅助的内核设计为复杂的信号估计任务提供了有效的解决方案.
- 这种技术增强了接收器在没有预先了解信号参数的情况下运行的功能.
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