一种细分低级双可定位的静态共振方法,用于在雨天条件下在毫米波烟中固定距离检测目标
Bing Yang1, Kaiwei Wu1, Zhe Guo1
1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
这项研究引入了一种新的系统,以改善雨降低的毫米波 (MMW) 烟信号. 新方法提高了信号噪声比 (SNR),使得即使在恶劣的天气条件下,也可以可靠地检测目标.
科学领域:
- 雷达系统工程 雷达系统工程
- 信号处理 信号处理
- 对射频的环境影响
背景情况:
- 防火器中使用的毫米波 (MMW) 信号在雨中遭受严重的退化.
- 降雨导致衰减和散射,降低信号噪声比 (SNR) 和范围精度.
- 现有的方法在恶劣天气下与实时处理和性能作斗争.
研究的目的:
- 开发一个强大的系统,以增强MMW在雨天条件下的烟信号.
- 为了提高目标检测的准确性和可靠性,尽管降雨干扰.
- 为了满足MMW烟应用程序的实时处理需求.
主要方法:
- 提出了一个新的细分低级双稳定静态共振 (SLOBSR) 系统.
- 开发了一种固定距离目标检测算法.
- 集成的信号预处理,粒子群集优化 (PSO) 用于参数调整,以及 kurtosis 值检测.
主要成果:
- 实现了9.94dB的SNR增强,用于MMW烟在雨中的回声.
- 在低至-15dB的信号噪声比率下实现可靠的目标检测.
- 与传统方法相比,在SNR增强和计算效率方面表现出卓越的性能.
结论:
- SLOBSR系统显著提高了MMW烟在雨天环境中的性能.
- 拟议的检测算法增强了防雨干扰能力.
- 该方法为在恶劣天气条件下可靠的MMW烟运行提供了可行的解决方案.
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