基于频率相关性双SVD重建的非合作性目标识别的运动杂乱抑制
Weikun He1, Yichuan Luo1, Xiaoxiao Shang1
1Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
在混乱的雷达环境中,将鸟类与无人机区分开来是一项挑战. 一种新的频率相关双SVD方法有效地抑制了杂乱,并保留了目标的微动作,从而实现了准确的鸟类和无人机分类.
科学领域:
- 雷达信号处理是指处理雷达信号的过程.
- 目标检测和分类目标的检测和分类
- 航空航天工程是航空航天工程.
背景情况:
- 低高度,缓慢和小 (LSS) 的目标,如鸟类和无人机,具有低可观测性挑战.
- 雷达观测通常会因来自飞机和车辆等来源的强烈运动混乱而退化.
- 准确区分鸟类和无人机对于空中交通安全和监控至关重要.
研究的目的:
- 开发一种可靠的方法来区分鸟类和无人机在存在大量运动混乱的情况下.
- 为了克服传统的杂乱抑制技术的局限性,这些技术可能会导致剩余的杂乱或目标信号损失.
- 在复杂的雷达环境中提高LSS目标监测的可靠性.
主要方法:
- 提出了一种频率相关双SVD (单值分解) 重建方法来抑制运动杂乱.
- 利用杂乱与鸟类/无人机目标弱分散的光谱相关性.
- 提取的微运动特征包括规范化大自值和时间频率能量的和.
- 使用内核模糊c-means算法进行目标分类.
主要成果:
- 拟议的方法有效地抑制了强烈的运动杂乱,同时保持了目标微运动特征.
- 证明避免了剩余的杂乱和目标损失,与传统的基于SVD的方法不同.
- 根据提取的微运动特征,实现了鸟类和无人机目标的准确分类.
- 使用模拟和实验雷达数据验证了该方法的有效性.
结论:
- 频率相关双SVD方法在杂乱干扰下在LSS目标歧视方面取得了重大进展.
- 该方法为区分鸟类和无人机提供了可靠的解决方案,这对航空安全至关重要.
- 保存的微运动标志是在具有挑战性的雷达场景中准确分类的关键区分因素.
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