信号拒绝的交通速度偏移计测量基于部分群体优化-变化模式分解方法
Chaoyang Wu1, Yiyuan Duan1, Hao Wang2
1School of Civil Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
使用部分群集优化-变化模式分解 (PSO-VMD) 的新方法有效地减少了交通速度偏移仪 (TSD) 信号中的噪声. 这种技术可以提高曲率数据识别的准确性,以便更好地分析道路状况.
科学领域:
- 土木工程 土木工程是指土木工程.
- 信号处理 信号处理
- 数据分析 数据分析
背景情况:
- 交通速度偏移计 (TSD) 收集关键的偏移数据用于道路评估.
- TSD信号中的噪音可能导致道路状况的识别和分析不准确.
- 现有的无声化方法可能不适合TSD特异性信号特征.
研究的目的:
- 建议和验证交通速度偏移仪 (TSD) 信号的新型无声化方法.
- 通过减少测量噪声来提高偏移数据识别的准确性.
- 为TSD信号消噪提供一个强大的策略.
主要方法:
- 变化模式分解 (VMD) 用于初始信号分解.
- 计算模式选择和信号重建的相关系数.
- 粒子集群优化 (PSO) 以使用模糊来优化VMD超参数 (K和α).
主要成果:
- 拟议的PSO-VMD方法在模拟信号上表现出优异的消噪性能.
- 通过使用PSO和模糊,成功实现了VMD参数 (K和α) 的优化.
- 实际的TSD数据证实了该方法在减少偏移噪声方面的有效性.
结论:
- 该PSO-VMD方法提供了一个有效和可行的策略,用于TSD信号消噪.
- 通过最大限度地降低噪声,可以更好地准确识别偏移数据.
- 这种方法有助于使用TSD数据进行更可靠的道路状况评估.
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