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基于融合算法的MEMS加速器的温度补偿
Yangyanhao Guo1, Zihan Zhang2, Longkang Chang3
1Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China.
Micromachines
|July 27, 2024
概括
这项研究引入了一种新的算法,它结合了变量模态分解 (VMD),FE算法,前向线性预测 (FLP) 和粒子群优化回向传播 (PSO-BP) 以有效地减少加速度计信号中的温度漂移.
科学领域:
- 传感器技术 传感器技术
- 信号处理 信号处理
- 数据融合数据融合
背景情况:
- 加速度计的性能经常受到温度漂移的影响,影响测量准确度.
- 现有的温度补偿方法可能不足或过于复杂.
- 准确的温度补偿对于各种应用中的可靠传感器数据至关重要.
研究的目的:
- 提出并验证一种新的聚变算法,用于补偿加速度计信号中的温度偏移.
- 为了提高加速度计在不同温度下测量的准确性和可靠性.
- 提高关键性能指标,如加速随机步行,零偏差和温度系数.
主要方法:
- 使用变量模态分解 (VMD) 将加速度计信号分解为内在模式函数 (IMF).
- 使用FE算法将IMF分为混合组件,温度漂移和纯噪声.
- 通过前进线性预测 (FLP) 消除混合噪声.
- 使用粒子群优化-反向传播 (PSO-BP) 开发温度调整模型.
- 处理组件的重建以获得增强的输出信号.
主要成果:
- VMD-FE-FLP-PSO-BP算法显著提高了加速度计的性能.
- 加速随机步行增强了23%.
- 零偏差得到了24%的改善.
- 与原始信号相比,温度系数显示出92%的显著增强.
结论:
- 拟议的VMD-FE-FLP-PSO-BP融合算法有效地弥补了加速度计信号中的温度偏移.
- 该方法在关键绩效指标上取得了实质性的改进,验证了其有效性.
- 这种方法为提高加速度计在温度敏感环境中的精度和稳定性提供了有希望的解决方案.
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