Denoising of MEMS Gyroscope Based on Improved Wavelet Transform
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摘要:
为提高MEMS陀螺仪测量精度，抑制测量噪声对其造成的影响，该文分析了某型号MEMS陀螺仪误差特性，提出基于递归最小二乘法(RLS)多重小波分解重构的强追踪自反馈模型，建立新的软阈值函数。由于模型处理后的数据带有部分奇异值，该文提出了一种改进的中值滤波算法。对于陀螺仪零偏噪声问题，提出零偏不稳定性抑制算法，并对该算法模型进行了详细的描述。将某项目研究中列车姿态测量系统的实验数据应用到该算法模型中。测试实验分为静态、动态两组，其结果均表明：该算法减小了信号中的噪声，有效地抑制了MEMS陀螺仪随机漂移，提高了姿态解算的精度。肯定了该算法对陀螺仪输出信号噪声去除，以及使用精度提升的可行性和有效性。
Abstract:In order to improve the measurement accuracy of Micro Electro Mechanical Systems (MEMS) gyroscopes, the influence of measurement noise on them is suppressed. The error characteristics of a certain type of MEMS gyroscope are analyzed. A strong tracking self-feedback model based on Recursive Least Square (RLS) multiple wavelet decomposition reconstruction is proposed to establish a new soft threshold function. Since the model processed data has partial singular values, an improved median filtering algorithm is proposed. For the problem of gyro zero-bias noise, a zero-bias stability suppression algorithm is proposed. In this paper, the algorithm model is described in detail, and the experimental data of the train attitude measurement system in a project research are applied to the algorithm model. The test experiments are divided into static and dynamic groups. The results show that the algorithm reduces the noise in the signal, suppresses effectively the random drift of the MEMS gyroscope and improves the accuracy of the attitude calculation. The feasibility and effectiveness of this method are affirmed to remove the signal noise of the gyroscope output and improve the accuracy of the use.
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Key words:
- MEMS gyroscope /
- Wavelet decomposition /
- Attitude estimation
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表 1 传感器性能参数
陀螺仪 加速度计 磁力计 测量范围 ±150, ±500, ±1000, ±2000 (°/s) ±2, ±4, ±8, ±16 (g) ±0.6 (mT) 噪声密度 0.01° (/s·$\sqrt {{\rm{Hz}}} $) 110 (μg/$\sqrt {{\rm{Hzrms}}} $) 48 (nv/$\sqrt {{\rm{Hz}}} $) 敏感度 12.5 mv (/°·s) 1000 (mv/g) 0.1 mv (v·μT) 温漂 2% –0.3%/℃ ±0.3% 采样频率 0.1～200 Hz 0.1～20 Hz 0.1～20 Hz ARW (°/h0.5) 1.57 – – RRW (°/h1.5) 600 – – BI (°/h) 224.2 – – 
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表 2 两种小波变换对陀螺仪数据处理结果
算法 坐标轴 运行时间(s) RMS误差估计 RRW (°/h1.5) ARW (°/h0.5) BI (°/h) RR (°/h) 传统的小波变换 x 26.754976 10.1147 195.2674 0.0301 1.8401 5.3524 y 28.744975 9.2655 260.4219 0.0283 1.7349 4.5069 z 27.645963 9.2012 220.3894 0.0117 1.4410 12.7358 改进的小波变换 x 26.85396 0.1290 68.6507 0 0 3.0727 y 28.64576 0.1249 32.9762 0 0 2.3039 z 27.69872 0.1247 8.6092 0 0 8.7398
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表 3 姿态解算的MSE误差估计
坐标轴 MSE误差 算法改进前 算法改进后 z 4.3257×10–4 1.1512×10–7 x 8.7754×10–4 8.5849×10–7 y 1.5196×10–4 8.4663×10–5
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表 4 两种算法角速率误差比较数据
算法 坐标轴 MSE (°/s) 运行时间(s) MAE (°/s) ARE (%) 传统的小波变换 x 0.0421 7.614595 0.0554 11.10 y 0.0623 8.130619 0.0796 13.41 z 0.0976 8.647342 0.0842 15.76 改进的小波变换 x 0.0999 8.467372 0.0236 8.86 y 0.0043 7.047250 0.0354 10.87 z 0.0025 8.021335 0.0416 12.52
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