基于惯性测量单元的姿态传感器的噪声建模和分析：通过滤波和传感器融合改善性能
Noise modeling and analysis of an IMU-based attitude sensor: improvement of performance by filtering and sensor fusion
摘要：
我们描述的特性和消除噪声存在于惯性测量单元（IMU）MPU-6050。该IMU最初用于内部开发的姿态传感器（AS），随后用于为小型气球载天文有效载荷开发的指向和稳定平台。我们发现，IMU的性能随着时间的推移，由于不同的错误的积累而下降。使用Allan方差分析方法，我们确定了IMU中存在的噪声的不同成分，并使用功率谱密度分析（PSD）验证了结果。我们尝试使用平滑滤波器，如移动平均滤波器和Savitzky-Golay滤波器去除高频噪声。虽然我们确实设法过滤了一些高频噪声，但这些滤波器的性能并不令人满意。我们使用概率密度分析找到了IMU中存在的随机噪声的分布，并确定噪声本质上是高斯白噪声，我们通过卡尔曼滤波器实时成功地去除了该噪声。
英文摘要：
We describe the characterization and removal of noise present in the Inertial Measurement Unit (IMU) MPU-6050. This IMU was initially used in an attitude sensor (AS) developed in-house, and subsequently implemented in a pointing and stabilization platform developed for small balloon-borne astronomical payloads. We found that the performance of the IMU degrades with time due to the accumulation of different errors. Using the Allan variance analysis method, we identified the different components of noise present in the IMU and verified the results using a power spectral density analysis (PSD). We tried to remove the high-frequency noise using smoothing filters, such as moving average filter and Savitzky-Golay filter. Although we did manage to filter some of the high-frequency noise, the performance of these filters was not satisfactory for our application. We found the distribution of the random noise present in the IMU using a probability density analysis, and identified the noise to be white Gaussian in nature which we successfully removed by a Kalman filter in real time.
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