Implementation and Performance Assessment of Wavelet Prefiltered Platform Tilt Computation Using Low-cost MEMS IMU

Arya S J, V. R. Jisha, Ponmalar M, K. Usha, Haridas T R
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Abstract

MEMS sensors are becoming ubiquitous. Their applications range from navigation solutions in automated driving and personal health monitoring as wearable health devices to biomedical applications. However, their inherent high noise levels limit their use in high-accuracy applications. The scientific community explores many methods to overcome this limitation. Often a trade-off between noise levels, complexity, and bandwidth is encountered. In this paper, MEMS IMU data is denoised using wavelet-based prefiltering. This method retains the high dynamics content of the signal as well. The paper presents the algorithm of Wavelet transform and threshold parameters. We also perform the comparison with traditional methods by defining the key performance indicators (KPIs). Comparison carried out on a composite simulated signal that mimics the real-world signal and original MEMS IMU signal. Further, an actual use case is presented, viz., platform tilt computation, as part of the static alignment process in Inertial Navigation. Through extensive simulations, we establish the effectiveness of denoising sensor data using wavelet packet transform.
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基于低成本MEMS IMU的小波预滤波平台倾斜计算实现及性能评估
MEMS传感器正变得无处不在。他们的应用范围从自动驾驶的导航解决方案、个人健康监测(可穿戴健康设备)到生物医学应用。然而,它们固有的高噪声水平限制了它们在高精度应用中的使用。科学界探索了许多方法来克服这一限制。通常会遇到噪声水平、复杂性和带宽之间的权衡。本文采用基于小波预滤波的方法对MEMS IMU数据进行去噪。这种方法也保留了信号的高动态内容。给出了小波变换的算法和阈值参数。我们还通过定义关键绩效指标(kpi)与传统方法进行了比较。对模拟真实信号的复合仿真信号与原始MEMS IMU信号进行了比较。此外,给出了一个实际用例,即平台倾斜计算,作为惯性导航中静态对准过程的一部分。通过大量的仿真,验证了小波包变换对传感器数据去噪的有效性。
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