An efficient neural network model for de-noising of MEMS-based inertial data
An efficient neural network model for de-noising of MEMS-based inertial data
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DOI：
10.1017/s0373463304002875
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发表时间：
2004-09-01
影响因子：
2.4
通讯作者：
El-Diasty, M
中科院分区：
文献类型：
--
作者：
El-Rabbany, A;El-Diasty, M
基于微电子机械系统(MEMS)的惯性技术是最近发展起来的。它具有巨大的潜力，成为各种导航相关应用的未来技术。这主要是由于MEMS传感器的尺寸、成本和重量显著减少。然而，基于MEMS的低成本惯性传感器的一个主要缺点是其输出信号受到高电平噪声的污染。除非抑制高频噪声分量，否则无法实现预滤波方法的优化。提出了一种基于神经网络的MEMS惯性数据去噪模型。使用使用反向传播算法训练的模块化三层前馈神经网络来实现这一目的。利用仿真和实际的MEMS惯性数据集对模型进行了验证。结果表明，该模型能够在不改变原始信号随机性的前提下，将十字弓AHRS300CA捷联惯导数据的噪声降低一个数量级以上。这对于建立基于MEMS的惯性数据的通用随机模型是至关重要的。将所建立的神经网络模型与小波去噪方法进行了比较，进一步验证了该模型的有效性。结果表明，在达到与基于小波的去噪模型相同的去噪水平的情况下，改变了原始信号的随机特性。
Micro-Electro-Mechanical System (MEMS)-based inertial technology has recently evolved. It holds remarkable potential as the future technology for various navigation related applications. This is mainly due to the significant reduction in size, cost, and weight of MEMS sensors. A major drawback of low-cost MEMS-based inertial sensors, however, is that their output signals are contaminated by high-level noise. Unless the high frequency noise component is suppressed, optimizing the pre-filtering methodology cannot be achieved. This paper proposes a neural network-based de-noising model for MEMS-based inertial data. A modular, three-layer feedforward neural network trained using the back-propagation algorithm is used for this purpose. Simulated and real MEMS-based inertial data sets are used to validate the model. It is shown that the model is capable of reducing the noise of the Crossbow's AHRS300CA IMU data by over one order of magnitude without altering the stochastic nature of the original signal. This is of utmost importance in developing a generic stochastic model for MEMS-based inertial data. A comparison between the developed neural network model and the wavelet de-noising method is made to further validate the model. It is shown that achieving the same level of noise suppression with wavelet-based de-noising model changes the stochastic characteristics of original signal.