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Abstract：
MEMS accelerometers are significantly impacted by temperature and noise, leading to a considerable compromise in their accuracy. In response to this challenge, we propose a parallel denoising and temperature compensation fusion algorithm for MEMS accelerometers based on RLMD-SE-TFPF and GRU-attention. Firstly, we utilize robust local mean decomposition (RLMD) to decompose the output signal of the accelerometer into a series of product function (PF) signals and a residual signal. Secondly, we employ sample entropy (SE) to classify the decomposed signals, categorizing them into noise segments, mixed segments, and temperature drift segments. Next, we utilize the time-frequency peak filtering (TFPF) algorithm with varying window lengths to separately denoise the noise and mixed signal segments, enabling subsequent signal reconstruction and training. Considering the strong inertia of the temperature signal, we innovatively introduce the accelerometer\'s output time series as the model input when training the temperature compensation model. We incorporate gated recurrent unit (GRU) and attention modules, proposing a novel GRU-MLP-attention model (GMAN) architecture. Simulation experiments demonstrate the effectiveness of our proposed fusion algorithm. After processing the accelerometer output signal through the RLMD-SE-TFPF denoising algorithm and the GMAN temperature drift compensation model, the acceleration random walk is reduced by 96.11%, with values of 0.23032 g/h/Hz for the original accelerometer output signal and 0.00895695 g/h/Hz for the processed signal.
摘要：
MEMS加速度计受到温度和噪声的显著影响，导致他们的准确性相当大的妥协。为了应对这一挑战，提出了一种基于RLMD-SE-TFPF和GRU-attention的MEMS加速度计并行去噪和温度补偿融合算法。首先,我们利用鲁棒局部均值分解（RLMD）将加速度计的输出信号分解为一系列乘积函数（PF）信号和残差信号。其次,我们采用样本熵(SE)对分解后的信号进行分类，将它们分类为噪声段，混合段，和温度漂移段。接下来,我们利用具有不同窗口长度的时频峰值滤波（TFPF）算法来分别对噪声和混合信号段进行去噪，使随后的信号重建和训练。考虑到温度信号的强惯性，在训练温度补偿模型时，我们创新性地引入了加速度计的输出时间序列作为模型输入。我们纳入门控循环单元(GRU)和注意力模块，提出了一种新颖的GRU-MLP-注意力模型(GMAN)架构。仿真实验证明了所提融合算法的有效性。通过RLMD-SE-TFPF去噪算法和GMAN温度漂移补偿模型对加速度计输出信号进行处理后，加速度随机游走减少96.11%，原始加速度计输出信号的值为0.23032g/h/Hz，处理信号的值为0.00895695g/h/Hz。
