Purpose

To reduce the influence of temperature on MEMS gyroscope, this paper aims to propose a temperature drift compensation method based on variational modal decomposition (VMD), time-frequency peak filter (TFPF), mind evolutionary algorithm (MEA) and BP neural network.

Design/methodology/approach

First, VMD decomposes gyro’s temperature drift sequence to obtain multiple intrinsic mode functions (IMF) with different center frequencies and then Sample entropy calculates, according to the complexity of the signals, they are divided into three categories, namely, noise signals, mixed signals and temperature drift signals. Then, TFPF denoises the mixed-signal, the noise signal is directly removed and the denoised sub-sequence is reconstructed, which is used as training data to train the MEA optimized BP to obtain a temperature drift compensation model. Finally, the gyro’s temperature characteristic sequence is processed by the trained model.

Findings

The experimental result proved the superiority of this method, the bias stability value of the compensation signal is 1.279 × 10^–3°/h and the angular velocity random walk value is 2.132 × 10^–5°/h/vHz, which is improved compared to the 3.361°/h and 1.673 × 10^–2°/h/vHz of the original output signal of the gyro.

Originality/value

This study proposes a multi-dimensional processing method, which treats different noises separately, effectively protects the low-frequency characteristics and provides a high-precision training set for drift modeling. TFPF can be optimized by SEVMD parallel processing in reducing noise and retaining static characteristics, MEA algorithm can search for better threshold and connection weight of BP network and improve the model’s compensation effect.

中文翻译：

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基于TFPF-MEA-BP算法的双质量MEMS陀螺仪温度漂移补偿

目的

为了减少温度对MEMS陀螺仪的影响，本文旨在提出一种基于变分模态分解（VMD），时频峰值滤波器（TFPF），思维进化算法（MEA）和BP神经网络的温度漂移补偿方法。

设计/方法/方法

首先，VMD分解陀螺仪的温度漂移序列以获得具有不同中心频率的多个本征模式函数（IMF），然后根据信号的复杂度进行样本熵计算，将其分为噪声信号，混合信号和噪声信号三类。温度漂移信号。然后，TFPF对混合信号进行去噪，直接去除噪声信号，并重建去噪后的子序列，将其用作训练数据以训练MEA优化的BP以获得温度漂移补偿模型。最后，陀螺仪的温度特性序列由训练后的模型处理。

发现

实验结果证明了该方法的优越性，补偿信号的偏置稳定度值为1.279×10 ^–3 °/ h，角速度随机游走值为2.132×10 ^–5 °/ h / vHz，与之相比有较大的提高。陀螺仪原始输出信号的3.361 °/ h和1.673×10 ^–2 °/ h / vHz。

创意/价值

这项研究提出了一种多维处理方法，该方法可以分别处理不同的噪声，有效地保护低频特性，并为漂移建模提供高精度的训练集。可以通过SEVMD并行处理对TFPF进行优化，以降低噪声并保持静态特性，MEA算法可以寻找更好的BP网络阈值和连接权重，提高模型的补偿效果。