Modified Adaptive Real-time Filtering Algorithm for MEMS Gyroscope Random Noise

Jun FU; Hong-xiang HAN

doi:10.3788/gzxb20194812.1212003

Journals >Acta Photonica Sinica >Volume 48 >Issue 12 >Page 1212003 > Article

Acta Photonica Sinica
Vol. 48, Issue 12, 1212003 (2019)

Modified Adaptive Real-time Filtering Algorithm for MEMS Gyroscope Random Noise

Jun FU and Hong-xiang HAN^*

Author Affiliations

Department of Navigation, Naval University of Engineering, Wuhan 430033, China

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DOI: 10.3788/gzxb20194812.1212003 Cite this Article

Jun FU, Hong-xiang HAN. Modified Adaptive Real-time Filtering Algorithm for MEMS Gyroscope Random Noise[J]. Acta Photonica Sinica, 2019, 48(12): 1212003 Copy Citation Text

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Fig. 1. 3DM-GX4-25 X轴预处理后数据3DM-GX4-25 X-axis post-preprocessing data

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Fig. 2. MTi-100 X轴预处理后数据MTi-100 X-axis post-preprocessing data

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Fig. 3. Triaxial rate turntable and device packaging

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Fig. 4. MTi-100 sensor gyro X-axis ARMA model parameters online update curve

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Fig. 5. Comparison of output data before and after X-axis filter of MTi-100 gyro

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Fig. 6. Comparison of output data before and after X-axis filter of 3DM-GX3-25 gyro

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Fig. 7. Comparison of output data before and after X-axis filter of MTi-100 gyro

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Fig. 8. Comparison of output data before and after X-axis filter of 3DM-GX3-25 gyro

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Sensors	First set of data	Second set of data	Third set of data	Fourth set of data
Mti-100	-0.341	-0.344	-0.346	-0.360
3DM-GX4-25	0.054	0.060	0.063	0.067

Table 1. Output mean of two MEMS gyro

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Model name	AR(p)	MA(q)	ARMA(p, q)
Self-correlation coefficient function	Trailing	q step cut-off	Trailing
Partial correlation coefficient function	p-step tailing	Trailing	Trailing

Table 2. The traditional method of model identification

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Parameters	X_k	W_k	A	B
ARMA (1, 1)	\begin{document}${\left[ {\begin{array}{*{20}{l}} {{y_k}}&k \end{array}} \right]^{\rm{T}}}$\end{document}	\begin{document}${\left[ {\begin{array}{*{20}{l}} {{a_k}}&{{a_k} - 1} \end{array}} \right]^{\rm{T}}}$\end{document}	\begin{document}$\left[ {\begin{array}{*{20}{l}} {{\varphi _1}}&1\\ 0&1 \end{array}} \right]$\end{document}	\begin{document}$\left[ {\begin{array}{*{20}{c}} 1&{{\theta _1}}\\ 0&0 \end{array}} \right]$\end{document}
ARMA (2, 1)	\begin{document}${\left[ {\begin{array}{*{20}{l}} {{y_k}}&{{y_{k - 1}}}&k \end{array}} \right]^{\rm{T}}}$\end{document}	\begin{document}${\left[ {\begin{array}{*{20}{l}} {{a_k}}&{{a_k} - 1} \end{array}} \right]^{\rm{T}}}$\end{document}	\begin{document}$\left[ {\begin{array}{*{20}{c}} {{\varphi _1}}&{{\varphi _2}}&1\\ 1&0&0\\ 0&0&1 \end{array}} \right]$\end{document}	\begin{document}$\;\left[ {\begin{array}{*{20}{c}} 1&{{\theta _1}}\\ 0&0\\ 0&0 \end{array}} \right]$\end{document}

Table 3. The traditional method of model identification

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Sensors	Methods	Mean	Mean variance
MTi-100	Original output	-0.353	0.078
	KF	-0.353	0.037
	AKF	-0.353	0.019
	Method in this paper	-0.353	0.009
3DM-GX3-25	Original output	0.071	0.197
	KF	0.071	0.195
	AKF	0.071	0.099
	Method in this paper	0.071	0.013

Table 4. Statistical characteristics of gyro output signal before and after filtering

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Sensors	Methods	Mean	Mean variance
MTi-100	Original output	4.844	0.377
	KF	4.844	0.333
	AKF	4.844	0.315
	Method in this paper	4.844	0.275
3DM-GX3-25	Original output	5.092	0.323
	KF	5.092	0.321
	AKF	5.092	0.317
	Method in this paper	5.092	0.269

Table 5. Statistical characteristics of gyro output signal before and after filtering

Jun FU, Hong-xiang HAN. Modified Adaptive Real-time Filtering Algorithm for MEMS Gyroscope Random Noise[J]. Acta Photonica Sinica, 2019, 48(12): 1212003

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