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MEMS (Micro-electromechanical Systems) gyroscope online noise reduction method based on normalized LMS (Least Mean Square) algorithm

An LMS algorithm and normalization technology, applied in the field of navigation systems, can solve problems such as large amount of calculation, poor real-time performance, and strict filtering restrictions, and achieve the effects of high real-time performance, reduced calculation amount, and improved accuracy

Inactive Publication Date: 2018-11-13
HARBIN ENG UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Literature "Application of Digital Filters in Micromechanical Gyroscope Systems", Sensors and Microsystems, 2003, 22(9): 56-57 Using traditional digital filters can filter out the high-frequency noise part of the signal, but for non-stationary and bandwidth For narrower signals, traditional digital filters have deficiencies and limitations; the document "MEMS Sensor Random Error Allan Variance Analysis", Journal of Instrumentation, 2011, 32(12): 2863-2868 proposes to use the Allan variance method to determine the MEMS gyroscope The type of random noise and its source and characteristics of the instrument, use curve fitting to find out the error coefficients, and then use the Kalman filter online noise reduction technology, which can effectively suppress white noise or colored noise, but due to the strict filtering conditions, The prior statistical properties of the data must be known accurately, so there are certain limitations
Document "A real-time wavelet noise reduction algorithm", Journal of Instrumentation, 2004, 25(6): 781-783 uses the interval wavelet noise reduction algorithm to construct a real-time wavelet noise reduction algorithm, and its noise reduction effect is better, but this method The real-time performance is not strong and the amount of calculation is large, so this method is not suitable for inertial navigation systems

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  • MEMS (Micro-electromechanical Systems) gyroscope online noise reduction method based on normalized LMS (Least Mean Square) algorithm
  • MEMS (Micro-electromechanical Systems) gyroscope online noise reduction method based on normalized LMS (Least Mean Square) algorithm
  • MEMS (Micro-electromechanical Systems) gyroscope online noise reduction method based on normalized LMS (Least Mean Square) algorithm

Examples

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Effect test

Embodiment 1

[0087] Example 1: Handling static data:

[0088] The MEMS gyroscope is placed statically on a single-axis turntable, the sampling frequency is 100Hz, and 20,000 static data are collected. The dimension N is selected as 150, and the convergence factor μ is fixed n Choose 0.001, parameter α choose 0.0005, coefficient vector and input vector are zero vectors, and other initial variables are zero. Such as Figure 4 , it can be seen that the noise reduction effect is more obvious, and the output does not have the problem of lag. Through the analysis of the data, the root mean square value of the data noise before and after noise reduction is 0.9236 and 0.2793 respectively, and the noise reduction effect is obvious.

Embodiment 2

[0089] Example 2: Handling dynamic data:

[0090] Place the MEMS gyroscope on a single-axis turntable, set the rotation speed of the turntable to 6° / s, and the sampling frequency to 100Hz to collect 20,000 pieces of dynamic data. The parameter N is selected as 150, and the convergence factor μ is fixed n Choose 0.001, parameter α choose 0.0005, coefficient vector and input vector are zero vectors, and other initial variables are zero. Such as Figure 5 , it can be seen that the noise reduction effect of the present invention is very good, and the output does not have the problem of lagging. Figure 5 It shows that the gyro output value is slightly smaller than the reference true value 6° / s of the turntable output, which is caused by the constant value error of the gyro, and the noise reduction effect shows that the present invention can effectively suppress the random error of the MEMS gyroscope, but cannot suppress the constant value Error, since it is relatively easy to s...

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Abstract

The invention belongs to the field of a navigation system and discloses an MEMS (Micro-electromechanical Systems) gyroscope online noise reduction method based on a normalized LMS (Least Mean Square)algorithm. The MEMS gyroscope online noise reduction method comprises the following steps: step (1): designing the normalized LMS algorithm with a more rapid convergence speed; step (2): deducing a relation between a convergence factor and an instant square error and determining the value of the convergence factor; introducing a convergence factor control misalignment amount into an updating equation of the normalized LMS algorithm and introducing a parameter alpha to control step length; step (3): determining the value of the alpha and the range of the convergence factor. By adopting the MEMSgyroscope online noise reduction method provided by the invention, the problem that the expectation of real-time output value of an MEMS gyroscope is unpredictable is solved and online noise reduction of a gyroscope signal is realized; the accuracy of outputting angular motion information by the MEMS gyroscope of a low-cost strap-down inertial navigation system can be improved; the MEMS gyroscopeonline noise reduction method has the advantages of good noise reduction effect, high timeliness and reduced calculation amount.

Description

technical field [0001] The invention belongs to the field of navigation systems, in particular to an online noise reduction method for MEMS gyroscopes based on a normalized LMS algorithm. Background technique [0002] Inertial measurement components are directly installed on aircraft, ships, missiles and other bodies that require navigation information such as attitude, speed, and heading, and a navigation technology that uses a computer to convert the measurement signals into navigation parameters. The rapid development of modern computer technology has created conditions for the strapdown inertial navigation system. Since people began to study this new type of navigation system in the late 1950s, it has been successfully used to guide spacecraft re-entering the atmosphere. The strapdown inertial navigation system played a role as a backup system on the US "Apollo" spacecraft. [0003] The strapdown inertial navigation system does not rely on external information, does no...

Claims

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Application Information

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IPC IPC(8): G01C21/18G01C25/00
CPCG01C21/18G01C25/005
Inventor 黄卫权王刚程建华李景旺丁继成李亮马俊李梦浩崔雅车沣竺
Owner HARBIN ENG UNIV