Kalman and wavelet fused MEMS gyroscope self-adaptive anti-outlier denoising method

A Kalman filter and gyroscope technology, applied in CAD numerical modeling, design optimization/simulation, etc., can solve problems such as zero bias instability, MEMS gyroscope signal angle random walk, etc., to improve accuracy, reduce errors, Effect of suppressing low-frequency noise

Active Publication Date: 2020-04-10
CHINA THREE GORGES UNIV
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  • Claims
  • Application Information

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Problems solved by technology

However, angular random walk and bias instability in MEMS gyroscope signals are often in low frequency components

Method used

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  • Kalman and wavelet fused MEMS gyroscope self-adaptive anti-outlier denoising method
  • Kalman and wavelet fused MEMS gyroscope self-adaptive anti-outlier denoising method
  • Kalman and wavelet fused MEMS gyroscope self-adaptive anti-outlier denoising method

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Embodiment Construction

[0042] Such as figure 1 It is shown that the MEMS gyroscope adaptive anti-outlier denoising method that combines Kalman and wavelet includes the following steps,

[0043] Step 1: Build a time-series ARMA model of the MEMS gyroscope signal:

[0044] Extract the trend item and period item in the original signal of the MEMS gyroscope, and then establish a time series ARMA model for its residual, and use the final forecast error FPE criterion to determine the order of the model, and obtain the random drift error model of the MEMS gyroscope as the AR(1) model ,Expressed as:

[0045] m k+1 =am k +ξ k+1

[0046] where m k ,m k+1 Respectively represent t k moment and tk+1 Gyroscope error at time, a is the autoregressive coefficient, ξ k+1 for t k+1 momentary white noise;

[0047] Step 2: Denoise the gyroscope signal by using Kalman filter;

[0048] Step 2.1: Establish the state equation and measurement equation of the Kalman filter according to the AR(1) model obtained in ...

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Abstract

The invention discloses a Kalman and wavelet fused MEMS gyroscope adaptive anti-outlier denoising method. The method comprises the following steps: establishing a time sequence model of gyroscope signals; de-noising the gyroscope signal by adopting Kalman filtering; performing threshold processing on the low-frequency component and the high-frequency component of the gyroscope signal after Kalmanfiltering by using wavelet analysis; wavelet reconstruction is carried out on the high-frequency signals and the low-frequency signals of the gyroscope signals after threshold processing. The invention provides an adaptive anti-outlier denoising scheme integrating Kalman filtering and wavelet for MEMS gyroscope signals, the precision of the sensor can be more effectively improved, and errors are reduced.

Description

technical field [0001] The invention belongs to the field of sensor signal processing, and in particular relates to a method for self-adaptive anti-outlier value denoising of MEMS gyroscopes fused with Kalman and wavelet. Background technique [0002] The MEMS (Microelectro Mechanical Systems) sensor produced by Microelectronics Technology is a new type of miniature inertial sensor, which is widely used in low-cost strapdown inertial navigation systems. Before using the MEMS gyroscope to measure data, in order to reduce its random drift error and improve sensor accuracy, it needs to be denoised. [0003] For MEMS gyroscope signal denoising, Kalman filter or wavelet analysis is generally used. Kalman filter calculation is simple and easy to handle, but in the actual process, due to the influence of various factors, the filter will diverge. The weight suppresses the divergence of the filter, and the interference data such as outliers in the signal will also affect the accurac...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/20G06F111/10
CPCY02T10/40
Inventor 冉昌艳吴佳慧
Owner CHINA THREE GORGES UNIV
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