Abstract
High-G MEMS accelerometer (HGMA) is widely used in the aerospace field and the precise control of missiles. Therefore, its calibration accuracy is critical to sensor performance and the overall control system. In order to decrease the influence of noise on the HGMA output signal, a hybrid denoising algorithm which is based on the Time-frequency peak filtering (TFPF), Local mean decomposition (LMD) and Sample entropy (SE) has been proposed in this article. For the problem that the TFPF algorithm is limited in the choice of window length, LMD and SE are used to distinguish components, which can improve the TFPF algorithm effectively. It provides a better balance between noise reduction and signal fidelity. Firstly, the noise-containing signal can be decomposed by LMD to obtain PFs. Secondly, calculate the sample entropy values of each PFs, then divide the signal into mixed component, useful component and noise component according to the similarity of sample entropy. Thirdly, the mixed component can use long-window TFPF to reduce noise, the short-window TFPF can reduce the noise for the useful component, and the noise component can be wiped off directly. Finally, the useful component and the mixed component are both reconstructed to form the final denoised signal. Experiments have showed that this method can not only remove noise (the noise of static signal is reduced by 91.76%, the signal-noise ratio of dynamic signal has increased to 17.6), but also retain the details of frequency and amplitude (the shock peak amplitude error is 0.062% and the vibration amplitude error is 0.04%). Therefore, this method can reduce the noise of the High-G MEMS accelerometer signal with maintaining the characteristics of the original signal, thereby greatly improves the performance of the accelerometer, making it widely used.
Highlights
Since the MEMS inertial sensors are widely used in the aerospace, weapon equipment and consumer electronics fields [1]–[6], the High-G MEMS accelerometer (HGMA)The associate editor coordinating the review of this article and approving it for publication was Flavia Grassi.has come to a new type of sensor developed by microelectronics and micromachining technology
HIGH-G MEMS ACCELEROMETER The original signal collected in this paper is from the High-G MEMS accelerometer (HGMA) which is a newly designed and manufactured sensor
The noise of static signal is reduced by 91.76%, which can prove that the random noise of the signal is significantly reduced by the Local mean decomposition (LMD)-Sample entropy (SE)-Time-frequency peak filtering (TFPF) method
Summary
Since the MEMS inertial sensors are widely used in the aerospace, weapon equipment and consumer electronics fields [1]–[6], the High-G MEMS accelerometer (HGMA). Has come to a new type of sensor developed by microelectronics and micromachining technology. It has the advantages of light weight, small size, high integration density, high reliability, etc. Due to the inevitable causes of hardware circuits and sensors, the HGMA output signal contains a lot of noise, which could seriously affect the accuracy of the calibration. Z. Yan et al.: MEMS Accelerometer Calibration Denoising Method for Hopkinson Bar System reducing the noise of the HGMA output signal can improve the its performance [7], [8]
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