High-Efficiency Wavelet Compressive Fusion for Improving MEMS Array Performance

Abstract

With the rapid development of microelectromechanical systems (MEMS) technology, low-cost MEMS inertial devices have been widely used for inertial navigation. However, their application range is greatly limited in some fields with high precision requirements because of their low precision and high noise. In this paper, to improve the performance of MEMS inertial devices, we propose a highly efficient optimal estimation algorithm for MEMS arrays based on wavelet compressive fusion (WCF). First, the algorithm uses the compression property of the multiscale wavelet transform to compress the original signal, fusing the compressive data based on the support. Second, threshold processing is performed on the fused wavelet coefficients. The simulation result demonstrates that the proposed algorithm performs well on the output of the inertial sensor array. Then, a ten-gyro array system is designed for collecting practical data, and the frequency of the embedded processor in our verification environment is 800 MHz. The experimental results show that, under the normal working conditions of the MEMS array system, the 100 ms input array data require an approximately 75 ms processing delay when employing the WCF algorithm to support real-time processing. Additionally, the zero-bias instability, angle random walk, and rate slope of the gyroscope are improved by 8.0, 8.0, and 9.5 dB, respectively, as compared with the original device. The experimental results demonstrate that the WCF algorithm has outstanding real-time performance and can effectively improve the accuracy of low-cost MEMS inertial devices.

Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

“…The algorithms studied in this review are of great significance to the refinement of MEMS inertial sensors. This review aims to provide a guide for users studying random error reduction algorithms in the MEMS inertial sensor, as well as according to science and technology developmental trends [ 92 , 93 ], and some hotspots in the research field [ 59 , 63 , 94 , 95 , 96 ]. We also concluded on the following points to better drive the prospects of the algorithms for processing and suppression of random error in MEMS inertial sensors; viz., (1) choosing or improving the appropriate compensation algorithm would depend on accuracy requirements and application scenarios; (2) combination of common algorithms or sensor fusion could improve performance; (3) Smart MEMS inertial sensors integrated with artificial intelligence algorithms could provide better precision of the MEMS inertia sensor.…”

Optimal Compensation of MEMS Gyroscope Noise Kalman Filter Based on Conv-DAE and MultiTCN-Attention Model in Static Base Environment

“…Yingjie Hu [ 14 ] proposed a method combining wavelet denoising with time series analysis, using wavelet denoising to deal with high-frequency noise, followed by time series analysis combined with the Sage-Husa adaptive Kalman filter to remove low-frequency noise. Siyuan Liang [ 15 ] proposed to use the compression characteristics of multi-scale wavelet transform to compress the original signal of MEMS gyroscope, fuse the compressed data according to the support degree, and then perform threshold processing on the fused wavelet coefficients to improve the accuracy of MEMS inertial devices. The harsh environment of actual engineering often limits traditional methods, so machine learning represented by the neural network and support vector machine has also been derived to filter MEMS gyroscopes [ 16 ].…”

Data, Signal and Image Processing and Applications in Sensors

“…Liang et al [ 20 ] propose a highly efficient optimal estimation algorithm for MEMS arrays based on Wavelet Compressive Fusion (WCF), to improve the performance of MEMS inertial devices. The experimental results demonstrate that the WCF algorithm has outstanding real-time performance and can effectively improve the accuracy of low-cost MEMS inertial devices.…”