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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.

Microelectromechanical systems (MEMS) are widely used in the navigation field due to their low cost and easy integration. Its low positioning accuracy restricts its expansion into the high-end navigation field. To improve the performance of MEMS inertial devices, this paper proposes a nested Kalman fusion (NKF) for MEMS gyroscope array data fusion applied to the virtual gyroscope. First, the algorithm processes the raw gyroscope array data through Kalman filtering. Secondly, the obtained filtered array data converge as a virtual gyroscope by support degree data fusion-the NKF experimental data collected by the actual test. The experimental results show the zero-bias instability, angular random walk and rate ramp of the original data are improved by 10.64 dB, 12.45 dB and 10.26 dB, respectively, by the NKF algorithm. NKF can adjust the gyro parameters by about 6 dB in comparison with existing MEMS optimization algorithms.

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