Signal Processing Technique for Combining Numerous MEMS Gyroscopes Based on Dynamic Conditional Correlation

Abstract

Abstract:A signal processing technique is presented to improve the angular rate accuracy of Micro-Electro-Mechanical System (MEMS) gyroscope by combining numerous gyroscopes. Based on the conditional correlation between gyroscopes, a dynamic data fusion model is established. Firstly, the gyroscope error model is built through Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process to improve overall performance. Then the conditional covariance obtained through dynamic conditional correlation (DCC) estimator is used to describe the correlation quantitatively. Finally, the approach is validated by a prototype of the virtual gyroscope, which consists of six-gyroscope array. The experimental results indicate that the weights of gyroscopes change with the value of error. Also, the accuracy of combined rate signal is improved dramatically compared to individual gyroscope. The results indicate that the approach not only improves the accuracy of the MEMS gyroscope, but also discovers the fault gyroscope and eliminates its influence.

Redundant information processing techniques comparison for differential vibratory gyroscope

“…Based on carried out researches, the possibility to use the known algorithm of "virtual" gyro in differential CVG was revealed. This algorithm was grounded in [9] and applied to an array of four one-axis unidirectional gyros created on the basis of MEMS gyro [7]. This algorithm is designed for redundant information processing.…”

“…Integration of redundant information about angle rate, for example, by means of Kalman filter [3,4], and extended Kalman filter [5] in complicated cases leads to the necessity of mathematical modelling of the measured angle rate. For example, the angle rate model in [6] is represented by a Markov process of the first order, and the general auto-regression model conditionally heteroscedastic model of the angle rate is used in [7]. The latter takes into consideration the temperature model of the gyro drift.…”

Devising and introducing a procedure for measuring a dynamic stabilization error in weapon stabilizers

“…Mathematical modeling was carried out on the SVU-500 digital stabilizers with the use of electromechanical gyro tachometers (SVU-500-4C) and the modern Coriolis vibrating gyroscopes [13][14][15][16] SVU-500-7C. Based on the results, it was determined that the closest value of the dynamic error to that stated in the specifications for 2Е52 (≤2 t. d.) would be derived in the case of its determining when sending a sinusoidal signal to the point after the sensor's angular velocity output.…”

Improved Morphological Filter Based on Variational Mode Decomposition for MEMS Gyroscope De-Noising

“…Noise contained in the original signal of these low-cost gyroscopes degrades its accuracy and limits its applications. Consequently, signal noise reduction is essential to enhance their performance [ 5 , 6 , 7 , 8 , 9 ]. Kalman filter (KF) is a representative algorithm for gyroscope de-noising for practical inertial navigation and integrated navigation application [ 10 ], however, the filter models and noise characteristics will influence the performance easily [ 11 ].…”