Signal Processing of MEMS Gyroscope Arrays to Improve Accuracy Using a 1st Order Markov for Rate Signal Modeling

Abstract

This paper presents a signal processing technique to improve angular rate accuracy of the gyroscope by combining the outputs of an array of MEMS gyroscope. A mathematical model for the accuracy improvement was described and a Kalman filter (KF) was designed to obtain optimal rate estimates. Especially, the rate signal was modeled by a first-order Markov process instead of a random walk to improve overall performance. The accuracy of the combined rate signal and affecting factors were analyzed using a steady-state covariance. A system comprising a six-gyroscope array was developed to test the presented KF. Experimental tests proved that the presented model was effective at improving the gyroscope accuracy. The experimental results indicated that six identical gyroscopes with an ARW noise of 6.2 °/√h and a bias drift of 54.14 °/h could be combined into a rate signal with an ARW noise of 1.8 °/√h and a bias drift of 16.3 °/h, while the estimated rate signal by the random walk model has an ARW noise of 2.4 °/√h and a bias drift of 20.6 °/h. It revealed that both models could improve the angular rate accuracy and have a similar performance in static condition. In dynamic condition, the test results showed that the first-order Markov process model could reduce the dynamic errors 20% more than the random walk model.

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

“…And the detailed results are illustrated in Table 3. The simple average and the Kalman filter in [8,9,11] are competitors. In this paper, the accuracy means the closeness between the results of measurement and the true angular rate, which can be represented by the amount of measurements errors.…”

“…Compared with other models, the mean of virtual gyroscope combined by DCC model is reduced to 0.0047 °/s, so that the performance is also improved, and the improvement factor of the accuracy is about 5.17. simple average Kalman filter [8] Kalman filter [9] Kalman filter [11] dcc Figure 9. Allan variance results of the virtual gyro compared to the single gyro.…”

“…They also adopted a novel optimal Kalman filter to combine the gyroscopes [8]. Their further study used a first-order Markov process to model the rate signal to improve the performance in dynamic condition [11].…”

“…Al-Majed and Alsuwaidan presented a multi-filters adaptive estimator to improve the angular rate accuracy by using the noise correlation in [7] but with no simulation or experimental results. Jiang and Xue used six-gyro array in [8][9][10][11]. They also adopted a novel optimal Kalman filter to combine the gyroscopes [8].…”

Dynamic Performance Comparison of Two Kalman Filters for Rate Signal Direct Modeling and Differencing Modeling for Combining a MEMS Gyroscope Array to Improve Accuracy

“…The number N of component sensors in a gyroscope array can be selected as any integer, and from the results of [7,14], it demonstrated that the performance of a KF can be further improved through increasing number N of the component sensors. Additionally, the KF will show the best performance while the correlation factor ρ approaches −1/( N − 1), thus it will not need a large negative correlation factor to obtain the highest accuracy improvement by increasing the number N , which reduces the requirement of a negative correlation factor and is suitable for system implementation.…”