Abstract
The gyro array is a useful technique in improving the accuracy of a micro-electro-mechanical system (MEMS) gyroscope, but the traditional estimate algorithm that plays an important role in this technique has two problems restricting its performance: The limitation of the stochastic assumption and the influence of the dynamic condition. To resolve these problems, a multi-model combined filter with dual uncertainties is proposed to integrate the outputs from numerous gyroscopes. First, to avoid the limitations of the stochastic and set-membership approaches and to better utilize the potentials of both concepts, a dual-noise acceleration model was proposed to describe the angular rate. On this basis, a dual uncertainties model of gyro array was established. Then the multiple model theory was used to improve dynamic performance, and a multi-model combined filter with dual uncertainties was designed. This algorithm could simultaneously deal with stochastic uncertainties and set-membership uncertainties by calculating the Minkowski sum of multiple ellipsoidal sets. The experimental results proved the effectiveness of the proposed filter in improving gyroscope accuracy and adaptability to different kinds of uncertainties and different dynamic characteristics. Most of all, the method gave the boundary surrounding the true value, which is of great significance in attitude control and guidance applications.
Highlights
The advent of micro-electro-mechanical system (MEMS) gyroscopes have added motion sensing in consumer devices and special military applications [1,2,3]
Allan deviation of the errors from the the gyrogyro array. It can be seen from these results that both the multi-model methods and the combined filter using error; IF: Improve factor; angular random walk (ARW): Angular random walk; rate random walk (RRW): Rate random walk; bias instability (BI): Bias instability
A multi-model combined filter allowing for simultaneous treatment of stochastic and set-membership uncertainties was proposed to combine multiple MEMS gyroscopes to improve overall accuracy
Summary
The advent of micro-electro-mechanical system (MEMS) gyroscopes have added motion sensing in consumer devices and special military applications [1,2,3]. The motivation of our research was to design an effective estimate fusion method that could solve the above two difficulties To achieve this goal, a dual-noise acceleration model is presented to describe the rate signal instead of a random walk, and a multi-model combined filter with dual uncertainties is proposed to combine the MEMS gyro array readings. The proposed method in this paper has two advantages, which are the main contributions of this paper: (1) With two types of uncertainty, the proposed combined filter based on sets of densities can capture the true mean and error statistics, but can obtain the guaranteed estimation bounds; and (2) the subfilters in the proposed algorithm can adaptively switch between a known set of models at each sampling instant to respond to the dynamic changes of the system in time.
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