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A multi-MEMS sensors information fusion algorithm

机译：多MEMS传感器信息融合算法

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摘要

With the development of new technologies and changes in market demand, MEMS gyroscope is widely used, but the low accuracy, large drift and other defects limit their application in some areas. This paper presents a Kalman filter-based multi-sensor fusion algorithm to improve the positioning and tracking accuracy of MEMS devices. The technology of multi-sensor information fusion has been put into use in the military defense and other fields. Information fusion can obtain a more precise system estimate on the basis of multiple independent data. There are many methods of information fusion, of which Kalman filter is the most popular. The multi-sensor information fusion on the basis of Kalman filter focus on measuring integration and state integration. The paper firstly introduces the fundamental models of Kalman filter algorithm, and then puts forward the two improved algorithm. With the advantages of these two methods, a new fusion algorithm has come out, and simulation verification has been designed. The results show that, the proposed fusion algorithm can improve the fusion accuracy and ensure the stability, so that help MEMS devices to satisfy the demands of practical applications.
机译：随着新技术的发展和市场需求的变化，MEMS陀螺仪得到了广泛的应用，但其精度低，漂移大等缺陷限制了它们在某些领域的应用。本文提出了一种基于卡尔曼滤波器的多传感器融合算法，以提高MEMS器件的定位和跟踪精度。多传感器信息融合技术已经在军事防御等领域得到了应用。信息融合可以在多个独立数据的基础上获得更精确的系统估计。信息融合的方法很多，其中最流行的是卡尔曼滤波器。基于卡尔曼滤波器的多传感器信息融合主要集中在测量积分和状态积分上。本文首先介绍了卡尔曼滤波算法的基本模型，然后提出了两种改进算法。利用这两种方法的优点，提出了一种新的融合算法，并设计了仿真验证方法。结果表明，所提出的融合算法可以提高融合精度，保证稳定性，从而有助于MEMS器件满足实际应用的需求。

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