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为提高MEMS陀螺的精度,提出了一种基于最优定界椭球(OBE)的平滑算法,并将其用于陀螺阵列信号的处理。首先,利用多个相同型号的MEMS陀螺构成阵列,测量同一角速率信号,并建立数据融合模型。对于融合问题而言,噪声统计特性的不确定会导致传统融合方法精度下降。为解决该问题,引入仅要求噪声未知但有界的集员估计理论,结合RTS平滑思想,提出一种新的平滑算法作为融合方法,它由前向滤波和反向平滑两个过程构成:前者采用集员估计理论中的OBE滤波估计角速率,后者则逆序执行OBE算法进一步提高估计精度。实验表明:该方法能够将陀螺的静态漂移由0.5130(°)/s降低到0.1368(°)/s;动态条件下,在有效跟踪载体角度变化的同时,将漂移由0.5343(°)/s降低到0.1704(°)/s,显著提高了陀螺的使用精度。
In order to improve the accuracy of MEMS gyroscope, a smoothing algorithm based on Optimal Bounding Ellipsoid (OBE) is proposed and applied to the gyroscope signal processing. First, using multiple MEMS gyroscopes of the same type to form an array, measuring the same angular rate signal and establishing a data fusion model. For the fusion problem, the uncertainty of the statistical characteristics of noise will lead to the decline of the accuracy of the traditional fusion method. In order to solve this problem, we introduce a set-estimation theory which requires only unknown noise but boundedness. Combining with the idea of RTS smoothing, a new smoothing algorithm is proposed as a fusion method, which consists of forward filtering and reverse smoothing. The former OBE algorithm is used to estimate the angular rate by using the OBE filter in the set-head estimation theory. The latter is used to further improve the estimation accuracy. The experimental results show that this method can reduce the static drift of the gyro from 0.5130 ° / s to 0.1368 ° / s, and reduce the drift from 0.5343 ° / s while effectively tracking the change of carrier angle under dynamic conditions. To 0.1704 (°) / s, significantly improving the accuracy of the use of the gyro.