Different Applications of the Gyroscope Sensors Data Fusion in Distinctive Systems: An Extended Kalman Filter Approach

Abstract

Data fusion systems are applied greatly in the militaries industry, medical equipments and other multi-sensors systems. Here, the practical approaches of data fusion like Kalman filter (KF), support vector machine (SVM) and data fusion for noisy systems are surveyed for the multi-sensors systems. The angular velocity quantum is one of the practical parameters in different systems. The data fusion problem is suggested for the measuring of the angular velocity quantum. For this purpose, two gyroscopes with a same structure of dynamic model and different parameters are utilized. Gussian noises with zero-mean and different variances are applied to both the gyroscopes to assessment the gyroscope sensors data fusion problem. The gyroscope outputs are estimated through the Kalman filter approach. This suggested structures of the sensors data fusion is evaluated for the systems’ outputs. The convergence rate of Kalman filter coefficients and the covariance error are compared among three suggested structures of sensors data fusion. The simulation results survey the effectiveness of gyroscope sensors data fusion such that the obtaining data by using from multi-sensors data fusion is more applicable than the single-sensor.