| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000607883 |
| ISSN: | 1405-5546 |
| Autores: | Naderolasli, Amir1 Shirzadfar, Hamidreza1 |
| Instituciones: | 1Shahid Ashrafi Esfahani University, Faculty of Engineering and Technology, Isfahan. Irán |
| Año: | 2024 |
| Periodo: | Ene-Mar |
| Volumen: | 28 |
| Número: | 1 |
| Paginación: | 189-196 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | 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 are surveyed for the noisy multi-sensors systems.The angular velocity quantum is one of the practical parameters in the different systems in which the data fusion problem is suggested for the measuring of them. For this purpose, two gyroscopes with a same structure of dynamic model and different parameters are utilized that the Gussian noises with zero-mean and different variances are applied to both of them to assessment the gyroscope sensors data fusion problem. The gyroscope outputs are estimated through the Kalman filter approach. This suggested structure 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 obtained data by using multi-sensors is more applicable than a single-sensor. |
| Keyword: | Data fusion, Kalman filter (KF), Support vector machine (SVM), Angular velocities, Gyroscope sensors |
| Texto completo: | Texto completo (Ver PDF) Texto completo (Ver HTML) |
