Cargando…
FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter
In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be establ...
Autores principales: | Sun, Jin, Xu, Xiaosu, Liu, Yiting, Zhang, Tao, Li, Yao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970119/ https://www.ncbi.nlm.nih.gov/pubmed/27420062 http://dx.doi.org/10.3390/s16071073 |
Ejemplares similares
-
Advancements in Buoy Wave Data Processing through the Application of the Sage–Husa Adaptive Kalman Filtering Algorithm
por: Jiang, Sha, et al.
Publicado: (2023) -
Radar Target Tracking for Unmanned Surface Vehicle Based on Square Root Sage–Husa Adaptive Robust Kalman Filter
por: Qiao, Shuanghu, et al.
Publicado: (2022) -
SINS/CNS/GNSS Integrated Navigation Based on an Improved Federated Sage–Husa Adaptive Filter
por: Xu, Shuqing, et al.
Publicado: (2019) -
AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal
por: Yang, Gongliu, et al.
Publicado: (2015) -
An Adaptive Multi-Dimensional Vehicle Driving State Observer Based on Modified Sage–Husa UKF Algorithm
por: Luo, Zeyuan, et al.
Publicado: (2020)