Cargando…
Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions
Micro-electro-mechanical system inertial measurement unit (MEMS-IMU), a core component in many navigation systems, directly determines the accuracy of inertial navigation system; however, MEMS-IMU system is often affected by various factors such as environmental noise, electronic noise, mechanical n...
Autores principales: | Han, Shipeng, Meng, Zhen, Zhang, Xingcheng, Yan, Yuepeng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7923423/ https://www.ncbi.nlm.nih.gov/pubmed/33672478 http://dx.doi.org/10.3390/mi12020214 |
Ejemplares similares
-
A MEMS IMU De-Noising Method Using Long Short Term Memory Recurrent Neural Networks (LSTM-RNN)
por: Jiang, Changhui, et al.
Publicado: (2018) -
Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review
por: Han, Shipeng, et al.
Publicado: (2020) -
Navigation Grade MEMS IMU for A Satellite
por: Zhao, Wanliang, et al.
Publicado: (2021) -
An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions
por: Angelucci, Alessandra, et al.
Publicado: (2023) -
Performance Analysis of a Deep Simple Recurrent Unit Recurrent Neural Network (SRU-RNN) in MEMS Gyroscope De-Noising
por: Jiang, Changhui, et al.
Publicado: (2018)