scholarly journals Attitude Heading Reference System Using MEMS Inertial Sensors with Dual-Axis Rotation

Sensors ◽  
2014 ◽  
Vol 14 (10) ◽  
pp. 18075-18095 ◽  
Author(s):  
Li Kang ◽  
Lingyun Ye ◽  
Kaichen Song ◽  
Yang Zhou
Keyword(s):  
Reference System ◽  
Inertial Sensors ◽  
Axis Rotation

Single-Axis Rotation Modulation of SINS

2013 ◽  
Vol 313-314 ◽  
pp. 643-646 ◽  
Author(s):  
Yu Liang Mao ◽  
Jia Bin Chen ◽  
Chun Lei Song ◽  
Jing Yuan Yin
Keyword(s):  
Theoretical Analysis ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Rotation Speed ◽  
Rotation Axis ◽  
Constant Bias ◽  
Rotation Scheme ◽  
Axis Rotation ◽  
Single Axis Rotation

In order to effectively inhibit the influence of constant bias of inertial sensors on strapdown inertial navigation system accuracy, a self-compensation method based on single-axis rotation modulation is proposed. It is shown by theoretical analysis that, the gyro constant drift and zero errors of accelerometer perpendicular to the rotation axis can be modulated to sinusoidal signal, which will be eliminated by integral. The constant bias along the rotation axis cannot be modulated. The effectiveness of the rotation scheme is proved by experiment and the influence of rotation speed is demonstrated.

Assimilation-error in tool use as a question of reference system

2011 ◽  
Author(s):  
S. Ladwig ◽  
C. Sutter ◽  
J. Musseler ◽  
K. Wendler ◽  
F. Bade
Keyword(s):  
Tool Use ◽  
Reference System

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

A Preliminary Framework and Feasibility Study for Spacecraft Disturbance Source Detection and Isolation with Distributed Inertial Sensors

2021 ◽  
Author(s):  
Adam Augustyniak ◽  
David J. Hanley ◽  
Timothy W. Bretl ◽  
Neil J. Hejmanowski ◽  
David L. Carroll
Keyword(s):  
Feasibility Study ◽  
Inertial Sensors ◽  
Source Detection ◽  
Disturbance Source
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