Modelling and Optimization of Inertial Sensor-Accelerometer

2017 ◽  
pp. 331-345 ◽  
Author(s):  
Zakriya Mohammed ◽  
Waqas Amin Gill ◽  
Mahmoud Rasras
Keyword(s):  
Inertial Sensor ◽  
Modelling And Optimization

Vision and Inertial Sensor Fusion for Terrain Relative Navigation

2021 ◽  
Author(s):  
Andrew Verras ◽  
Roshan Thomas Eapen ◽  
Andrew B. Simon ◽  
Manoranjan Majji ◽  
Ramchander Rao Bhaskara ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Inertial Sensor ◽  
Relative Navigation

scholarly journals Modelling and optimization of information systems with distributed databases

2021 ◽  
Vol 1155 (1) ◽  
pp. 012062
Author(s):  
I Ya Lvovich ◽  
Ya E Lvovich ◽  
A P Preobrazhenskiy ◽  
Yu P Preobrazhenskiy ◽  
O N Choporov
Keyword(s):  
Information Systems ◽  
Distributed Databases ◽  
Modelling And Optimization

scholarly journals Displacement Estimation Based on Optical and Inertial Sensor Fusion

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1390
Author(s):  
Tomasz Ursel ◽  
Michał Olinski
Keyword(s):  
Sensor Fusion ◽  
Inertial Sensor ◽  
External Disturbance ◽  
Linear Displacement ◽  
Measuring System ◽  
Displacement Estimation ◽  
Mems Accelerometer ◽  
Pedestrian Navigation ◽  
Complementary Filter ◽  
Optical Flow Sensor

This article aims to develop a system capable of estimating the displacement of a moving object with the usage of a relatively cheap and easy to apply sensors. There is a growing need for such systems, not only for robots, but also, for instance, pedestrian navigation. In this paper, the theory for this idea, including data postprocessing algorithms for a MEMS accelerometer and an optical flow sensor (OFS), as well as the developed complementary filter applied for sensor fusion, are presented. In addition, a vital part of the accelerometer’s algorithm, the zero velocity states detection, is implemented. It is based on analysis of the acceleration’s signal and further application of acceleration symmetrization, greatly improving the obtained displacement. A test stand with a linear guide and motor enabling imposing a specified linear motion is built. The results of both sensors’ testing suggest that the displacement estimated by each of them is highly correct. Fusion of the sensors’ data gives even better outcomes, especially in cases with external disturbance of OFS. The comparative evaluation of estimated linear displacements, in each case related to encoder data, confirms the algorithms’ operation correctness and proves the chosen sensors’ usefulness in the development of a linear displacement measuring system.

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