scholarly journals Wavelet-Based Moment-Matching Techniques for Inertial Sensor Calibration

2020 ◽  
Vol 69 (10) ◽  
pp. 7542-7551 ◽  
Author(s):  
Stephane Guerrier ◽  
Juan Jurado ◽  
Mehran Khaghani ◽  
Gaetan Bakalli ◽  
Mucyo Karemera ◽  
...  
Keyword(s):  
Inertial Sensor ◽  
Sensor Calibration ◽  
Moment Matching ◽  
Matching Techniques

Discussion on Maximum Likelihood-Based Methods for Inertial Sensor Calibration

2016 ◽  
Vol 16 (14) ◽  
pp. 5522-5523 ◽  
Author(s):  
Stephane Guerrier ◽  
Roberto Molinari ◽  
James Balamuta
Keyword(s):  
Maximum Likelihood ◽  
Inertial Sensor ◽  
Sensor Calibration

scholarly journals Canoeing Motion Tracking and Analysis via Multi-Sensors Fusion

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2110 ◽  
Author(s):  
Long Liu ◽  
Sen Qiu ◽  
ZheLong Wang ◽  
Jie Li ◽  
JiaXin Wang
Keyword(s):  
Motion Tracking ◽  
Learning Algorithm ◽  
Inertial Sensor ◽  
Recovery Phase ◽  
Descent Method ◽  
Sensor Nodes ◽  
Sensor Calibration ◽  
Gradient Descent Method ◽  
Whole Process ◽  
Training Methodologies

Coaches and athletes are constantly seeking novel training methodologies in an attempt to improve athletic performance. This paper proposes a method of rowing sport capture and analysis based on Inertial Measurement Units (IMUs). A canoeist’s motion was collected by multiple miniature inertial sensor nodes. The gradient descent method was used to fuse data and obtain the canoeist’s attitude information after sensor calibration, and then the motions of canoeist’s actions were reconstructed. Stroke quality was performed based on the estimated joint angles. Machine learning algorithm was used as the classification method to divide the stroke cycle into different phases, including propulsion-phase and recovery-phase, a quantitative kinematic analysis was carried out. Experiments conducted in this paper demonstrated that our method possesses the capacity to reveal the similarities and differences between novice and coach, the whole process of canoeist’s motions can be analyzed with satisfactory accuracy validated by videography method. It can provide quantitative data for coaches or athletes, which can be used to improve the skills of rowers.

Scale-free PSO for in-run and infield inertial sensor calibration

Measurement ◽  
2019 ◽  
Vol 147 ◽  
pp. 106849
Author(s):  
Shashi Poddar ◽  
Amod Kumar
Keyword(s):  
Inertial Sensor ◽  
Sensor Calibration ◽  
Scale Free

scholarly journals Impacts of Residual Stress on Micro Vibratory Platform Used for Inertial Sensor Calibration

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3959
Author(s):  
Rui Hao ◽  
Huijun Yu ◽  
Bei Peng ◽  
Haixiang Zhan ◽  
Wu Zhou
Keyword(s):  
Residual Stress ◽  
Lead Zirconate Titanate ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Lead Zirconate ◽  
Micro Electro Mechanical System ◽  
Sensor Calibration ◽  
Long Term Storage ◽  
Piezoelectric Effects ◽  
Abnormal Phenomenon

A micro vibratory platform driven by converse piezoelectric effects is a promising in-situ recalibration platform to eliminate the influence of bias and scale factor drift caused by long-term storage of micro-electro–mechanical system (MEMS) inertial sensors. The calibration accuracy is critically determined by the stable and repeatable vibration of platform, and it is unavoidably impacted by the residual stress of micro structures and lead zirconate titanate (PZT) hysteresis. The abnormal phenomenon of the observed displacement response in experiments was investigated analytically using the stiffness model of beams and hysteresis model of piezoelectric material. Rather than the hysteresis, the initial deflection formed by the residual stress of the beam was identified as the main cause of the response error around the zero position. This conclusion provides guidelines to improve the performance and control of micro vibratory platforms.

A Comprehensive Overview of Inertial Sensor Calibration Techniques

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Shashi Poddar ◽  
Vipan Kumar ◽  
Amod Kumar
Keyword(s):  
High Precision ◽  
Kalman Filtering ◽  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Measurement Unit ◽  
Sensor Calibration ◽  
Comprehensive Overview ◽  
Inertial Measurement ◽  
Calibration Techniques

Inertial measurement unit (IMU) comprising of the accelerometer and gyroscope is prone to various deterministic errors like bias, scale factor, and nonorthogonality, which need to be calibrated carefully. In this paper, a survey has been carried out over different calibration techniques that try to estimate these error parameters. These calibration schemes are discussed under two broad categories, that is, calibration with high-end equipment and without any equipment. Traditional calibration techniques use high-precision equipment to generate references for calibrating inertial sensors and are generally laboratory-based setup. Inertial sensor calibration without the use of any costly equipment is further studied under two subcategories: ones based on multiposition method and others with Kalman filtering framework. Later, a brief review of vision-based inertial sensor calibration schemes is also provided in this work followed by a discussion which indicates different shortcomings and future scopes in the area of inertial sensor calibration.

Sign in / Sign up

Export Citation Format

Share Document