Indoor Positioning Based on LTE Carrier Phase Measurements and an Inertial Measurement Unit

2018 ◽  
Author(s):  
Ali A. Abdallah ◽  
Kimia Shamaei ◽  
Zak M. Kassas
Keyword(s):  
Carrier Phase ◽  
Inertial Measurement Unit ◽  
Indoor Positioning ◽  
Measurement Unit ◽  
Phase Measurements ◽  
Inertial Measurement

Survey Boat Attitude Determination with GPS/IMU Systems

2001 ◽  
Vol 54 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Reha Metin Alkan ◽  
Orhan Baykal
Keyword(s):  
Carrier Phase ◽  
Inertial Measurement Unit ◽  
Attitude Determination ◽  
Measurement Unit ◽  
Phase Measurements ◽  
Inertial Measurement ◽  
The Third ◽  
Hydrographic Surveying ◽  
Echo Sounder

Any vehicle such as a vessel or aeroplane has three attitude parameters. These are most commonly defined as pitch, roll and heading from true north. In hydrographic surveying, determination of these parameters is essential for the correction of multi-beam echo sounder measurements. In the study on which this paper is based, two of the three parameters, the pitch and roll angles, were measured with an inexpensive IMU (Inertial Measurement Unit) device. The third was calculated from GPS carrier phase measurements that were collected from two antennas on the boat. The GPS antennas depart from the vertical when they are subject to pitch and roll effects. In this case, the coordinates derived from GPS will be erroneous. Thus, if heading is to be calculated accurately, the horizontal coordinates have to be corrected for pitch and roll. This paper defines an algorithm for the purpose that enables pitch and roll angles to be measured with an accuracy of about 0·003 arcdeg and headings computed with an accuracy of about 0·010 arcdeg.

scholarly journals RTK/Pseudolite/LAHDE/IMU-PDR Integrated Pedestrian Navigation System for Urban and Indoor Environments

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1791
Author(s):  
Ruihui Zhu ◽  
Yunjia Wang ◽  
Hongji Cao ◽  
Baoguo Yu ◽  
Xingli Gan ◽  
...  
Keyword(s):  
Real Time ◽  
High Precision ◽  
Navigation System ◽  
Carrier Phase ◽  
Inertial Measurement Unit ◽  
Dead Reckoning ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Inertial Measurement ◽  
Pedestrian Navigation

This paper presents an evaluation of real-time kinematic (RTK)/Pseudolite/landmarks assistance heuristic drift elimination (LAHDE)/inertial measurement unit-based personal dead reckoning systems (IMU-PDR) integrated pedestrian navigation system for urban and indoor environments. Real-time kinematic (RTK) technique is widely used for high-precision positioning and can provide periodic correction to inertial measurement unit (IMU)-based personal dead reckoning systems (PDR) outdoors. However, indoors, where global positioning system (GPS) signals are not available, RTK fails to achieve high-precision positioning. Pseudolite can provide satellite-like navigation signals for user receivers to achieve positioning in indoor environments. However, there are some problems in pseudolite positioning field, such as complex multipath effect in indoor environments and integer ambiguity of carrier phase. In order to avoid the limitation of these factors, a local search method based on carrier phase difference with the assistance of IMU-PDR is proposed in this paper, which can achieve higher positioning accuracy. Besides, heuristic drift elimination algorithm with the assistance of manmade landmarks (LAHDE) is introduced to eliminate the accumulated error in headings derived by IMU-PDR in indoor corridors. An algorithm verification system was developed to carry out real experiments in a cooperation scene. Results show that, although the proposed pedestrian navigation system has to use human behavior to switch the positioning algorithm according to different scenarios, it is still effective in controlling the IMU-PDR drift error in multiscenarios including outdoor, indoor corridor, and indoor room for different people.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

scholarly journals Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4767
Author(s):  
Karla Miriam Reyes Leiva ◽  
Milagros Jaén-Vargas ◽  
Benito Codina ◽  
José Javier Serrano Olmedo
Keyword(s):  
Inertial Measurement Unit ◽  
Visually Impaired ◽  
Inertial Sensors ◽  
Assistive Technologies ◽  
Biomechanical Analysis ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Visually Impaired People ◽  
Impaired People ◽  
Application Fields

A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

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