國立虎尾科技大學 |

Indoor Localization Using Inertial Navigation Systems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Indoor Localization Using Inertial Navigation Systems./
Author:	Ahmad, Touqeer.
Description:	1 online resource (67 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-05.
Contained By:	Masters Abstracts International57-05(E).
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355744088

Indoor Localization Using Inertial Navigation Systems.
Ahmad, Touqeer.

Indoor Localization Using Inertial Navigation Systems. - 1 online resource (67 pages)

Source: Masters Abstracts International, Volume: 57-05.

Thesis (Master's)--Stevens Institute of Technology, 2017.

Includes bibliographical references

Localizing objects in an environment is very important as a lot of tasks depend upon the presence of objects at certain points. Outdoor localization can be easily done using global navigation satellite system (GNSS). This method does not ensure certain accuracy for an indoor environment. A host of indoor localization techniques are available to improve accuracy. Most of them involve installing certain equipment (transmitter and receiver nodes). This thesis investigates an approach based on an inertial measurement unit (IMU) that doesn't involve any equipment installation and can track objects in an indoor environment with precision. The IMU used includes 3-axis accelerometer, 3-axis gyroscope, and 3-axis magnetometer. The accelerometer and gyroscope measure multiple forces that act on the sensor making the measured data noisy. Different filters including 6th order lowpass Butterworth filter, denoised filter, and median filter are used to filter out the noise without effecting the shape of the original signal. The magnetometer measures the magnetic field in all directions providing the absolute magnetic north. All sensors are calibrated to eliminate any bias including acceleration due to gravity measurement by the accelerometer at rest, zero angular velocity by the gyroscope, and heading correctness for the magnetometer.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355744088Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Indoor Localization Using Inertial Navigation Systems.
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245 1 0 $a Indoor Localization Using Inertial Navigation Systems.
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300 $a 1 online resource (67 pages)
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500 $a Source: Masters Abstracts International, Volume: 57-05.
500 $a Advisers: Yu-Dong Yao; Kevin W. Lu.
502 $a Thesis (Master's)--Stevens Institute of Technology, 2017.
504 $a Includes bibliographical references
520 $a Localizing objects in an environment is very important as a lot of tasks depend upon the presence of objects at certain points. Outdoor localization can be easily done using global navigation satellite system (GNSS). This method does not ensure certain accuracy for an indoor environment. A host of indoor localization techniques are available to improve accuracy. Most of them involve installing certain equipment (transmitter and receiver nodes). This thesis investigates an approach based on an inertial measurement unit (IMU) that doesn't involve any equipment installation and can track objects in an indoor environment with precision. The IMU used includes 3-axis accelerometer, 3-axis gyroscope, and 3-axis magnetometer. The accelerometer and gyroscope measure multiple forces that act on the sensor making the measured data noisy. Different filters including 6th order lowpass Butterworth filter, denoised filter, and median filter are used to filter out the noise without effecting the shape of the original signal. The magnetometer measures the magnetic field in all directions providing the absolute magnetic north. All sensors are calibrated to eliminate any bias including acceleration due to gravity measurement by the accelerometer at rest, zero angular velocity by the gyroscope, and heading correctness for the magnetometer.
520 $a A complimentary filter is used to estimate the orientation of the object by fusing the data from the accelerometer, gyroscope, and magnetometer. Static acceleration components (gravity and static noises) are removed from its orientation. The Kalman filter is used to predict the position of the object using the dynamic acceleration and dead reckoning technique. GPS data are also incorporated to provide an initial position and reduce the chances of drift caused by dead reckoning.
520 $a The algorithm was tested in real-time, the raw data were logged for different motions and implemented in MATLAB to predict the position of the object. At the end, algorithm was implemented on data collected from three different IMU devices including standalone MPU9255 sensor, iPhone 7, and Jackal Robot. The proposed algorithm shows similar position accuracy compare to other indoor tracking techniques that require equipment installation.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Engineering. $3 561152
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
690 $a 0537
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Stevens Institute of Technology. $b Electrical. $3 1194232
773 0 $t Masters Abstracts International $g 57-05(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10685367 $z click for full text (PQDT)