Öz
In an outdoor environment, the
person's location information is obtained using GPS technology. In an indoor
environment, GPS technology can not be used efficiently because it can not
provide sufficient connectivity to satellites. In order to obtain Indoor localization
information of pedestrians; cameras, infrared, radio frequencies (Bluetooth,
UWB), ultrasonic sensor, cellular communication methods, motion sensors
(accelerometer, gyroscope, magnetometer and compass sensors) are used. In this
study, in an indoor environment, the data obtained from the gyroscope and
acceleration sensor using the motion sensor card (ROZAR IMU M0) were recorded
on the SD card. Furthermore, in indoor environments, the Zero Velocity Update
algorithm (ZUPT) is used to reduce errors originating from sensors in position
determination. A GUI was designed to visualize the pedestrian position by
selecting the correct threshold value.
Anahtar Kelimeler
Kaynakça
- Baniukevic A., Jensen C.S., Lu H., 2013. Hybrid Indoor Positioning with Wi-Fi and Bluetooth: Architecture and Performance, IEEE 14th International Conference on Mobile Data Management, 1, 207 – 216.
- Chen A.T.Y., Fan J., Abhari M. B., Wang K.I.K., 2017. A Computationally Efficient Pipeline for Camera-Based Indoor Person Tracking, International Conference on Image and Vision Computing New Zealand (IVCNZ), 1-6.
- Despaux F., Runser K.J.,Bossche A.V.D., Val T., 2016. Accurate and Platform-agnostic Time-of-Flight Estimation in Ultra-Wide Band, IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 1-7.
- Farid Z., Nordin R., Ismail M., 2013. Recent Advances in Wireless Indoor Localization Techniques and System, Journal of Computer Networks and Communications, 12 (2013).
- Ferro E., Potorti F., 2005. Bluetooth and Wi-Fi Wireless Protocols: A Survey and A Comparison, IEEE Wireless Communications, 12(1), 12-16.
- Jimenez A.R., Seco F., Prieto C. and Guevara J., 2009. A Comparison of Pedestrian Dead-Reckoning Algorithms using a Low-Cost MEMS IMU, 6th IEEE International Symposium on Intelligent Signal Processing, 37-42
- Karabey I., Bayındır L., 2015. Utilization of Room-to-Room Transition Time in Wi-Fi Fingerprint-Based İndoor Localization,International Conference on High Performance Computing & Simulation (HPCS) 318-322.
- Ma M., Song Q.,Li Y. Zhou Z. 2017. A zero velocity intervals detection algorithm based on sensor fusion for indoor pedestrian navigation, IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 418 – 423.
- Mainetti L. Patrono L., Sergi I., 2014. A survey on indoor positioning systems, 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 111 – 120.
- Norrdine A., Kasmi Z., JörgBlankenbach J., 2016. Step Detection for ZUPT-Aided Inertial Pedestrian Navigation System Using Foot-Mounted Permanent Magnet, IEEE Sensors Journal, 16 (17), 6766 – 6773.
- Zhang W., Li X., Wei D., Ji X., Yuan H. 2017. A foot-mounted PDR system based on IMU/EKF+HMM+ZUPT+ZARU+HDR+compass algorithm, International Conference on Indoor Positioning and Indoor Navigation (IPIN), 1-5.
- Thaljaoui A., Val T., Nasri N., Brulin D., 2015. BLE Localization Using RSSI Measurements and iRingLA, IEEE International Conference on Industrial Technology (ICIT), 2178 – 2183.
- Xuebeing Y., Shuia Y., Shengzhi Z., Gouping W., Sheng L., 2015. Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System, Sensors, 15, 780-786.
Öz
Dış ortamlarda, kişinin konum
bilgisi GPS teknolojisi kullanılarak elde edilmektedir. Kapalı ortamlarda GPS
teknolojisi, uydularla yeterli seviyede bağlantı sağlayamadığından dolayı
verimli olarak kullanılamamaktadır. İç ortamlarda yayaların konum bilgisi elde
etmek için; kameralar, infrared, radyo frekansları (Bluetooth, UWB), ultrasonik
sensör, hücresel haberleşme metotları, hareket sensörleri(ivmeölçer, jiroskop)
kullanılmaktadır. Bu çalışmada, kapalı ortamda hareket sensör kartı (ROZAR IMU
M0) kullanılarak jiroskop ve ivme sensörlerinden elde edilen veriler SD kart’a
kaydedilmiştir. Ayrıca kapalı ortamlarda
konum belirlemede sensörlerden kaynaklı hataları azaltmak için hız sıfırlama
algoritması (ZUPT) kullanılmıştır. Doğru eşik değeri seçilerek, yaya konumunu
görselleştirmeye yönelik GUI tasarlanmıştır.
Anahtar Kelimeler
Kaynakça
- Baniukevic A., Jensen C.S., Lu H., 2013. Hybrid Indoor Positioning with Wi-Fi and Bluetooth: Architecture and Performance, IEEE 14th International Conference on Mobile Data Management, 1, 207 – 216.
- Chen A.T.Y., Fan J., Abhari M. B., Wang K.I.K., 2017. A Computationally Efficient Pipeline for Camera-Based Indoor Person Tracking, International Conference on Image and Vision Computing New Zealand (IVCNZ), 1-6.
- Despaux F., Runser K.J.,Bossche A.V.D., Val T., 2016. Accurate and Platform-agnostic Time-of-Flight Estimation in Ultra-Wide Band, IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 1-7.
- Farid Z., Nordin R., Ismail M., 2013. Recent Advances in Wireless Indoor Localization Techniques and System, Journal of Computer Networks and Communications, 12 (2013).
- Ferro E., Potorti F., 2005. Bluetooth and Wi-Fi Wireless Protocols: A Survey and A Comparison, IEEE Wireless Communications, 12(1), 12-16.
- Jimenez A.R., Seco F., Prieto C. and Guevara J., 2009. A Comparison of Pedestrian Dead-Reckoning Algorithms using a Low-Cost MEMS IMU, 6th IEEE International Symposium on Intelligent Signal Processing, 37-42
- Karabey I., Bayındır L., 2015. Utilization of Room-to-Room Transition Time in Wi-Fi Fingerprint-Based İndoor Localization,International Conference on High Performance Computing & Simulation (HPCS) 318-322.
- Ma M., Song Q.,Li Y. Zhou Z. 2017. A zero velocity intervals detection algorithm based on sensor fusion for indoor pedestrian navigation, IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 418 – 423.
- Mainetti L. Patrono L., Sergi I., 2014. A survey on indoor positioning systems, 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 111 – 120.
- Norrdine A., Kasmi Z., JörgBlankenbach J., 2016. Step Detection for ZUPT-Aided Inertial Pedestrian Navigation System Using Foot-Mounted Permanent Magnet, IEEE Sensors Journal, 16 (17), 6766 – 6773.
- Zhang W., Li X., Wei D., Ji X., Yuan H. 2017. A foot-mounted PDR system based on IMU/EKF+HMM+ZUPT+ZARU+HDR+compass algorithm, International Conference on Indoor Positioning and Indoor Navigation (IPIN), 1-5.
- Thaljaoui A., Val T., Nasri N., Brulin D., 2015. BLE Localization Using RSSI Measurements and iRingLA, IEEE International Conference on Industrial Technology (ICIT), 2178 – 2183.
- Xuebeing Y., Shuia Y., Shengzhi Z., Gouping W., Sheng L., 2015. Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System, Sensors, 15, 780-786.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Elektrik Mühendisliği |
| Bölüm | Araştırma Makaleleri \ Research Articles |
| Yazarlar | |
| Yayımlanma Tarihi | 26 Haziran 2019 |
| Gönderilme Tarihi | 2 Ağustos 2018 |
| Kabul Tarihi | 10 Ocak 2019 |
| Yayımlandığı Sayı | Yıl 2019 Cilt: 7 Sayı: 2 |