國立虎尾科技大學 |

Wireless Channel Characterization and Modeling for Indoor Time-of-Arrival Localization.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Wireless Channel Characterization and Modeling for Indoor Time-of-Arrival Localization./
作者:	Latinovic, Zoran.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	119 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-08, Section: B.
Contained By:	Dissertations Abstracts International80-08B.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13425180
ISBN:	9780438815162

Wireless Channel Characterization and Modeling for Indoor Time-of-Arrival Localization.
Latinovic, Zoran.

Wireless Channel Characterization and Modeling for Indoor Time-of-Arrival Localization. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 119 p.

Source: Dissertations Abstracts International, Volume: 80-08, Section: B.

Thesis (Ph.D.)--New York University Tandon School of Engineering, 2019.

This item must not be sold to any third party vendors.

Reliable and accurate positioning of objects and users is an important feature for emergency calls in cellular networks, localization based services, industrial applications, robotics and emerging internet of things (IoT) technology. In this work we focused on time-of-arrival (ToA) because it provides, in general, more accurate location metrics compared to other methods such as received signal strength indicator (RSSI) or angle-of-arrival (AoA). The Global Navigation Satellite Systems (GNSS) provide very accurate positioning for large number of outdoor receivers but they are ineffective indoors due to attenuated satellite signals inside buildings and more challenging indoor environment. Unfortunately, there are no general indoor channel models relevant for localization in the sub-6GHz bands suitable for an Observed Time Difference of Arrival (OTDoA) system. We executed an indoor measurement campaign to statistically characterize the bias in ToA estimates incurred by multipath and non line of sight (NLOS) reflections in 5.5 GHz industrial, scientific and medical (ISM) band. Bandwidths of the used waveforms are 10 and 100 MHz, which are close to the bandwidths of Long-Term Evolution (LTE) signal and emerging sub-6GHz 5G standards. This measurement scenario is particularly applicable to indoor localization with micro or femto cell infrastructure. Delay of the direct path between the transmitter and receiver is known accurately as a result of precisely synchronized equipment and reliably measured distance between the omnidirectional antennas. Measurements in three buildings with different propagation conditions are performed to analyze ToA statistics in wide range of indoor environments, from partitioned offices over typical cubical space to very open cafeteria mall area. We demonstrate that WINNER II and similar channel models designed for communications are not suitable for localization applications as they do not provide absolute delays. Furthermore, to reduce complexity, such models assume constant root mean square delay spread over distance, which is one of the main contributors to the ToA errors. Other physical aspects of the wireless channel such as specular components or reflections in line of sight (LOS) and random bias in NLOS scenario are also not considered in those channel models. An extension of the WINNER II model is proposed that is closer to practical indoor radio channels and provides more realistic localization performance. It was noticed from measurements that strong reflections are often present in LOS conditions and corridor environments. These reflections introduce significant mean delay spread offset that reduces ranging accuracy. Delay difference between the geometrical direct path and first detectable arrival in NLOS conditions creates additional delay offset, also known as the NLOS bias. The LOS and NLOS modifications that address those flaws are implemented in a generic way that is applicable to any other indoor or outdoor channel model. Finally, validity of the extended WINNER II model and its applicability on localization is demonstrated by a good match between the ToA error statistics obtained from extended WINNER II channel model simulations and ToA errors calculated from the measurements. This allows credible simulations for accurate prediction of ToA ranging errors and furthermore system level simulations of different ToA/TDoA localization algorithms.

ISBN: 9780438815162Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Channel modeling for localization

Wireless Channel Characterization and Modeling for Indoor Time-of-Arrival Localization.
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502 $a Thesis (Ph.D.)--New York University Tandon School of Engineering, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Reliable and accurate positioning of objects and users is an important feature for emergency calls in cellular networks, localization based services, industrial applications, robotics and emerging internet of things (IoT) technology. In this work we focused on time-of-arrival (ToA) because it provides, in general, more accurate location metrics compared to other methods such as received signal strength indicator (RSSI) or angle-of-arrival (AoA). The Global Navigation Satellite Systems (GNSS) provide very accurate positioning for large number of outdoor receivers but they are ineffective indoors due to attenuated satellite signals inside buildings and more challenging indoor environment. Unfortunately, there are no general indoor channel models relevant for localization in the sub-6GHz bands suitable for an Observed Time Difference of Arrival (OTDoA) system. We executed an indoor measurement campaign to statistically characterize the bias in ToA estimates incurred by multipath and non line of sight (NLOS) reflections in 5.5 GHz industrial, scientific and medical (ISM) band. Bandwidths of the used waveforms are 10 and 100 MHz, which are close to the bandwidths of Long-Term Evolution (LTE) signal and emerging sub-6GHz 5G standards. This measurement scenario is particularly applicable to indoor localization with micro or femto cell infrastructure. Delay of the direct path between the transmitter and receiver is known accurately as a result of precisely synchronized equipment and reliably measured distance between the omnidirectional antennas. Measurements in three buildings with different propagation conditions are performed to analyze ToA statistics in wide range of indoor environments, from partitioned offices over typical cubical space to very open cafeteria mall area. We demonstrate that WINNER II and similar channel models designed for communications are not suitable for localization applications as they do not provide absolute delays. Furthermore, to reduce complexity, such models assume constant root mean square delay spread over distance, which is one of the main contributors to the ToA errors. Other physical aspects of the wireless channel such as specular components or reflections in line of sight (LOS) and random bias in NLOS scenario are also not considered in those channel models. An extension of the WINNER II model is proposed that is closer to practical indoor radio channels and provides more realistic localization performance. It was noticed from measurements that strong reflections are often present in LOS conditions and corridor environments. These reflections introduce significant mean delay spread offset that reduces ranging accuracy. Delay difference between the geometrical direct path and first detectable arrival in NLOS conditions creates additional delay offset, also known as the NLOS bias. The LOS and NLOS modifications that address those flaws are implemented in a generic way that is applicable to any other indoor or outdoor channel model. Finally, validity of the extended WINNER II model and its applicability on localization is demonstrated by a good match between the ToA error statistics obtained from extended WINNER II channel model simulations and ToA errors calculated from the measurements. This allows credible simulations for accurate prediction of ToA ranging errors and furthermore system level simulations of different ToA/TDoA localization algorithms.
590 $a School code: 1988.
650 4 $a Electrical engineering. $3 596380
653 $a Channel modeling for localization
653 $a Indoor localization
653 $a Ranging
653 $a Time of arrival
653 $a Toa bias statistics
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710 2 $a New York University Tandon School of Engineering. $b Electrical and Computer Engineering. $3 1190679
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792 $a 2019
793 $a English
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