Mobile Positioning and Tracking Based on TOA/TSOA/TDOA/AOA with NLOS-Reduced Distance Measurements

Wei-Kai CHAO; Kuen-Tsair LAY

doi:10.1093/ietcom/e90-b.12.3643

Mobile Positioning and Tracking Based on TOA/TSOA/TDOA/AOA with NLOS-Reduced Distance Measurements

Wei-Kai CHAO, Kuen-Tsair LAY

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In this paper, we address the issue of mobile positioning and tracking after measurements have been made on the distances and possibly directions between an MS (mobile station) and its nearby base stations (BS's). The measurements can come from the time of arrival (TOA), the time sum of arrival (TSOA), the time difference of arrival (TDOA), and the angle of arrival (AOA). They are in general corrupted with measurement noise and NLOS (non-line-of-sight) error. The NLOS error is the dominant factor that degrades the accuracy of mobile positioning. Assuming specific statistic models for the NLOS error, however, we propose a scheme that significantly reduces its effect. Regardless of which of the first three measurement types (i.e. TOA, TSOA, or TDOA) is used, the proposed scheme computes the MS location in a mathematically unified way. We also propose a method to identify the TOA measurements that are not or only slightly corrupted with NLOS errors. We call them nearly NLOS-error-free TOA measurements. From the signals associated with TOA measurements, AOA information can be obtained and used to aid the MS positioning. Finally, by combining the proposed MS positioning method with Kalman filtering, we propose a scheme to track the movement of the MS.

Publication: IEICE TRANSACTIONS on Communications Vol.E90-B No.12 pp.3643-3653

Publication Date: 2007/12/01

Publicized

Online ISSN: 1745-1345

DOI: 10.1093/ietcom/e90-b.12.3643

Type of Manuscript: PAPER

Category: Navigation, Guidance and Control Systems

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Wei-Kai CHAO, Kuen-Tsair LAY, "Mobile Positioning and Tracking Based on TOA/TSOA/TDOA/AOA with NLOS-Reduced Distance Measurements" in IEICE TRANSACTIONS on Communications, vol. E90-B, no. 12, pp. 3643-3653, December 2007, doi: 10.1093/ietcom/e90-b.12.3643.
Abstract: In this paper, we address the issue of mobile positioning and tracking after measurements have been made on the distances and possibly directions between an MS (mobile station) and its nearby base stations (BS's). The measurements can come from the time of arrival (TOA), the time sum of arrival (TSOA), the time difference of arrival (TDOA), and the angle of arrival (AOA). They are in general corrupted with measurement noise and NLOS (non-line-of-sight) error. The NLOS error is the dominant factor that degrades the accuracy of mobile positioning. Assuming specific statistic models for the NLOS error, however, we propose a scheme that significantly reduces its effect. Regardless of which of the first three measurement types (i.e. TOA, TSOA, or TDOA) is used, the proposed scheme computes the MS location in a mathematically unified way. We also propose a method to identify the TOA measurements that are not or only slightly corrupted with NLOS errors. We call them nearly NLOS-error-free TOA measurements. From the signals associated with TOA measurements, AOA information can be obtained and used to aid the MS positioning. Finally, by combining the proposed MS positioning method with Kalman filtering, we propose a scheme to track the movement of the MS.
URL: https://globals.ieice.org/en_transactions/communications/10.1093/ietcom/e90-b.12.3643/_p

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@ARTICLE{e90-b_12_3643,
author={Wei-Kai CHAO, Kuen-Tsair LAY, },
journal={IEICE TRANSACTIONS on Communications},
title={Mobile Positioning and Tracking Based on TOA/TSOA/TDOA/AOA with NLOS-Reduced Distance Measurements},
year={2007},
volume={E90-B},
number={12},
pages={3643-3653},
abstract={In this paper, we address the issue of mobile positioning and tracking after measurements have been made on the distances and possibly directions between an MS (mobile station) and its nearby base stations (BS's). The measurements can come from the time of arrival (TOA), the time sum of arrival (TSOA), the time difference of arrival (TDOA), and the angle of arrival (AOA). They are in general corrupted with measurement noise and NLOS (non-line-of-sight) error. The NLOS error is the dominant factor that degrades the accuracy of mobile positioning. Assuming specific statistic models for the NLOS error, however, we propose a scheme that significantly reduces its effect. Regardless of which of the first three measurement types (i.e. TOA, TSOA, or TDOA) is used, the proposed scheme computes the MS location in a mathematically unified way. We also propose a method to identify the TOA measurements that are not or only slightly corrupted with NLOS errors. We call them nearly NLOS-error-free TOA measurements. From the signals associated with TOA measurements, AOA information can be obtained and used to aid the MS positioning. Finally, by combining the proposed MS positioning method with Kalman filtering, we propose a scheme to track the movement of the MS.},
keywords={},
doi={10.1093/ietcom/e90-b.12.3643},
ISSN={1745-1345},
month={December},}

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TY - JOUR
TI - Mobile Positioning and Tracking Based on TOA/TSOA/TDOA/AOA with NLOS-Reduced Distance Measurements
T2 - IEICE TRANSACTIONS on Communications
SP - 3643
EP - 3653
AU - Wei-Kai CHAO
AU - Kuen-Tsair LAY
PY - 2007
DO - 10.1093/ietcom/e90-b.12.3643
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E90-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2007
AB - In this paper, we address the issue of mobile positioning and tracking after measurements have been made on the distances and possibly directions between an MS (mobile station) and its nearby base stations (BS's). The measurements can come from the time of arrival (TOA), the time sum of arrival (TSOA), the time difference of arrival (TDOA), and the angle of arrival (AOA). They are in general corrupted with measurement noise and NLOS (non-line-of-sight) error. The NLOS error is the dominant factor that degrades the accuracy of mobile positioning. Assuming specific statistic models for the NLOS error, however, we propose a scheme that significantly reduces its effect. Regardless of which of the first three measurement types (i.e. TOA, TSOA, or TDOA) is used, the proposed scheme computes the MS location in a mathematically unified way. We also propose a method to identify the TOA measurements that are not or only slightly corrupted with NLOS errors. We call them nearly NLOS-error-free TOA measurements. From the signals associated with TOA measurements, AOA information can be obtained and used to aid the MS positioning. Finally, by combining the proposed MS positioning method with Kalman filtering, we propose a scheme to track the movement of the MS.
ER -