• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.415-421
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• 2009

TOA(time-of-arrival) and TDOA(time-difference-of-arrival) positioning techniques are commonly used in many radio-navigation systems. From the literature, it is known that the position estimate and error covariance matrix of TDOA obtained by GN(Gauss-Newton) method is exactly the same as that of TOA when the error source of the range measurement is only an IID white Gaussian noise. In case of geo-location and indoor positioning, however, multi-path or NLOS(non-line-of-sight) error is frequently appeared in range measurements. Though its occurrence is random, the multipath acts like a bias for a stationary user if it occurs. This paper presents the comparisons of error characteristics between TOA and TDOA positioning in presence of multi-path or NLOS error. It is analytically shown that the position estimate of TDOA is exactly the same as that of TOA even when bias errors are included in range measurements with different magnitudes. By computer simulation, position estimation error and error distribution are analyzed in presence of range bias errors.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.3
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• pp.152-156
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• 2010

Non-line-of-sight (NLOS) propagation is one of the challenges in radio positioning. Distinguishing the transmission status of the communication as line-of-sight (LOS) or NLOS is of great importance for the wireless communication systems. This paper focuses on the identification of NLOS based on time-of-arrival (TOA) distance estimates and the received signal strength (RSS) measurements. We set a path loss threshold based on the joint TOA and RSS based NLOS detection method to determine LOS or NLOS. Simulation results show that the proposed method ensures the correct of detection for the LOS condition and can improve the NLOS identification for the weak noise and long distance.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.437-443
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• 2019

In this paper, a new algorithm for the calculation of the range between an LTE base station (BS) and a user equipment (UE) using time-of-arrival (TOA) measurements of LTE signals is proposed. First, the cell identity (cell ID) of the received signal is acquired using the primary synchronization signal (PSS) and secondary synchronization signal (SSS) to identify the BS transmitted the signal. The proposed algorithm exploits the cell-specific reference signal (CRS), the reference sequence inserted in commercial LTE signals, to estimate the time delay using 2D cross-correlation. The obtained TOA estimations can be used to calculate the range employed from the known BS location. The performance of the proposed algorithm is evaluated with the experiment performed using real LTE signals transmitted from the commercial BS.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.2
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• pp.77-85
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• 2013

this study examined hybrid Time of Arrival/Angle of Arrival (TOA/AOA) localization technique in a cellular network. Based on the linearized equations from the TOA and AOA measurements, the weighted least square (WLS) method is proposed to obtain the location estimation of a mobile station (MS) by analyzing the statistical properties of the error vector in Line of Sight (LOS) and Non-line of Sight (NLOS) environments, respectively. Moreover, the precise expression of the Cramer-Rao lower bound (CRLB) for hybrid TOA/AOA measurements in different LOS/NLOS conditions was derived when the LOS error is a Gaussian variable and the NLOS error is an exponential variable. The idea of cooperative localization is proposed based on the additional information from short-range communication among the MSs in fourth generation (4G) cellular networks. Therefore, the proposed hybrid TOA/AOA WLS method can be improved further with the cooperative scheme. The simulation results show that the hybrid TOA/AOA method has better performance than the TOA only method, particularly when the AOA measurements are accurate. Moreover, the performance of the hybrid TOA/AOA method can be improved further by the cooperative scheme.

• Journal of Positioning, Navigation, and Timing
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• v.7 no.2
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• pp.61-71
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• 2018

Existing radio positioning systems have a drawback that the attitude of user's tag is difficult to be determined. Although forward link angle of arrival (FLAOA) technology that uses measurements of array antenna arranged in a tag among the angle of arrival (AOA) technologies can estimate attitude and positioning of tags, it cannot extend the estimated results into three-dimensional (3D) results due to complex non-linear model displayed because of the effects of 3D positioning and attitude in tags. This paper proposed a radio navigation technique that determines 3D attitude and positioning via FLAOA / time of arrival (TOA) integration. According to the order of determining attitude and positioning, two integration techniques were proposed. To analyze the performance of the proposed technique, MATLAB-based simulations were used to verify the performance. The simulation results showed that the first proposed method, TOA-FLAOA integrated technique, showed about 0.15 m of positioning error, and $2-3^{\circ}$ of attitude error performances regardless of the positioning space size whereas the second method, differenced FLAOA-TOA integrated technique, revealed a problem that a positioning error became larger as the size of the positioning space became larger.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.34-41
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• 2014

Through applying hostile radar signals that use stagger PRI to PRI transform in real time, we can analyze stagger PRI and calculate the future TOA for active cancellation by using measured TOA and estimated PRI. We shows the effect of the errors that are contained in PRI and measured TOA. Then, it will suggest the technique to improve the accuracy of estimated PRI and the TOA averaging method for reducing the effect of measured TOA error. Finally, we will show that accuracy of estimated future TOA that is calculated by proposed scheme is higher than that of future TOA that is simply calculated with TDOA and newest TOA through comparing RMSE performance.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.37 no.10
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• pp.1-8
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• 2000

In this paper, positioning schemes based on AOA(Angle of Arrival), TOA(Time of Arrival), and TDOA(Time Difference of Arrival) measurements are reviewed and analyzed. In the case of using those schemes in microcell structure with severe multipath fading and shadowing conditions, the rapid and unpredictable variation of signal level makes it difficult to estimate the position and velocity of mobiles. Therefore, we propose a novel mobile tracking method based on the multicriteria decision making, in which uncertain parameters such as RSS(Received Signal Strength), the distance between mobile and base station, the moving direction, and the previous location are participated in the decision process using aggregation function in fuzzy set theory. Through a simulation, we analysis the impaction of the frequent change of direction and speed of mobiles.

• Journal of KIISE:Information Networking
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• v.34 no.5
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• pp.370-378
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• 2007

In wireless sensor networks (WSNs), thousands of sensors are often deployed in a hostile environment. In such an environment, WSNs can be applied to various applications by using the absolute or relative location information of the sensors. Until now, the time-of-arrival (TOA) based localization method has been considered most accurate. In the TOA method, however, inaccuracy in distance estimation is caused by clock drift and clock skew between sensor nodes. To solve this problem, several numbers of periodic time synchronization methods were suggested while these methods introduced overheads to the packet traffic. In this paper, we propose a new localization method based on multiple round-trip times (RTOA) of a signal which gives more accurate distance and location estimation even in the presence of clock skew between sensor nodes. Our experimental results show that the Proposed RTOA method gives up to 93% more accurate location estimation.

• ETRI Journal
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• v.37 no.4
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• pp.707-716
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• 2015

The purpose of a localization system is to estimate the coordinates of the geographic location of a mobile device. The accuracy of wireless localization is influenced by nonline-of-sight (NLOS) errors in wireless sensor networks. In this paper, we present an improved time of arrival (TOA)-based localization method for wireless sensor networks. TOA-based localization estimates the geographic location of a mobile device using the distances between a mobile station (MS) and three or more base stations (BSs). However, each of the NLOS errors along a distance measured from an MS (device) to a BS (device) is different because of dissimilar obstacles in the direct signal path between the two devices. To accurately estimate the geographic location of a mobile device in TOA-based localization, we propose an optimized localization method with a BS selection scheme that selects three measured distances that contain a relatively small number of NLOS errors, in this paper. Performance evaluations are presented, and the experimental results are validated through comparisons of various localization methods with the proposed method.

• The Journal of Korea Robotics Society
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• v.7 no.4
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• pp.231-242
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• 2012

This paper proposes particle filter(PF) method using acoustic signal for localization of an underwater robot. The method uses time of arrival(TOA) or time difference of arrival(TDOA) of acoustic signals from beacons whose locations are known. An experiment in towing tank uses TOA information. Simulation uses TDOA information and it reveals dependency of the localization performance on the uncertainty of robot motion and senor data. Also, comparison of the PF method with the least squares method of spherical interpolation(SI) and spherical intersection(SX) is provided. Since PF uses TOA or TDOA which comes from measurement of external information as well as internal motion information, its estimation is more accurate and robust to the sensor and motion uncertainty than the least squares methods.