• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.04a
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• pp.483-489
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• 2009

The Time Difference of Arrival (TDOA) algorithm is being used widely for identifying the location of a source emanating either electrical or acoustic signal. It's application areas will not be limited to identifying the source at a fixed location, for example the origin of an earthquake, but will also include the trajectory monitoring for a moving source equipped with a GPS sensor. Most of the TDOA algorithm uses time correlation technique to find the time delay between received signals, and therefore difficult to be used for identifying the location of multiple sources. In this paper a TDOA algorithm based on cross-spectrum is developed to find the trajectory of two sound sources with different frequencies. Although its application is limited to for the sources on a disk plane, but it can be applied for identifying the locations of more than two sources simultaneously.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.178-183
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• 2024

This study introduces an enhanced sound source localization technique, bolstered by a data-driven deep learning approach, to improve the precision and accuracy of direction of arrival estimation. Focused on refining Time Difference Of Arrival (TDOA) based sound source localization, the research hinges on accurately estimating TDOA from cross-correlation functions. Accurately estimating the TDOA still remains a limitation in this research field because the measured value from actual microphones are mixed with a lot of noise. Additionally, the digitization process of acoustic signals introduces quantization errors, associated with the sampling frequency of the measurement system, that limit the precision of TDOA estimation. A deep learning-based approach is designed to overcome these limitations in TDOA accuracy and precision. To validate the method, we conduct comprehensive evaluations using both two and three-microphone array configurations. Moreover, the feasibility and real-world applicability of the suggested method are further substantiated through experiments conducted in an anechoic chamber.

• Journal of Convergence for Information Technology
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• v.10 no.9
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• pp.15-21
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• 2020

This paper presents the outlier detection algorithm in the estimation method of a source location and velocity based on two-step weighted least-squares method using time difference of arrival(TDOA) and frequency difference of arrival(FDOA) data. Since the accuracy of the estimated location and velocity of a moving source can be reduced by the outliers of TDOA and FDOA data, it is important to detect and remove the outliers. In this paper, the method to find the minimum inlier data and the method to determine whether TDOA and FDOA data are included in inliers or outliers are presented. The results of numerical simulations show that the accuracy of the estimated location and velocity is improved by removing the outliers of TDOA and FDOA data.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.5
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• pp.886-893
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• 2010

This paper considers the problem of time difference-of-arrival(TDOA) source localization when the TDOA and angle of arrival(AOA) measurements from an airborne emitter source are subject to ground-platform sensor position. The optimization of sensors' position is a challenging problem and a solution with good localization accuracy has yet to be found. This paper proposes an estimator that can achieve these purposes and provides optimized sensor position for good localization accuracy using the proposed estimator. The developed algorithm and sensor position are then examined under the special case of a single airborne source. The theoretical developments are supported by simulations.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.185-190
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• 2007

This article describes a development CDMA LBS in order to apply to "Control System of Underground Infrastructure Fire Accident" which one of U-City Projects of Seoul City. Our goal guides taking shelter of the sufferer it will not be able to use a GPS when the fire occurs from subway station. There are Location measurement methods which measures the AOA(Angle of Arrival) of the signal which it sends with the MS(Mobile station) from the BS(Base station), an electronic delivery time (TOA:Time of Arrival) and the relative difference of electronic arrival time from Base stations (TDOA:Time Difference of Arrival). This time the error due to a multiplex course error and near-far problem and NLOS(Non Line of Sight). We are planning to construct the Test Bed which is an error below 1 meter.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.231-236
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• 2011

In recent years, LBS(Location Based Service) is applied in many different fields. Therefore, various location-aware schemes have been studied. In the location awareness system using time dependent algorithm, TOA(Time of Arrival) or TDOA(Time Difference of Arrival) algorithm, distortion of a signal by AWGN(Additive White Gaussian Noise) and multi-path effects cause the degradation of location awareness performance. In this paper, the unexpected noise is eliminated by averaging multiple pulses in order to overcome the degradation of performance. Also, we research the technique for improving the performance of the location awareness by detecting direct-path signal with adjusting threshold.

• Journal of Communications and Networks
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• v.15 no.3
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• pp.245-251
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• 2013

This paper addresses the issue of time-difference-of-arrival (TDOA) estimation for complex noncircular signals. First, under the wide-sense stationary assumption, we derive the maximum likelihood (ML) estimator and the Cramer-Rao lower bound for Gaussian noncircular signals in Gaussian circular noise. The ML estimator uses the second-order statistics information of a noncircular signal more comprehensively when compared with the cross-correlation (CC) and the conjugate CC estimators. Further, we present a scheme to modify the traditional signal-selective TDOA methods for noncircular signals on the basis of the cyclostationarity of man-made signals. This scheme simultaneously exploits the information contained in both the cyclic cross-correlation (CCC) and the conjugate CCC of a noncircular signal.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1069-1080
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• 2016

TDOA(Time Difference Of Arrival)-based localization system such as the passive surveillance system performs the time synchronization between the receivers after separated installing multiple receivers to set the same clock for all receivers. And it estimates 2D(or 3D) location of the target by solving intersection of the multiple hyperbola(or hyperboloid) using TDOA. To perform time synchronization, one receiver must be set to the master, and it provide the reference data to compensate the clock of the rest of the slaves. The positioning accuracy of TDOA-based localization system is changed in accordance with the master that is selected among multiple receivers. So, the optimum receiver which is selected among multiple receivers must be set to master to get best performance in the considered deployment of receivers. In this paper, we propose a selection scheme of master receiver for time synchronization using DOP(Dilution Of Precision) which is based on location of the target and the multiple receivers. The proposed scheme has low complexity and short processing time, and it is easy to automate in the TDOA-based localization systems.

• Journal of electromagnetic engineering and science
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• v.18 no.2
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• pp.94-100
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• 2018

Partial discharges (PDs) are electrical sparks that occur inside insulation between two conducting electrodes and can lead to the disastrous failure of insulation systems. To determine the location of a PD, a distributed array of UHF PD sensors is used to detect the electromagnetic (EM) signals emitted from the PD source, and the localization of the PD source can be estimated using the time difference of arrival (TDOA) between EM signals captured by the UHF PD sensor array. There are four popular methods to estimate the TDOA-the first peak method, the cross-correlation method, the energy criterion method, and the average time window threshold method. In this work, we numerically investigate the influence of noise on estimating the TDOA for the four different methods. Numerical results show that the energy criterion method is more robust against noise than other methods.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.7
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• pp.710-718
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• 2009

The time difference of arrival(TDOA) algorithm is being used widely for identifying the location of a source emanating either electrical or acoustic signal. It's application areas will not be limited to identifying the source at a fixed location, for example the origin of an earthquake, but will also include the trajectory monitoring for a moving source equipped with a GPS sensor. Most of the TDOA algorithm uses time correlation technique to find the time delay between received signals, and therefore difficult to be used for identifying the location of multiple sources. In this paper a TDOA algorithm based on cross-spectrum is developed to find the trajectory of two sound sources with different frequencies. Although its application is limited to for the sources on a disk plane, it can be applied for identifying the locations of more than two sources simultaneously.