• Journal of the Korean Society for Aviation and Aeronautics
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• v.21 no.2
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• pp.33-39
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• 2013

Multilateration(MLAT) may complement secondary surveillance radar and also act as a real-time backup for the ADS-B system. This System is using time difference of arrival (TDOA) and based on triangulation principle. Each TDOA measurement defines a hyperbola describing possible aircraft locations. The accuracy in MLAT system depends on the positional relationship of the receiver and aircraft. There are various algorithms to localize aircraft based on TOA estimation. In this paper, we use least square method and extended Kalman filter and compare their results. Study results show that the extend Kalman filter provides a better performance than the least square method.

• Earthquakes and Structures
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• v.11 no.6
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• pp.1143-1164
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• 2016

Time Delay of Arrival (TDOA) estimation methods based on correlation function analysis play an important role in the micro-seismic event monitoring. It makes full use of the similarity in the recorded signals that are from the same source. However, those methods are subjected to the noise effect, particularly when the global similarity of the signals is low. This paper proposes a new approach for micro-seismic monitoring based on cross wavelet transform. The cross wavelet transform is utilized to analyse the measured signals under micro-seismic events, and the cross wavelet power spectrum is used to measure the similarity of two signals in a multi-scale dimension and subsequently identify TDOA. The offset time instant associated with the maximum cross wavelet transform spectrum power is identified as TDOA, and then the location of micro-seismic event can be identified. Individual and statistical identification tests are performed with measurement data from an in-field mine. Experimental studies demonstrate that the proposed approach significantly improves the robustness and accuracy of micro-seismic source locating in mines compared to several existing methods, such as the cross-correlation, multi-correlation, STA/LTA and Kurtosis methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.76-84
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• 2013

In the passive emitter localization using instantaneous TDOA (time difference of arrival) and FDOA (frequency difference of arrival) measurements, the estimation accuracy can be improved by collecting additional measurements. To achieve this goal, it is required to increase the number of the sensors. However, in electronic warfare environment, a large number of sensors cause the loss of military strength due to high probability of intercept. Also, the additional processes should be considered such as the data link and the clock synchronization between the sensors. Hence, in this paper, the passive localization of a stationary emitter is presented by using the successive TDOA and FDOA measurements from two moving sensors. In this case, since an independent pair of sensors is added in the data set at every instant of measurement, each pair of sensors does not share the common reference sensor. Therefore, the QCLS (quadratic correction least squares) methods cannot be applied, in which all pairs of sensor should include the common reference sensor. For this reason, a Gauss-Newton algorithm is adopted to solve the non-linear least square problem. In addition, to show the performance of the proposed method, we compare the RMSE (root mean square error) of the estimates with CRLB (Cramer-Rao lower bound) and derived the CEP (circular error probable) planes to analyze the expected estimation performance on the 2-dimensional space.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.85-91
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• 2013

For location based service and context awareness services, accurate indoor positioning technology is essential. The TDOA method that uses the range difference between signal source and receivers for estimating the location of the signal source, has estimation error due to measurement error. In this paper, a new algorithm is proposed to select the real root among calculated roots using the range difference information, and the estimated position of the signal source shows good accuracy compared to the existing method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.311-318
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• 2021

Underwater, signals are transmitted by sound waves. Sound waves are transmitted through a multipath, either directly or through reflection, due to the variety of underwater environmental characteristics. In such diverse and complex underwater environments, tests must be conducted to determine the extent of the hazard from the survivability and pitfalls of submarines by measuring the underwater radiated noise. Usually, the sound source level measurement of underwater radiated noise should be made within the closest point (CPA: Closest Point of Approach) ± a few meters between the measurement sensor and the submarine. In this study, FDOA and TDOA methods were proposed to estimate the underwater source range. A simulation based on the underwater channel model confirmed the performance of the proposed method.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.5
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• pp.267-275
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• 2017

Time of difference of arrival (TDOA) method using passive sonar sensor array has normally been used to estimate the location of a concealed moving target in underwater environment. Particle filter has been introduced for effective target estimation for non-Gaussian and nonlinear systems. In this paper, we propose a GPU-based acceleration of target position estimation using particle filter and propose efficient embedded system and software architecture. For the TDOA measurement from the passive sonar sensor, we use the generalized cross correlation phase transform (GCC-PHAT) method to obtain the correlation coefficient of the signal using FFT and we try to accelerate the calculation of GCC-PHAT based TDOA measurements using FFT with GPU CUDA. We also propose parallelization method of the target position estimation algorithm using the GPU CUDA to update the state of each particle for the target position estimation using the measured values. The target estimation algorithm was verified using Matlab and implemented using GPU CUDA. Then, we realized the proposed signal processing acceleration system using NVIDIA Jetson TX1 as the target board to analyze in terms of the execution time. The execution time of the algorithm is reduced by 55% to the CPU standalone-operation on the target board. Experiment results show that the proposed architecture is a feasible solution in terms of high-performance and area-efficient architecture.

• 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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.3
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• pp.244-250
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• 2018

Multilateration acquires the transponder signal of target from receivers installed on the ground and calculates the position of the target using the difference of the signal acquisition time of each receiver. One of the factors that influence the positioning accuracy of Multilateration using the TDOA calculation method is the error due to the precision measurement of signal input time. When measuring the signal input time at the receiver, the input signal is sampled using the reference clock of the receiver and a reference sample having the same sampling rate is applied to the cross correlation technique. Therefore, the accuracy of the signal input time is proportional to the reference clock. In this paper, the algorithm for precisely measuring the signal input time by performing cross correlation between the input signal of the receiver and DRS(Delayed Reference Sample) is proposed. In order to verify this, we implemented the pulse signal of the transponder that is transmitted from the target using Matlab. Through the simulation, cross correlation between the proposed DRS and the input signal was performed. From this result, the performance of the precise measurement of signal input time was analyzed.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.9
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• pp.941-946
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• 2011

The Loran-C, a radio navigation system based on TDOA measurements is enhanced to eLoran using TOA measurements instead of TDOA measurements. Many error factors such as PF, SF, ASF, clock errors and unknown biases are included in eLoran TOA measurements. Because these error factors can cause failure in eLoran navigation algorithm, these errors must be compensated for high accuracy eLoran navigation results. Compensation of ASF and unknown biases are difficult to calculate, while the others such as PF and SF are relatively easy to eliminate. In order to compensate all errors in eLoran TOA measurements, a simple GPS aided bias compensation method is suggested in this paper. This method calculates the bias as the difference of TOA measurement and the range between eLoran transmitters and the receiver whose position is determined using GPS. The real data measured in Europe are used for verification of suggested method and navigation algorithm.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.527-539
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• 2023

In the positioning algorithm of two-dimensional planar sensor array, the estimation error of time difference-ofarrival (TDOA) algorithm is difficult to avoid. Thus, how to achieve accurate positioning is a key problem of the positioning technology based on planar array. In this paper, a method of sensor reliability discrimination is proposed, which is the foundation for selecting positioning sensors with small error and excellent performance, simplifying algorithm, and improving positioning accuracy. Then, a positioning model is established. The estimation characteristics of the least square method are fully utilized to calculate and fuse the positioning results, and the non-uniform weighting method is used to correct the weighting factors. It effectively handles the decreased positioning accuracy due to measurement errors, and ensures that the algorithm performance is improved significantly. Finally, the characteristics of the improved algorithm are compared with those of other algorithms. The experiment data demonstrate that the algorithm is better than the standard least square method and can improve the positioning accuracy effectively, which is suitable for vibration detection with large noise interference.