• Journal of Advanced Navigation Technology
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• v.24 no.1
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• pp.47-52
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• 2020

Direction of arrival (DoA) estimation is a scheme of estimating the directions of targets by analyzing signals generated or reflected from the targets and is used in various fields. Artificial neural networks (ANN) is a field of machine learning that mimics the neural network of living organisms. They show good performance in pattern recognition. Although researches has been using ANNs to estimate the DoAs, there are limitationsin dealing with variations of the signal-to-noise ratio (SNR) of the target signals. In this paper, we propose a three-stage ANN algorithm for DoA estimation. The proposed algorithm can minimize the performance reduction by applying the model trained in a single SNR environment to various environments through a 'noise reduction process'. Furthermore, the scheme reduces the difficulty in learning and maintains efficiency in estimation, by employing a process of DoA shift. We compare the performance of the proposed algorithm with Cramer-Rao bound (CRB) and the performances of existing subspace-based algorithms and show that the proposed scheme exhibits better performance than other schemes in some severe environments such as low SNR environments or situations in which targets are located very close to each other.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.359-369
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• 2005

Delay estimation is a difficult Problem in computer networks. Accurate one-way delay estimation is crucial because it serves a very important role in network performance and thus application design. RTT(Round Trip Time) is often used as an approximation of the delay, but because it is a sum of the forward and reverse delays, the actual one-way delay cannot be estimated accurately from RTT. To estimate one-way delay accurately, this paper proposes a new scheme that analytically derives one-way delay, forward and reverse delay respectively. We show that the performance of TCP can improve dramatically in asymmetric networks using our scheme. A key contribution of this paper is that our one-way deiay estimation is much more accurate than RTT estimation so that TCP can quickly find the network capacity in the slow start phase. Since RTT is the sum of the forward and reverse delays, our scheme can be applied to any protocol that is based on RTT.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.7A
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• pp.641-650
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• 2002

Space-Time Block Code(STBC) provides full diversity gains with simple linear processing at the receiver. Interference Cancellation(IC) techniques in system using STBC improve the capacity and performance of wireless systems with co-channel users. Various IC techniques, Minimum Mean-Squared Error(MMSE) and Zero-Forcing(ZF) algorithms in system with STBC were proposed in the literatures in multiuser environment. The performance of these IC techniques were simulated by assuming perfect channel state information(CSI) of multiuser at the receiver. However, in practice it is difficult to know perfect CSI of multiuser at the receiver. Thus, channel estimation scheme is essential at the receiver. Also SNR estimation scheme is required to operate the MMSE IC algorithm. In this paper, we present estimation schemes of CSI and SNR using training sequences. Through extensive computer simulation, we compare and evaluate the performance of IC techniques using the proposed CSI and SNR estimation techniques.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.1C
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• pp.138-144
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• 2009

This paper proposes an efficient scheme to estimate the channel parameters such as channel gain and delay for the IEEE802.15.4a LR-UWB systems. Sliding window (SW) method is generally used for the channel estimation of LR-UWB systems, which extracts the channel parameters by performing the cross-correlation with the repeatedly transmitted signal. However, the SW method experiences the severe performance degradation because the cross-correlation is performed just once for the received signal. In this paper, we propose a novel channel estimation scheme, which can achieve a great performance gain by performing the cross-correlation repeatedly with the repeated receive signal. In order to verify the performance gain of the proposed scheme, we performed the intensive simulation with the Saleh-Valenzuela channel model. Simulation results show that the proposed scheme has a performance improvement of 4dB compared to the conventional SW channel estimation scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5C
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• pp.351-360
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• 2008

In cellular mobile radio systems with frequency reuse, the interference signals degrade the channel estimation and signal detection performance due to the low signal-to-interference ratio near coverage boundaries. When the preamble pilot sequences from different cells are orthogonal or located in disjointed positions, they can be used for multi-cell channel estimation and interference cancellation. In time-varying channels caused by Doppler spread, data pilot symbols are needed for channel estimations. However, data pilot symbols are usually located in identical positions for the overhead reduction, which degrades the channel estimation performance. In this paper, we demonstrate a significant amount of performance improvement is achieved by multiplying different pseudonoise(PN) sequences to the data pilot symbols from adjacent interference cells. In particular, for detection scheme using maximal ratio combining(MRC) and inter-cell spatial demultiplexing(ISD), quantitative performance gain of spectral efficiency for different values of Doppler frequency and interference power is presented.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.1
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• pp.133-143
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• 2021

In government procurement programs, cost estimation and analysis support funding decisions and are the basis for other major decisions, too. Such estimating and analyzing the cost of the weapon systems are crucial in execution of the defense budget. However, existing cost estimations and analyses have focused on domestic R&D projects, thus those are not valid in application to foreign weapon acquisitions. This study aims at foreign weapon systems that are acquired from Direct Commercial Sales. Because the data for price estimation of a foreign weapon is usually not available, we suggest a price estimation model based on performance factors of the weapon. In this study, the proper price of the weapon system is estimated using the parametric cost estimating model. Using the data of helicopter-launched anti-tank guided missiles worldwide, we analyze the effect of each performance factor on the weapon system price by regression analysis, and use step-wise and ridge regression analysis to remove multi-collinearity. This study hopefully contributes to more reasonable decision making on proper price of weapons.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.305-311
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• 2006

In this paper the performance of a array invariant method is evaluated for source-range estimation in horizontally stratified shallow water ocean waveguide. The method has advantage of little computationally effort over existing source-localization methods. such as matched field processing or the waveguide invariant and array gain is fully exploited. And. no knowledge of the environment is required except that the received field should not be dominated by purely interference This simple and instantaneous method is applied to simulated acoustic propagation filed for testing range estimation performance. The result of range estimation according to the SNR for the underwater impulsive source with broadband spectrum is demonstrated. The spatial smoothing method is applied to suppress the effect of mutipath propagation by high frequency signal. The result of performance test for range estimation shows that the error rate is within 20% at the SNR above 10dB.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.606-614
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• 2012

A practical recursive linear robust estimation scheme is proposed for target localization in the sensor network which provides range difference of arrival (RDOA) measurements. In order to radically solve the known practical difficulties such as sensitivity for initial guess and heavy computational burden caused by intrinsic nonlinearity of the RDOA based target localization problem, an uncertain linear measurement model is newly derived. In the suggested problem setting, the target localization performance of the conventional linear estimation schemes might be severely degraded under the low SNR condition and be affected by the target position in the sensor network. This motivates us to devise a new sensor network localization algorithm within the framework of the recently developed robust least squares estimation theory. Provided that the statistical information regarding RDOA measurements are available, the estimate of the proposition method shows the convergence in probability to the true target position. Through the computer simulations, the omnidirectional target localization performance and consistency of the proposed algorithm are compared to those of the existing ones. It is shown that the proposed method is more reliable than the total least squares method and the linear correction least squares method.

• The Journal of the Acoustical Society of Korea
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• v.36 no.6
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• pp.394-400
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• 2017

This paper shows the extension of SpSF (Sparse Spectrum Fitting) algorithm, which is one of covariance matrix fitting-based DOA (Direction-of-Arrival) estimation algorithms, from the time domain to the frequency domain, and presents that SpSF can be implemented in the frequency domain. The superiority of the SpSF algorithm has been demonstrated by comparing DOA estimation performance with the performance of Conventional DOA estimation algorithm in the frequency domain for sinusoidal incident signals.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.220-225
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• 2020

This paper investigates the non-intrusive speech intelligibility estimation method in noise environments when the bottleneck feature of autoencoder is used as an input to a neural network. The bottleneck feature-based method has the problem of severe performance degradation when the noise environment is changed. In order to overcome this problem, we propose a novel non-intrusive speech intelligibility estimation method that adds the noise environment information along with bottleneck feature to the input of long short-term memory (LSTM) neural network whose output is a short-time objective intelligence (STOI) score that is a standard tool for measuring intrusive speech intelligibility with reference speech signals. From the experiments in various noise environments, the proposed method showed improved performance when the noise environment is same. In particular, the performance was significant improved compared to that of the conventional methods in different environments. Therefore, we can conclude that the method proposed in this paper can be successfully used for estimating non-intrusive speech intelligibility in various noise environments.