• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.923-930
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• 2018

In the wireless communication system based on an array antenna, the angle of arrival (AOA) information of signal is very important element and various AOA estimation algorithms have been studied. Although most AOA estimation algorithms employ the uniform linear array (ULA), some algorithms apply the planar array (PA) antenna. In this paper, we present an algorithm for efficiently estimating AOAs of adjacent multiple signals, based on the uniform rectangular array antenna. This approach has two steps; after approximately estimating AOA groups consisting of the closely located signal sources using CAPON, accurately estimating the individual AOA of each signal in the estimated AOA group using Beamsapce MUSIC. The estimation performance of the presented cascade AOA algorithm is illustrated through the computer simulation example.

• The Journal of the Korea Contents Association
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• v.3 no.2
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• pp.11-18
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• 2003

In this paper, we propose a fast variable block matching algorithm for motion estimation based on bit-patterns. Motion estimation in the proposed algorithm is peformed after the representation of image sequence is transformed 8-bit pixel values into 1-bit ones by the mean pixel value of search block, which brings a short searching time by reducing the computational complexity. Moreover, adaptive searching methods according to the motion information of the block make the procedure of motion estimation efficient by eliminating unnecessary searching processes of low motion block and deepening a searching procedure in high motion block. Experimental results show that the proposed algorithm provides bettor performance - average 0.5dB PSNR improvement and about 99% savings in the number of operations - than full search Hock matching algorithm with a fixed block size.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1528-1534
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• 2019

Recently, due to the popularization of 3D depth cameras, new researches and opportunities have been made in research conducted on RGB images, but estimation of human hand pose is still classified as one of the difficult topics. In this paper, we propose a robust estimation method of human hand pose from various input 3D depth images using a learning algorithm. The proposed approach first generates a skeleton-based hand model and then aligns the generated hand model with three-dimensional point cloud data. Then, using a random forest-based learning algorithm, the hand pose is strongly estimated from the aligned hand model. Experimental results in this paper show that the proposed hierarchical approach makes robust and fast estimation of human hand posture from input depth images captured in various indoor and outdoor environments.

• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.176-185
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• 2008

In this paper, new filtering method for sensor registration is provided to estimate and correct error of registration parameters in multiple sensor environments. Sensor registration is based on filtering method to estimate registration parameters in multiple sensor environments. Accuracy of sensor registration can increase performance of data fusion method selected. Due to various error sources, the sensor registration has registration errors recognized as multiple objects even though multiple sensors are tracking one object. In order to estimate the error parameter, new nonlinear information filtering method is developed using minimum mean square error estimation. Instead of linearization of nonlinear function like an extended Kalman filter, information estimation through unscented prediction is used. The proposed method enables to reduce estimation error without a computation of the Jacobian matrix in case that measurement dimension is large. A computer simulation is carried out to evaluate the proposed filtering method with an extended Kalman filter.

• 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.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.2 no.4
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• pp.17-27
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• 2003

This paper presents the channel estimation method with STBC applied to IEEE802.11a. Tenacious channel estimation method for OFDM system, that generally uses STTC, has been presented once and massive amount of calculation was needed However, regarding this paper, the amount of calculation can be reduced by using Curve Fitting, and channel tracking performance improvement can be achieved Based on the estimation method, computer simulation was done for the performance analysis and delay spread Doppler frequency and many other group sizes and numbers were considered As a result of the testing, we found out that Doppler frequency deviation effects equally as frequency offset. Also compared to the existing technique, channel estimated performance confirmed that have 0.9dB SNR improvement than old method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.40-52
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• 2020

In this paper, we consider a precoder design problem for multiuser interference channel. Most of the conventional schemes for precoder design utilize a signal-to-interference-plus-noise ratio (SINR) as a cost function. However, since the SINR metric of a desired transmitter-receiver pair is a function of precoding vectors of all transmitters in the multiuser interference channel, an analytic closed-form solution is not available for the precoding vector of a desired transmitter that maximizes the SINR metric. To eliminate coupling between the precoding vectors of all transmitters and to find a closed-form solution for the precoding vector of the desired transmitter, we use a signal-to-leakage-plus-noise ratio (SLNR) instead as a cost function because the SLNR at a transmitter is a function of the precoding vector of the desired transmitter only. In addition, channel estimation errors for undesired links are considered when designing the precoding vector because they are inevitable in a multiuser interference channel. In this case, we propose a design scheme for the precoding vector that is robust to the channel estimation error. In the proposed scheme, the precoding vector is designed to maximize the worst-case SLNR. Through computer simulation, we show that the proposed scheme has better performance than the conventional scheme in terms of SLNR, SINR, and sum rate of all users.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.10
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• pp.1874-1881
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• 2010

A recursive linear stochastic error compensation algorithm is newly proposed for target localization in sensor network which provides range difference of arrival(RDOA) measurements. Target localization with RDOA is a well-known nonlinear estimation problem. Since it can not solve with a closed-form solution, the numerical methods sensitive to initial guess are often used before. As an alternative solution, a pseudo-linear estimation scheme has been used but the auto-correlation of measurement noise still causes unacceptable estimation errors under low SNR conditions. To overcome these problems, a stochastic error compensation method is applied for the target localization problem under the assumption that a priori stochastic information of RDOA measurement noise is available. Apart from the existing methods, the proposed linear target localization scheme can recursively compute the target position estimate which converges to true position in probability. In addition, it is remarked that the suggested algorithm has a structural reconciliation with the existing one such as linear correction least squares(LCLS) estimator. Through the computer simulations, it is demonstrated that the proposed method shows better performance than the LCLS method and guarantees fast and reliable convergence characteristic compared to the nonlinear method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.4103-4117
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• 2014

Image processing and computer vision algorithms are gaining larger concern in a variety of application areas such as robotics and man-machine interaction. Vision allows the development of flexible, intelligent, and less intrusive approaches than most of the other sensor systems. In this work, we determine the location and orientation of a mobile robot which is crucial for performing its tasks. In order to be able to operate in real time there is a need to speed up different vision routines. Therefore, we present and evaluate a method for introducing parallelism into the multiple non-overlapping camera pose estimation algorithm proposed in [1]. In this algorithm the problem has been solved in real time using multiple non-overlapping cameras and the Extended Kalman Filter (EKF). Four cameras arranged in two back-to-back pairs are put on the platform of a moving robot. An important benefit of using multiple cameras for robot pose estimation is the capability of resolving vision uncertainties such as the bas-relief ambiguity. The proposed method is based on algorithmic skeletons for low, medium and high levels of parallelization. The analysis shows that the use of a multiprocessor system enhances the system performance by about 87%. In addition, the proposed design is scalable, which is necaccery in this application where the number of features changes repeatedly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8B
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• pp.1383-1390
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• 2000

Pilot symbol-based coherent detection is used to increase the link capacity in CDMA systems. In order to detect data efficiently, data signals must be compensated with channel estimation values obtained by using pilot symbols. Many channel estimation schemes were published in previous literatures. However, most of these schemes have performance deterioration in fast fading because of using average vaules of several timeslots. In this paper, we propose an improved channel estimation scheme which is efficient in fast fading and also operates in slow fading satisfactorily. Through computer simulation we show that the proposed scheme has better performance than other schemes in terms of both MSE and BER in fast fading and show robustness for variation in Doppler frequencies.