• The Journal of the Acoustical Society of Korea
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• v.11 no.3
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• pp.53-60
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• 1992

Many adaptive beamforming methods have been studied for interference cancellation and speech signal enhancement in telephone conference and auditorium. Main aspect of adaptive beamforming methods for speech signal processing is different from radar, sonar and seismic signal processing because desire output signal should be apt to the human ear. Considering that phase of speech is quite insensible to the human ear, Sondhi proposed a nonlinear constrained optimization technique whose constraint was on the magnitude transfer function from the source to the output. In real environment the phase response of the speech signal affects the human auditorium system. So it is desirable to design linear phase system. In this paper, linear phase beamformer is proposed and sample processing algorithm is also proposed for real time consideration Simulation results show that the proposed algorithm yields more consistent beam patterns and deep nulls to the noise direction than Sondhi's.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2003.05a
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• pp.87-91
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• 2003

It has well known that MUSIC and ESPRIT algorithms estimate angle of arrival(AOA) with high resolution by eigenvalue decomposition of the covariance matrix which were obtained from the array antennas. However, the disadvantage of MUSIC and ESPRIT is that they are computationally ineffective, and then they are difficult to implement in real time. The other problem of MUSIC and ESRPIT is to require calibrated antennas with uniform features, and are sensitive to the manufacturing facult and other physical uncertainties. To overcome these disadvantages, several method using neural model have been study. For multiple signals, those require huge training data prior to AOA estimation. This paper proposes the algorithm for AOA estimation by interconnected hopfield neural model. Computer simulations show the validity of the proposed algorithm. The proposed method does not require huge training procedure and only assigns interconnected coefficients to the neural network prior to AOA estimation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.343-348
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• 2022

The STAP system suppresses clutter and jamming of the radar signal, but required a large number of samples for optimal performance. A large number of samples increases the signal processing computation. Therefore, there is need for a transformation method for reducing the signal rank. The LCMV beamforming method can easily set the distortion-free-constraint in the direction of arrival, and the beamforming scaling is excellent, so that overall rank can be reduced. In this study, the information of target is estimated using the proposed quadratic pattern constraints(QPC) and LCMV beamforming methods. The proposed method can perform beam pattern control in a desired direction according to the number of constraint conditions as a secondary pattern constraint condition. Through simulation, the performance of the propose method is verified. As a result on th simulation, the desired target was estimated when the proposed method had an angular resolution of 10 degrees or more, but it was not possible to accurately estimate the desired target when the angular resolution was less than 10 degrees.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.174-192
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• 2021

The LIDAR sensor, which provides higher cognitive performance than cameras and radar, is difficult to apply to ADAS or autonomous driving because of its high price. On the other hand, as the price is decreasing rapidly, expectations are rising to improve existing autonomous driving functions by taking advantage of the LIDAR sensor. In level 3 autonomous vehicles, when a dangerous situation in the cognitive module occurs due to a sensor defect or sensor limit, the driver must take control of the vehicle for manual driving. If the driver does not respond to the request, the system must automatically kick in and implement a minimum risk maneuver to maintain the risk within a tolerable level. In this study, based on this background, a LIDAR-based LKS MRM algorithm was developed for the case when the normal operation of LKS was not possible due to troubles in the cognitive system. From point cloud data collected by LIDAR, the algorithm generates the trajectory of the vehicle in front through object clustering and converts it to the target waypoints of its own. Hence, if the camera-based LKS is not operating normally, LIDAR-based path tracking control is performed as MRM. The HAZOP method was used to identify the risk sources in the LKS cognitive systems. B, and based on this, test scenarios were derived and used in the validation process by simulation. The simulation results indicated that the LIDAR-based LKS MRM algorithm of this study prevents lane departure in dangerous situations caused by various problems or difficulties in the LKS cognitive systems and could prevent possible traffic accidents.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.826-833
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• 2017

It is important to localize trains precisely for the purpose of controlling them and there have been many studies designed to accomplish this without the need for wayside systems. Since trains run on fixed railway lines, it is possible to search in one direction to localize them. Moreover, it is also possible to know the shape of the line in advance. In the case of high speed trains, their speed and, therefore, their Doppler frequency is relatively high and the railway line is either linear or circular with a large radius. In this study, we utilize these features and propose a train localization method using the Doppler frequency of the signals transmitted from two points (base stations). We derive localization equations for a linear line, circular line, and mixed line (linear plus circular) respectively. Though Doppler radars are usually used to measure speed, the proposed method obtains the location information and the speed successively using the ratio of the doppler frequencies of two signals without knowing the location information or the speed. Computer simulations are performed to show the variation of the estimation error according to the train's location and the measurement error level. The conditions required to obtain the target error level and the increase in the estimation error according to the measurement error are compared between the proposed and conventional methods. The results show the superior performance and robustness of the proposed method.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.72-79
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• 2009

This paper describes the performance evaluation of NHD(nonhomogeneity detector) for STAP(space-time adaptive processing) airborne radar according to various normalization methods in the nonhomogeneous clutter environment. In practice, the clutter can be characterized as random variation signals, because it sometimes includes signals with very large magnitude like impulsive signal due to the system environment. The received interference signals are composed of homogeneous and nonhomogeneous data. In this situation, NHB is needed to maintain the STAP performance. The normalization using the NHD result is an effective method for removing the nonhomogeneous data. The optimum normalization can be performed by a representative value considered with a characteristic of the given data, so we propose the K-means clustering algorithm. The characteristic of random variation data due to nonhomogeneous clutters can be considered by the number of clusters, and then the representative value for selecting the homogeneous data is determined in the clustering result. In order to reflect a characteristic of the nonstationary interference data, we also investigate the algorithm for a calculation of the proper number of clusters. Through our simulations, we verified that the K-means clustering algorithm has very superior normalization and target detection performances compared with the previous introduced normalization methods.