• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2003.05a
• /
• pp.111-114
• /
• 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. In the case that 2-D large-sized array antenna is required, however, one of the disadvantages of MUSIC and ESPRIT is that they are computationally ineffective, and then they are difficult to implement in real time. To alleviate the computational complexity, several method using neural model have been study. For multiple signals, those methods 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 Journal of Korean Institute of Communications and Information Sciences
• /
• v.31 no.5C
• /
• pp.556-561
• /
• 2006

In this study, an ultra-wideband(UWB) signal model is considered to estimate the angle-of-arrivals(AOAs) of clusters in an UWB indoor-to-outdoor communication environment having random angle spreads. A conventional AOA algorithm for UWB estimates the directions of both clusters and multipath signals together and so has complex estimation procedure. In order to solve that problem, we propose a new clustered AOA estimation algorithm. The estimation technique based a well-known MUSIC algorithm is proposed and the estimates of the AOA on received clusters are obtained using the proposed technique. The proposed model and estimation technique are verified through computer simulations.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.3
• /
• pp.639-644
• /
• 2009

This paper presents a modified CFS (Closed-Form Solution) for FLAOA (Forward Link AOA) measurements. During when the pseudo-measurement equation for FLAOA is derived, the angle measurement noise is treated more carefully in approximation. As the covariance matrix of the pseudo-measurement noise is influenced by unknown user positions and azimuth, a two-step estimation technique is used in the proposed CFS. The performance of the proposed CFS for FLAOA is compared to that of the conventional CFS for FLAOA and RLAOA (Reverse Link AOA) in a computer simulation. Simulation results show that the proposed method is potentially suitable for applications involving the localization and guidance of indoor mobile robots.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.12 no.5
• /
• pp.777-782
• /
• 2017

The Signal Intelligence (SIGINT) system based on the adaptive beamformer, comprised of the AOA estimator followed by the interference canceller, is a cutting edge technology for collecting various signal information utilizing all sorts of devices such as the radar and satellite. In this paper, we present the efficient adaptive SIGINT structure consisted of an AOA estimator and an adaptive beamformer. For estimating AOA information of various signals, we employ the Multiple Signal Classification (MUSIC) algorithm and for efficiently suppressing high-power interference signals, we employ the Minimum Variance Distortionless Response (MVDR) algorithm. Also, we provide computer simulation examples to verify the performance of the presented adaptive beamformer structure.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.31 no.3C
• /
• pp.279-285
• /
• 2006

In this study, a new signal model suitable for UWB indoor environments with random angle spread is proposed to estimate the angle-of-arrivals(AOAs) of UWB cluster signals in an UWB wireless communication. A subspace based estimation technique adopted for this model is investigated and the estimates of the AOA and distribution parameter on the received UWB cluster signals are obtained. The proposed model and estimation technique are verified using computer simulations, and the performance of the estimation error is analyzed.

• Journal of Positioning, Navigation, and Timing
• /
• v.9 no.4
• /
• pp.379-385
• /
• 2020

The satellite employs an adaptive beamformer to efficiently detect various signals and to suppress multiple interference signals, simultaneously. Although the adaptive beamforming satellite system needs Angle-of-Arrival (AOA) information of the desired signal, it is difficult to estimate the signal AOAs on the satellite environment. However, the AOA estimation on the ground control tower is more efficient and accurate comparing to the satellite environment. In this paper, we propose an adaptive beamforming satellite system based on the signal location information on the ground, consisting on an angle estimator, an adaptive beamformer, and signal processing & D/B unit. The ground control tower estimates the accurate location of the signal source, and it sends the estimated coordinates of the desired signal to the satellite. The angle estimator mounted on the satellite calculates the desired signal AOA, based on the signal location information transmitted from the ground control center. The satellite beamformer detects the desired signal and suppresses unwanted signals based on the signal AOA calculated by the angle estimator. We provide computer simulation results to present the performance of the proposed satellite adaptive beamforming system based on the signal location information.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.30 no.8C
• /
• pp.769-774
• /
• 2005

If the angle of arrival (AOA) of a source is perturbed due to some reasons in a statistical way as in the environment of wireless mobile communications, a new model appropriate for such environment should be used instead of the point source model. In this paper, an angel-perturbed source model is proposed and an estimation method based on the eigen-decomposition tecklique is investigated under the model. The asymptotic distribution of the estimation errors is obtained to observe the statistical properties.

• Steel and Composite Structures
• /
• v.45 no.6
• /
• pp.877-894
• /
• 2022

Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues associated with the production of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete to help reduce CO₂ emissions in the construction industry. The compressive strength (f_c) of GPC is predicted using artificial intelligence approaches in the present study when ground granulated blast-furnace slag (GGBS) is substituted with natural zeolite (NZ), silica fume (SF), and varying NaOH concentrations. For this purpose, two machine learning methods multi-layer perceptron (MLP) and radial basis function (RBF) were considered and hybridized with arithmetic optimization algorithm (AOA), and grey wolf optimization algorithm (GWO). According to the results, all methods performed very well in predicting the f_c of GPC. The proposed AOA - MLP might be identified as the outperformed framework, although other methodologies (AOA - RBF, GWO - RBF, and GWO - MLP) were also reliable in the fc of GPC forecasting process.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.25 no.8
• /
• pp.838-848
• /
• 2014

We present a simulation study of an algorithm for the range and angle of arrival(AOA) estimation with an interferometric synthetic aperture radar(InSAR) altimeter using a real digital elevation model(DEM). We also illustrate a step-by-step procedure of generating raw InSAR data, as well as their range and azimuth compressed data, which is to be used for the subsequent altitude and angle estimation. The AOA is estimated using a deterministic maximum likelihood estimator(DMLE) applied to the first arrived point for each pulse in the compressed data obtained with three antennas. The range bin size and the pulse repetition interval(PRI) are much smaller than the cell size of the DEM used in this study. To make the DEM compatible to the radar parameters, we first generate a higher resolution DEM by linearly interpolating the given DEM. After a brief description of the principle of the InSAR altimeter, the algorithms for altitude and angle estimation are presented, and their performance is assessed through simulation.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.6
• /
• pp.603-608
• /
• 2007

In the conventional AOA(angle-of-arrival) positioning utilizing reverse-link wireless channel, each sensor should be equipped with an array antenna to measure the incident angle of signal transmitting from a tag. To perform the complicated signal processing for angle measurements, sensor size and its power consumption will be large. In some applications like mobile robot location, there exists no strict restriction in tag size or in power consumption. Rather, it is desirable that the sensor would be as small as possible. This paper presents a new AOA positioning method utilizing forward-link channel. Under the assumption that the mobile robot is operating on the flat surface, the measurement model for FLAOA(tiJrward-link AOA) is derived first. Two kinds of position estimation algorithms using FLAOA measurements are proposed; Gauss-Newton method and closed-fonn solution method. With the proposed methods, we can ohtain the attitude of robot as well as its position. Positioning performance of proposed methods is compared by computer simulation. Simulation results show that the closed-form solution method using FLAOA measurements is suitable for indoor robot positioning.