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

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.479-483
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• 2006

Indoor positioning is recently highlighted and various kinds of indoor positioning systems are under developments. Since positioning systems have their own characteristics, proper positioning scheme should be chosen according to the required specifications. Positioning methods are often classified into time-based and angle-based one, and this paper presents the error analysis of these location methods. Because measurement equations of these methods are nonlinear, linearization is usually needed to get the position estimate. In this paper, Gauss-Newton method is used in the linearization. To analyze the position error, we investigate the error ellipse parameters that include eccentricity, rotation angle, and the size of ellipse. Simulation results show that the major axes of error ellipses of TOA and AOA method lie in different quadrants at most region of workspace, especially where the geometry is poor. When the TOA/AOA hybrid scheme is employed, it is found that the error ellipse is reduced to the intersection of ellipses of TOA and AOA method.

• International Journal of Aeronautical and Space Sciences
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• v.12 no.3
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• pp.266-273
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• 2011

This paper deals with missile autopilot design for agile turn phase in air-to-air engagement scenarios. To attain a fast response, angle-of-attack (AOA) is adopted for an autopilot command structure. Since a high operational AOA is generally required during the agile turn phase, dealing with the aerodynamic uncertainties can be a challenge for autopilot design. As a remedy, a new controller design method based on robust nonlinear control methodology such as time delay control is proposed in this paper. Nonlinear observer is also proposed to estimate the AOA in the presence of the model uncertainties. The performance of the proposed controller with variation of the aerodynamic coefficients is investigated through numerical simulations.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.485-489
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• 2006

In last decades, several linearization methods for the AOA measurements have been proposed, for example, Gauss-Newton method and closed-form solution. Gauss-Newton method can achieve high accuracy, but the convergence of the iterative process is not always ensured if the initial guess is not accurate enough. Closed-form solution provides a non-iterative solution and it is less computational. It does not suffer from convergence problem, but estimation error is somewhat larger. This paper proposes a self-tuning weighted least square AOA algorithm that is a modified version of the conventional closed-form solution. In order to estimate the error covariance matrix as a weight, two-step estimation technique is used. Simulation results show that the proposed method has smaller positioning error compared to the existing methods.

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

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1840-1848
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• 2017

Linear wireless sensor networks typically construct a network topology with a high reliability through sequential 1:1 mapping among sensor nodes, so that they are used in various surveillance applications of major national infrastructures. Most existing techniques for identifying sensor nodes in those networks are using GPS, AOA, and RSSI mechanisms. However, GPS or AOA based node identification techniques affect the size or production cost of the nodes so that it is not easy to construct practical sensor networks. RSSI based techniques may have a high deviation regrading location identification according to propagation environments and equipment quality so that complexity of error correction algorithm may increase. We propose a timestamps based sequential localization algorithm that uses transmit and receive timestamps in a message between sensor nodes without using GPS, AOA, and RSSI techniques. The algorithms for distance measurement between each node are expected to measure distance within up to 1 meter in case of an crystal oscillator of 300MHz or more.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.8
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• pp.623-628
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• 2013

The retrodirective array antenna system is operated faster than existing techniques of beamforming due to its less complexity. Therefore, it is effective for beam tracking in the environment of fast vehicle. On the other hand, it also has difficulty in estimating AOA according to multipath environment or multiuser signals. To improve the certainty of estimating AOA), this article proposes hybrid digital retrodirective array antenna systme combined with MUSIC algorithm. In this paper, the digital retrodirective array antenna system is designed according to the number of antenna array by using only one digital PLL which finds angle of delayed phase. And we evaluate the performance of the digital retrodirective array antenna for the high speed tracking application. Performance is studied by simulink when the speed of mobile is 300km/h and the distance between transmitter and receiver is 100m and then we have to confirm the performance of the system in multi path environment. As a result, the mean of AOA (Angle Of Arrival) error is $4.2^{\circ}$ when SNR is 10dB and it is $1.3^{\circ}$ when SNR is 20dB. Consequently, the digital RDA shows very good performance for high speed tracking due to the simple calculation and realization.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.9
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• pp.889-895
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• 2011

In recent days, some techniques to prevent from radar detection have been applied on aircraft system. RWR(Radar Warning Receiver) can be used for estimating the source location of the aircraft which emits radar pulse. Current existing method of localizing radar pulse emission source is using AOA(Angle Of Arrival) and most techniques are focused on finding exact AOA to find exact location. In this case, however, the exact AOA does not always result in finding exact source location while target aircraft is moving fast. In this paper, a localization method using the phase delay of the radar pulse's low frequency applies and so a scanning method for the interest area does in order to estimate exact source location by using phase delay.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.713-720
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• 2018

The Signal Intelligence (SIGINT) system based on Angle-of-Arrival(AOA) estimation, interference suppression, and transmission beamforming techniques is a cutting edge technology for efficiently collecting various signal information. In this paper, we present the efficient structure of a satellite system consisted of an AOA estimator, an adaptive beamformer, a signal processing and D/B unit, and a transmission beamformer, for collecting signal information. For accurately estimating AOAs of various signals, efficiently suppressing interference or jamming signals, and efficiently transmitting the collected information or data, we employ Multiple Signal Classification (MUSIC), Minimum Variance Distortionless Response (MVDR), and Minimum Mean Square Error (MMSE) algorithms, respectively. Also, we evaluate and analysis the performance of the presented satellite system through the computer simulation.

• Geomechanics and Engineering
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• v.36 no.3
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• pp.259-276
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• 2024

The undrained shear strength is widely acknowledged as a fundamental mechanical property of soil and is considered a critical engineering parameter. In recent years, researchers have employed various methodologies to evaluate the shear strength of soil under undrained conditions. These methods encompass both numerical analyses and empirical techniques, such as the cone penetration test (CPT), to gain insights into the properties and behavior of soil. However, several of these methods rely on correlation assumptions, which can lead to inconsistent accuracy and precision. The study involved the development of innovative methods using extreme gradient boosting (XGB) to predict the pile set-up component "A" based on two distinct data sets. The first data set includes average modified cone point bearing capacity (q_t), average wall friction (f_s), and effective vertical stress (σ_vo), while the second data set comprises plasticity index (PI), soil undrained shear cohesion (S_u), and the over consolidation ratio (OCR). These data sets were utilized to develop XGBoost-based methods for predicting the pile set-up component "A". To optimize the internal hyperparameters of the XGBoost model, four optimization algorithms were employed: Particle Swarm Optimization (PSO), Social Spider Optimization (SSO), Arithmetic Optimization Algorithm (AOA), and Sine Cosine Optimization Algorithm (SCOA). The results from the first data set indicate that the XGBoost model optimized using the Arithmetic Optimization Algorithm (XGB - AOA) achieved the highest accuracy, with R2 values of 0.9962 for the training part and 0.9807 for the testing part. The performance of the developed models was further evaluated using the RMSE, MAE, and VAF indices. The results revealed that the XGBoost model optimized using XGBoost - AOA outperformed other models in terms of accuracy, with RMSE, MAE, and VAF values of 0.0078, 0.0015, and 99.6189 for the training part and 0.0141, 0.0112, and 98.0394 for the testing part, respectively. These findings suggest that XGBoost - AOA is the most accurate model for predicting the pile set-up component.