• ETRI Journal
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• v.33 no.4
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• pp.487-496
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• 2011

Korea Multi-Purpose Satellite-5 (KOMPSAT-5) is the first satellite in Korea that provides 1 m resolution synthetic aperture radar (SAR) images. Precise orbit determination (POD) using a dual-frequency IGOR receiver data is performed to conduct high-resolution SAR images. We suggest orbit determination strategies based on a differential GPS technique. Double-differenced phase observations are sampled every 30 seconds. A dynamic model approach using an estimation of general empirical acceleration every 6 minutes through a batch least-squares estimator is applied. The orbit accuracy is validated using real data from GRACE and KOMPSAT-2 as well as simulated KOMPSAT-5 data. The POD results using GRACE satellite are adjusted through satellite laser ranging data and compared with publicly available reference orbit data. Operational orbit determination satisfies 5 m root sum square (RSS) in one sigma, and POD meets the orbit accuracy requirements of less than 20 cm and 0.003 cm/s RSS in position and velocity, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.206-221
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• 2019

Source localization in three-dimensional (3-D) wireless sensor networks (WSNs) is becoming a major research focus. Due to the complicated air-ground environments in 3-D positioning, many of the traditional localization methods, such as received signal strength (RSS) may have relatively poor accuracy performance. Benefit from prior learning mechanisms, fingerprinting-based localization methods are less sensitive to complex conditions and can provide relatively accurate localization performance. However, fingerprinting-based methods require training data at each grid point for constructing the fingerprint database, the overhead of which is very high, particularly for 3-D localization. Also, some of measured data may be unavailable due to the interference of a complicated environment. In this paper, we propose an efficient kernel based 3-D localization algorithm via tensor completion. We first exploit the spatial correlation of the RSS data and demonstrate the low rank property of the RSS data matrix. Based on this, a new training scheme is proposed that uses tensor completion to recover the missing data of the fingerprint database. Finally, we propose a kernel based learning technique in the matching phase to improve the sensitivity and accuracy in the final source position estimation. Simulation results show that our new method can effectively eliminate the impairment caused by incomplete sensing data to improve the localization performance.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.4
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• pp.361-368
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• 2022

An indoor navigation system that utilizes long-term evolution (LTE) signals has the benefit of no additional infrastructure installation expenses and low base station database management costs. Among the LTE signal measurements, received signal strength (RSS) is particularly appealing because it can be easily obtained with mobile devices. Propagation channel models can be used to estimate the position of mobile devices with RSS. However, conventional channel models have a shortcoming in that they do not discriminate between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions of the received signal. Accordingly, a previous study has suggested separated LOS and NLOS channel models. However, a method for determining LOS and NLOS conditions was not devised. In this study, a machine learning-based LOS/NLOS classification method using RSS measurements is developed. We suggest several machine-learning features and evaluate various machine-learning algorithms. As an indoor experimental result, up to 87.5% classification accuracy was achieved with an ensemble algorithm. Furthermore, the range estimation accuracy with an average error of 13.54 m was demonstrated, which is a 25.3% improvement over the conventional channel model.

• Journal of KIISE:Databases
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• v.34 no.4
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• pp.343-356
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• 2007

Due to advances in position monitoring technologies such as global positioning systems and sensor networks, recent position information of moving objects has the form of streaming data which are updated continuously and rapidly. In this paper we propose an efficient trajectory maintenance method that stores the streaming position data of moving objects in the limited size of storage space and estimates past positions based on the stored data. For this, we first propose a new concept of incremental extraction of position information. The incremental extraction means that, whenever a new position is added into the system, we incrementally re-compute the new version of past position data maintained in the system using the current version of past position data and the newly added position. Next, based on the incremental extraction, we present an overall framework that stores position information and estimates past positions in the stream environment. We then propose two polynomial-based methods, line-based and curve-based methods, as the method of estimating the past positions on the framework. We also propose three incremental extraction methods: equi-width, slope-based, and recent-emphasis extraction methods. Experimental results show that the proposed incremental extraction provides the relatively high accuracy (error rate is less than 3%) even though we maintain only a little portion (only 0.1%) of past position information. In particular, the curve-based incremental extraction provides very low error rate of 1.5% even storing 0.1% of total position data. These results indicate that our incremental extraction methods provide an efficient framework for storing the position information of moving objects and estimating the past positions in the stream environment.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.569-575
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• 2013

This paper proposes a method for PDR (Pedestrian Dead-Reckoning) using a low cost IMU. Generally, GPS has been widely used for localization of pedestrians. However, GPS is disabled in the indoor environment such as in buildings. To solve this problem, this research suggests the PDR scheme with an IMU attached to the pedestrian's waist. However, despite the fact many methods have been proposed to estimate the pedestrian's position, but their results are not sufficient. One of the most important factors to improve performance is, a new calibration method that has been proposed to obtain the reliable sensor data. In addition to this calibration, the PDR method is also proposed to detect steps, where estimation schemes of step length, attitude, and heading angles are developed. Peak and zero crossings are detected to count the steps from 3-axis acceleration values. For the estimation of step length, a nonlinear step model is adopted to take advantage of using one parameter. Complementary filter and zero angular velocity are utilized to estimate the attitude of the IMU module and to minimize the heading angle drift. To verify the effectiveness of this scheme, a real-time system is implemented and demonstrated. Experimental results show an accuracy of below 1% and below 3% in distance and position errors, respectively, which can be achievable using a high cost IMU.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.9
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• pp.1035-1042
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• 2014

Object recognition is one of the key technologies of the monitoring system for the prevention of various intelligent crimes. The height is one of the physical information of a person, and it may be important information for identification of the person. In this paper, a method which can detect pedestrians from CCTV images and estimate the height of the detected objects, is proposed. In this method, GMM (Gaussian Mixture Model) method was used to separate the moving object from the background and the pedestrian was detected using the conditions such as the width-height ratio and the size of the candidate objects. The proposed method was applied to the CCTV video, and the height of the pedestrian at far-distance, middle- distance, near-distance was estimated for the same person, and the accuracy was evaluated. Experimental results showed that the proposed method can estimate the height of the pedestrian as the accuracy of 97% for the short-range, 98% for the medium-range, and more than 97% for the far-range. The image sizes for the same pedestrian are different as the position of him in the image, it is shown that the proposed algorithm can estimate the height of pedestrian for various position effectively.

• Journal of Navigation and Port Research
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• v.37 no.5
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• pp.447-452
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• 2013

Vessel traffic system uses multiple sea surveillance radars as a primary sensor to obtain maritime traffic information like as ship's position, speed, course. The systematic errors such as the range bias and the azimuth bias of the two-dimensional radar system can significantly degrade the accuracy of the radar image and target tracking information. Therefore, the systematic errors of the radar system should be corrected precisely in order to provide the accurate target information in the vessel traffic system. In this paper, it is proposed that the method compensates the range bias and the azimuth bias using AtoN information installed at VTS coverage. The radar measurement residual error model is derived from the standard error model of two-dimensional radar measurements and the position information of AtoN, and then the linear Kalman filter is designed for estimation of the systematic errors of the radar system. The proposed method is validated via Monte-Carlo runs. Also, the convergence characteristics of the designed filter and the accuracy of the systematic error estimates according to the number of AtoN information are analyzed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.114-119
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• 2009

Sound source localization systems in service robot applications estimate the direction of a human voice. Time delay information obtained from a few separate microphones is widely used for the estimation of the sound direction. Correlation is computed in order to calculate the time delay between two signals. Inverse cosine is used when the position of the maximum correlation value is converted to an angle. Because of nonlinear characteristic of inverse cosine, the accuracy of the computed angle is varied depending on the position of the specific sound source. In this paper, we propose an efficient sound source localization system using angle division. By the proposed approach, the region from $0^{\circ}$ to $180^{\circ}$ is divided into three regions and we consider only one of the three regions. Thus considerable amount of computation time is saved. Also, the accuracy of the computed angle is improved since the selected region corresponds to the linear part of the inverse cosine function. By simulations, it is shown that the error of the proposed algorithm is only 31% of that of the conventional a roach.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.465-472
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• 2019

The SPR (Single Photo Resection) is widely used as a method of estimating the EOPs (Exterior Orientation parameters) at the time of taking a photograph, but it requires an initial value and has a disadvantage of being sensitive to the initial value. In this study, we introduce quaternion-based single photo resection and PnP (Perspective-n-Point) algorithm that do not require initial values and compare the results. Photos were taken using a general smartphone, and the ground control point acquisition was based on the hybrid MMS (Mobile Mapping System) point cloud data possessed by the researchers. As a result, when the collinear condition based SPR is true value, quaternion-based SPR has higher attitude angle estimation accuracy than PnP algorithm. In case of camera position estimation, both algorithms showed accuracy within 0.8m when compared with ground control points.

• Journal of Navigation and Port Research
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• v.36 no.10
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• pp.833-838
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• 2012

TDOA which is one of the position estimation methods is used on indoor positioning, jammer localization, rescue of life, etc. due to high accuracy and simple structure. This paper proposes the super-resolution TDOA estimator using MPM(Matrix Pencil Method). The proposed estimator has more accuracy and is applicable to narrowband signal compared with the conventional cross-correlation. Furthermore, its complexity is low because obtained data directly is used for construction of matrix unlike the MUSIC(Multiple Signal Classification) which is one of the well-known super-resolution estimator using covariance matrix. To validate the performance of proposed estimator, errors of estimation and computational burden is compared to MUSIC through software simulation.