• Journal of Sensor Science and Technology
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• v.28 no.4
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• pp.216-220
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• 2019

In this paper, we propose a finite memory filter (estimator) for discrete-time stochastic linear systems with delays in state and measurement. A novel filtering algorithm is designed based on finite memory strategies, to achieve high estimation accuracy and stability under parametric uncertainties. The new finite memory filter uses a set of recent observations with appropriately chosen initial horizon conditions. The key contribution is the derivation of Lyapunov-like equations for finite memory mean and covariance of system state with an arbitrary number of time delays. A numerical example demonstrates that the proposed algorithm is more robust and accurate than the Kalman filter against dynamic model uncertainties.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.341-347
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• 2022

This paper presents a comparison result of two simultaneous localization and mapping (SLAM) algorithms for navigation that have been proposed in literature. The performances of Extended Kalman Filter (EKF) SLAM under Gaussian condition, FastSLAM algorithms using Rao-Blackwellised method for particle filtering are compared in terms of accuracy of state estimations for localization of a robot and mapping of its environment. The algorithms were run using the same type of robot on indoor environment. The results show that the Particle filter based FastSLAM has the better performance in terms of accuracy of localization and mapping. The experimental results are discussed and compared.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.125-130
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• 2022

In modern society, as many data are used for research or commercial purposes, the value of data is gradually increasing. In related fields, research is being actively conducted to collect valuable data, but it is difficult to collect proper data because the value of collection is determined according to the performance of existing sensors. To solve this problem, a method to effectively reduce noise has been proposed, but there is a point in which performance is degraded due to damage caused by noise. In this paper, a device capable of collecting time series data was designed to correct such data noise, and a correction technique was performed by giving an error value based on the representatively collected ultrafine dust data, and then comparing before and after Compare performance. For the correction method, Kalman, LPF, Savitzky-Golay, and Moving Average filter were used. Savitzky-Golay filter and Moving Average Filter showed excellent correction rate as an experiment. Through this, the performance of the sensor can be supplemented and it is expected that data can be effectively collected.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.167-176
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• 2022

We designed a borehole deviation survey tool applicable for steel-cased holes, K-DEV, and developed a prototype for a depth of 500 m aiming to development of own equipment required to secure deep subsurface characterization technologies. K-DEV is equipped with sensors that provide digital output with verified high performance; moreover, it is also compatible with logging winch systems used in Korea. The K-DEV prototype has a nonmagnetic stainless steel housing with an outer diameter of 48.3 mm, which has been tested in the laboratory for water resistance up to 20 MPa and for durability by running into a 1-km deep borehole. We confirmed the operational stability and data repeatability of the prototype by constantly logging up and down to the depth of 600 m. A high-precision micro-electro-mechanical system (MEMS) gyroscope was used for the K-DEV prototype as the gyro sensor, which is crucial for azimuth determination in cased holes. Additionally, we devised an accurate trajectory survey algorithm by employing Unscented Kalman filtering and data fusion for optimization. The borehole test with K-DEV and a commercial logging tool produced sufficiently similar results. Furthermore, the issue of error accumulation due to drift over time of the MEMS gyro was successfully overcome by compensating with stationary measurements for the same attitude at the wellhead before and after logging, as demonstrated by the nearly identical result to the open hole. We believe that the methodology of K-DEV development and operational stability, as well as the data reliability of the prototype, were confirmed through these test applications.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.3
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• pp.521-528
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• 2016

The tollbooths of a main motorway toll plaza are usually operated proactively responding to the variations of traffic demands of two-type vehicles, i.e. cars and the other (heavy) vehicles, respectively. In this vein, it is one of key elements to forecast accurate traffic volumes for the two vehicle types in advanced tollgate operation. Unfortunately, it is not easy for existing univariate short-term prediction techniques to simultaneously generate the two-vehicle-type traffic demands in literature. These practical and academic backgrounds make it one of attractive research topics in Intelligent Transportation System (ITS) forecasting area to forecast the future traffic volumes of the two-type vehicles at an acceptable level of accuracy. In order to address the shortcomings of univariate short-term prediction techniques, a Multiple In-and-Out (MIO) forecasting model to simultaneously generate the two-type traffic volumes is introduced in this article. The MIO model based on a non-parametric approach is devised under the on-line access conditions of large-scale historical data. In a feasible test with actual data, the proposed model outperformed Kalman filtering, one of a widely-used univariate models, in terms of prediction accuracy in spite of multivariate prediction scheme.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.5
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• pp.51-58
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• 2012

Accurate transmission and estimation of the channel statistics affected by high Doppler spread is one of the main issues of concern for the latest and future mobile communication systems. Therefore, it is important to research in novel channel estimation techniques that overcome the limitations of conventional methods. In this paper, we propose a novel channel estimation method that, after a simple estimation in the first OFDM symbol, uses Kalman filter to predict the channel in the rest of OFDM symbols with pilot subcarriers. Our method is designed considering the lattice-type arrangement of pilot subcarriers in LTE-Advanced, since most of the studies so far focus on block-type or comb-type pilot arrangements. In addition, to optimize the results, we use the filtering of channel impulse response and Wiener Filter for the estimation of the channel frequency response in the rest of the subcarriers.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.2
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• pp.68-78
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• 2000

This paper suggests a new map-matching algorithm for matching traveling trace of vehicle, which is measured by GPS receiver, to the road of a digital map This eventually brings the Improvement of positioning accuracy of the vehicle with GPS receiver After representing the travelling vehicle's motion by state equations using Singer's model, the proposed map-matching algorithm places the position of a vehicle right on the road and also improves the positioning accuracy of the vehicle using a Kalman filter In the crossroad, since it is difficult to determine precisely a current travelling road, we take linear regression to the estimated values from Kalman filtering This gives the direction angle of turning vehicle, then we can determine the correct route direction after comparing with each route-direction angle at the intersection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.428-451
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• 2010

Traffic matrix-based anomaly detection and DDoS attacks detection in networks are research focus in the network security and traffic measurement community. In this paper, firstly, a new type of unidirectional flow called IF flow is proposed. Merits and features of IF flows are analyzed in detail and then two efficient methods are introduced in our DDoS attacks detection and evaluation scheme. The first method uses residual variance ratio to detect DDoS attacks after Recursive Least Square (RLS) filter is applied to predict IF flows. The second method uses generalized likelihood ratio (GLR) statistical test to detect DDoS attacks after a Kalman filter is applied to estimate IF flows. Based on the two complementary methods, an evaluation formula is proposed to assess the seriousness of current DDoS attacks on router ports. Furthermore, the sensitivity of three types of traffic (IF flow, input link and output link) to DDoS attacks is analyzed and compared. Experiments show that IF flow has more power to expose anomaly than the other two types of traffic. Finally, two proposed methods are compared in terms of detection rate, processing speed, etc., and also compared in detail with Principal Component Analysis (PCA) and Cumulative Sum (CUSUM) methods. The results demonstrate that adaptive filter methods have higher detection rate, lower false alarm rate and smaller detection lag time.

• KDI Journal of Economic Policy
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• v.34 no.1
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• pp.29-52
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• 2012

During the global financial turmoil in 2007-2008, deviation from the covered interest parity (CIP) between the Korean won and US dollar through the foreign exchange swap has escalated in its magnitude beyond 1,000bp in November 2008, and it still persists around 100bp level. In this paper, we examine a newly developed margin based asset pricing model using Kalman filter approach and show that the escalation of the CIP deviation is found to be significantly related to the global dollar funding illiquidity and country-specific funding conditions. Furthermore, we find evidence that the poor funding conditions (or higher margins) are driven by the general money market illiquidity and may lead to higher funding illiquidity, which suggests the reinforcing effects of the liquidity spiral. We also show that the supply of dollar liquidity and improved funding conditions help alleviate the deviations from the parity, however the persistent anomaly is found to be related to the high level of volatility in the FX swap market.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.2
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• pp.149-157
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• 2009

Tracking technologies which provide real-time and customized information through various information collecting and processing for pedestrians who use traffic connective and transferring center have been being examined. However some problems are caused due to the wide-range positioning error for some services as device installation and service place. It is also difficult to be applied to traffic linkage and transfer services because many situations can be barren. In the testbed, Gwangmyoung Station, we got some results in bad conditions such as a lot of steel construction and another communication device. Practically, conditions of the place which will be built can be worse than Gwangmyoung station. Therefore, we researched suitable Location correction algorithm as a method for accuracy to traffic connective and transferring system. And its algorithm is designed through grid coordinates, map-matching, modeling coordinates and Kalman filtering and is being implemented continuously. Also preparing for optimization of various transferring center model, we designed for simulator type algorithm what is available for deciding algorithm factor.