• Journal of Institute of Control, Robotics and Systems
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• v.20 no.3
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• pp.372-379
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• 2014

This paper surveys past and state-of-the-art SLAM technologies. The standard methods for solving the SLAM problem are the Kalman filter, particle filter, graph, and bundle adjustment-based methods. Kalman filters such as EKF (Extended Kalman Filter) and UKF (Unscented Kalman Filter) have provided successful results for estimating the state of nonlinear systems and integrating various sensor information. However, traditional EKF-based methods suffer from the increase of computation burden as the number of features increases. To cope with this problem, particle filter-based SLAM approaches such as FastSLAM have been widely used. While particle filter-based methods can deal with a large number of features, the computation time still increases as the map grows. Graph-based SLAM methods have recently received considerable attention, and they can provide successful real-time SLAM results in large urban environments.

• Bulletin of the Korean Space Science Society
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• 2011.04a
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• pp.25.4-25.4
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• 2011

Various estimation methods based on Kalman filter have been applied to the real-time satellite orbit determination. The most popular method is the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). The EKF is easy to implement and to use on orbit determination problem. However, the linearization process of the EKF can cause unstable solutions if the problem has the inaccurate reference orbit, sparse or insufficient observations. In this case, the UKF can be a good alternative because it does not contain linearization process. However, because both methods are based on Gaussian assumption, performance of estimation can become worse when the distribution of state parameters and process/measurement noise are non-Gaussian. In nonlinear/non-Gaussian problems the particle filter which is based on sequential Monte Carlo methods can guarantee more exact estimation results. This study develops and tests the particle filter for satellite orbit determination. The particle filter can be more effective methods for satellite orbit determination in nonlinear/non-Gaussian environment.

• The Journal of Korea Robotics Society
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• v.9 no.4
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• pp.216-224
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• 2014

This paper compares methods for attitude estimation of a UUV(Unmanned Underwater Vehicle). Attitude estimation plays a key role in underwater navigation using DVL(Doppler Velocity Log). The paper proposes attitude estimation methods using EKF(Extended Kalman Filter), UKF(Unscented Kalman Filter), and CF(Complementary Filter). It derives methods using the measurements from MEMS-AHRS(Microelectromechanical Systems-Attitude Heading Reference System) and DVL. The methods are used for navigation in a test pool and their navigation performance is compared. The results suggest that even if there is no measurement relative to some absolute landmarks, DVL-only navigation can be useful for navigation in a limited time and range.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.135-135
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• 2022

In this study, we evaluated uncertainties in baseflow separation filter methods focusing on changes in recession constant (𝛼) values, which include Lynie & Holick (LH) algorithm, Chapman algorithm, Eckhardt filter, and EWMA filter. Here, we analyzed daily streamflow data at 14 stations in the Urmia Lake basin, Iran, from 2015 to 2019. The 𝛼 values were computed using three different approaches from calculating the slope of a recession curve by averaging the flow over all seasons, a correlation method, and a mean value of the ratio of Q_t+1 to Q_t. In addition to the 𝛼 values, the BFImax (maximum value of the baseflow index (BFI)) was determined for the Eckhardt filter through the backward filter method. As results, it indicates that the estimated baseflow is dependent upon the selection of filter methods, their parameters, and catchment characteristics at different stations. In particular, the EWMA filter showed the least changes in estimating the baseflow value by changing the 𝛼 value, and the Eckhardt filter and LH algorithm showed the highest sensitivity to this parameter at different stations.

• Journal of Positioning, Navigation, and Timing
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• v.5 no.1
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• pp.1-9
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• 2016

Range-based wireless localization system must measure accurate range between a mobile node (MN) and reference nodes. However, non-line-of-sight (NLOS) error caused by the spatial structures disturbs the localization system obtaining the accurate range measurements. Localization methods using the range measurements including NLOS error yield large localization error. But filter-based localization methods can provide comparatively accurate location solution. Motivated by the accuracy of the filter-based localization method, a filter residual-based NLOS error estimation method is presented in this paper. Range measurement-based residual contains NLOS error. By considering this factor with NLOS error properties, NLOS error is mitigated. Also a process noise covariance matrix tuning method is presented to reduce the time-delay estimation error caused by the single dynamic model-based filter when the speed or moving direction of a MN changes, that is the used dynamic model is not fit the current dynamic of a MN. The presented methods are evaluated by simulation allowing direct comparison between different localization methods. The simulation results show that the presented filter is more accurate than the iterative least squares- and extended Kalman filter-based localization methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.3 no.4
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• pp.282-288
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• 2008

In this paper, the Passive Intermodulation Distortion(PIMD) of high power RF Filter is measured with filter inner coating materials and we suggest how to reduced the PIMD of RF filter coating methods. According to the standard measurement regulation by IEC, the Passive Intermodulation Distortion of Wibro relay high power filter are measured. We suggest the coating materials and coating methods of high power filter inner structure to reduce the PIMD generating by insert loss and worse flatness of filter delay in the design of Wibro high power filter efficiently.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.5
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• pp.1155-1171
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• 1996

In this paper, we have analized the characteristics of pseudomedian filter based on the preceding study. The proposed pseudomedian filter methods consist of two types, that are H-shaped and Asterisk-shaped window types. At first, the definition of pseudomedian filter and its algorithm for de-interlacing scan conversion have been descibed, especially its charateristics, this, is the edge preserving characteristics and the required computation have been compared with the conventional algorithms. And PSNR has been introduced to evaluate the pseudomedian filter methods and the conventional algorithms. Finally, it has been discussed on the features and trade off of the pseudomedian filter methods. And the merit and application fields of the pseudomedian filter methods have been discussed.

• The Journal of Engineering Research
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• v.3 no.1
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• pp.43-46
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• 1998

Considerable research in the last three decades has examined the problem of enhancement of speech degraded by additive background noise. We compare traditional methods such as spectral subtraction and Wiener filter, recently proposed psychoacoustic model based methods such as perceptual filter and noise suppression in EVRC in terms of performance and complexity.

• Nuclear Engineering and Technology
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• v.26 no.2
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• pp.205-211
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• 1994

The best method is selected among the 3 digital dynamic compensation methods which are developed or applied for the Rhodium self-powered neutron detector. The three digital dynamic compensation methods are the existing Dominant Pol Tustin method of the COLSS(Core Operating Limit Supervisory System), the Direct Inversion method and Kalman Filter method. The Direct Inversion method is an improved method of D. Hoppe and R. Maletti and the Kalman Filter method is developed using the Kalman Filter. Response times of the compensated signals to achieve 90% of a step input are 28.1, 17.2 and 6.5 seconds respectively for the same noise gain telling that the Kalman Filter method is the best amens the 3 methods.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.1
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• pp.85-90
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• 2013

This paper presents a study on terrain referenced navigation (TRN). The extended Kalman filter (EKF) is adopted as a filter method. A Jacobian matrix of measurement equations in the EKF consists of terrain slope terms, and accurate slope estimation is essential to keep filter stability. Two slope estimation methods are proposed in this study. Both methods are based on the least-squares approach. One is planar regression searching the best plane, in the least-squares sense, representing the terrain map over the region, determined by position error covariance. It is shown that the method could provide a more accurate solution than the previously developed linear regression approach, which uses lines rather than a plane in the least-squares measure. The other proposed method is weighted planar regression. Additional weights formed by Gaussian pdf are multiplied in the planar regression, to reflect the actual pdf of the position estimate of EKF. Monte Carlo simulations are conducted, to compare the performance between the previous and two proposed methods, by analyzing the filter properties of divergence probability and convergence speed. It is expected that one of the slope estimation methods could be implemented, after determining which of the filter properties is more significant at each mission.