• Journal of Sensor Science and Technology
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• v.25 no.6
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• pp.418-422
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• 2016

A filtering algorithm based on the decentralized moving average Kalman filter with uncertainties is proposed in this paper. The proposed filtering algorithm presented combines the Kalman filter with the moving average strategy. A decentralized fusion algorithm with the weighted sum structure is applied to the local moving average Kalman filters (LMAKFs) of different window lengths. The proposed algorithm has a parallel structure and allows parallel processing of observations. Hence, it is more reliable than the centralized algorithm when some sensors become faulty. Moreover, the choice of the moving average strategy makes the proposed algorithm robust against linear discrete-time dynamic model uncertainties. The derivation of the error cross-covariances between the LMAKFs is the key idea of studied. The application of the proposed decentralized fusion filter to dynamic systems within a multisensor environment demonstrates its high accuracy and computational efficiency.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.593-598
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• 2005

The standard Kalman filter has been used to estimate the states of the target, but in the presence of a maneuver, its error is occurred and performance may be seriously degraded. To solve this problem, this paper presents a new intelligent tracking algorithm using the fuzzy Kalman filter. In this algorithm, the unknown acceleration is regarded as an additive process noise by using the fuzzy logic based on genetic algorithm(GA) method. And then, the modified filter is corrected by the new update equation method which is a fuzzy system using the relation between the filter residual and its variation. To shows the feasibility of the suggested method with only one filter, the computer simulations system are provided, this method is compared with multiple model method.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.471-478
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• 2012

A foot motion is estimated using an inertial sensor unit, which is installed on a shoe. The inertial sensor unit consists of 3 axis accelerometer and 3 axis gyroscopes. Attitude and position of a foot are estimated using an inertial navigation algorithm. To increase estimation performance, a smoother is used, where the smoother employs a forward and backward filter structure. An indirect Kalman filter is used as a forward filter and backward filter. A new combining algorithm for the smoother is proposed to combine a forward indirect Kalman filter and a backward indirect Kalman filter. Through experiments, the estimation performance of the proposed smoother is verified.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.563-566
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• 2004

An intelligent Kalman filter (IKF) is proposed for tracking an incoming anti-ship missile. In the proposed IKF, the unknown target acceleration is regarded as an additive process noise. When the target maneuver is occurred, the residual of the Kalman filter increases in proportion to its magnitude. From this fact, the overall process noise variance can be approximated from the filter residual and its variation at every sampling time. A fuzzy system is utilized to approximate this valiance, and the genetic algorithm (GA) is applied to optimize the fuzzy system. In computer simulations, the tracking performance of the proposed IKF is compared with those of conventional maneuvering target tracking methods.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.261-272
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• 2020

A Kalman filter algorithm is used for the generation of an ensemble timescale with three hydrogen masers maintained in KRISS. Allan deviation curves of three pairs of clocks were obtained by a three-cornered hat method and were used as reference curves for determination of parameters of the Kalman filter-based timescale. The ensemble timescale equation of a 3-clock system was established, and the clocks' phases estimated by the Kalman filter were used as the prediction time of each clock in the equation. The weight of each clock was determined inversely proportional to the Allan variance calculated with the clocks' phases. The Allan deviation of the weighted mean was 1.2×10^-16 at the averaging time of 57,600 s. However when we made fine adjustments of the clocks' weight, the minimum Allan deviation of 2×10^-17 was obtained. To find out the reason of the great improvement in the frequency stability, additional researches are in progress theoretically and experimentally.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.11
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• pp.1001-1011
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• 2012

In this paper, the effects to system performance are evaluated in IEEE 802.11 system when the number of competing terminals are estimated and reflected to the system. The IEEE 802.11 system uses DCF (Distributed Coordination Function) for the multiple access method, and the system throughput performance depends on the accuracy of the estimated number of competing terminals. We propose extended H infinity filter (EHIF) approach which does not require the noise information for estimating the number of competing terminals. Simulation results show that EHIF outperforms the extended Kalman filter in both saturated and non-saturated network conditions.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.269-274
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• 1986

A continuous-time modified gain extended Kalman filter (MGEKF) is developed in an effort to extend the discrete-time results of 1) and 2). Used as an observer, it is globally exponentially convergent. For stochastic system, the stability of the MGEKF is proven under certain conditions. The performance of the MGEKF is compared with that of the EKF for a particular nonlinear system where the fininate dimensional optimal filter exists.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.3
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• pp.263-273
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• 2008

The precision of sensors' position is particularly important in the application of road extraction or digital map generation. In general, the various ranging solution systems such as GPS, Total Station, and Laser Ranger have been employed for the position of the sensor. Basically, the ranging solution system has problems that the signal may be blocked or degraded by various environmental circumstances and has low temporal resolution. To overcome those limitations a IMU/range integrated system could be introduced. In this paper, after pointing out the limitation of extended Kalman filter which has been used for workhorse in navigation and geodetic community, the two sampling based nonlinear filters which are sigma point Kalman filter using nonlinear transformation and carefully chosen sigma points and particle filter using the non-gaussian assumption are implemented and compared with extended Kalman filter in a simulation test. For the ranging solution system, the GPS and Total station was selected and the three levels of IMUs(IMU400C, HG1700, LN100) are chosen for the simulation. For all ranging solution system and IMUs the sampling based nonlinear filter yield improved position result and it is more noticeable that the superiority of nonlinear filter in low temporal resolution such as 5 sec. Therefore, it is recommended to apply non-linear filter to determine the sensor's position with low degree position sensors.

• Water for future
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• v.28 no.2
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• pp.125-136
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• 1995

The propose of this study is improve the uncertainty of parameter choice of tank model by the trials and errors method. The real time prediction of parameter by using the Kalman filter is practiced to get the effective prediction algorithm of low flow runoff. Even though the total discharge of runoff through the orifice of each tank should be similar to the observed discharge, the tank model which can show the various basin characteristic is influenced by the runoff circumstances. As a result of the real-time estimation of the tank model parameter by the state-space type of Kalman filter, the variation of runoff circumstances is static when the convergence of observed value and estimated value keeps the ficed high point. The parameter of tank model which is estimated by Kalman filter shows good result for low flow and reasonable adaptability where flow change abruptly. The Kalman filter method is proved to give better result than Automatic structure estimation method.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.10
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• pp.874-881
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• 2001

By showing the existence of the ARE solution and the convergence property of the DRE solution, this paper proves that a Kalman filter for the linear system with the unobservable bias is stable. It is also shown that the Kalman filter has a biased steady state estimation error whose covariance is affected mainly by the unobservable bias. Finally, the results are illustrated through a 2nd order system example including the inertial navigation system.