• Journal of Industrial Technology
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• v.10
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• pp.15-20
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• 1990

A parallel processing algorithm for discrete Kalman filter, which is one of the most commonly used filtering technique in modern control, signal processing, and communication, is proposed. Previously proposed parallel algorithms to decrease the number of computations needed in the Kalman filter are the hierachical structures by distributed processing of measurements, or the systolic structures to disperse the computational burden. In this paper, a new parallel Kalman filter employing a structure similar to recursive doubling is proposed. Estimated values of state variables by the new algorithm converge with two times faster data processing speed than that of the conventional Kalman filter. Moreover it maintains the optimality of the conventional Kalman filter.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.10
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• pp.1057-1065
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• 1990

A parallel processing algorithm for the discrete Kalman filter, which is one of the most commonly used filtering techniques in modern control, signal processing, and communication, is proposed. To decrease the number of computations critical in the Kalman filter, previously proposed parallel algorithms are of the hierarchical structure by distributed processing of measurements, or of the systolic structure to disperse the computational burden. In this paper, a new parallel Kalman filter employing a structure similar to recursive doubling is proposed. Estimated valuse of state variables by the new algorithm converge faster to the true values because the new algorithm can process data twice faster than the conventional Kalman filter. Moreover, it maintains the optimality of the conventional Kalman filter.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.64-67
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• 1990

A parallel processing algorithm for discrete Kalman filter, which is one of the most commonly used filtering technique in modern control, signal processing, and communication. is proposed. Previously proposed parallel algorithms to decrease the number of computations needed in the Kalman filter are the hierachical structures by distributed processing of measurements, or the systolic structures to disperse the computational burden. In this paper, a new parallel Kalman filter employing a structure similar to recursive doubling is proposed. Estimated values of state variables by the new algorithm converge with two times faster data processing speed than that of the conventional Kalman filter. Moreover it maintains the optimality of the conventional Kalman filter.

• 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.

• Journal of Sensor Science and Technology
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• v.28 no.5
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• pp.277-283
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• 2019

In this paper, we propose a distributed fusion estimation for sensor networks using a receding horizon strategy. Communication channels were modelled as Markov jump systems, and a posterior probability distribution for communication channel characteristics was calculated and incorporated into the filter to allow distributed fusion estimation to handle path loss observation situations automatically. To implement distributed fusion estimation, a Kalman-Consensus filter was then used to obtain the average consensus, based on the estimates of sensors randomly distributed across sensor networks. The advantages of the proposed algorithms were then verified using a large-scale sensor network example.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.694-701
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• 2013

This paper deals with the problem of accurately tracking a single target moving through UWSNs (Underwater Wireless Sensor Networks) by employing underwater acoustic sensors. This paper addresses the issues of estimating the states of the target, and improving energy efficiency by applying a Kalman filter in a distributed architecture. Each underwater wireless sensor nodes composing the UWSNs is battery-powered, so the energy conservation problem is a critical issue. This paper provides an algorithm which increases the energy efficiency of each sensor node through WuS (Waked-up/Sleeping) and VM (Valid Measurement) selecting schemes. Simulation results illustrate the performance of the distributed tracking filter.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.173-182
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• 2005

In this paper, a target tracking algorithm for tracking maneuvering vehicles is presented. The overall algorithm belongs to the category of an interacting multiple-model (IMM) algorithm used to detect multiple targets using fused information from multiple sensors. First, two kinematic models are derived: a constant velocity model for linear motions, and a constant-speed turn model for curvilinear motions. Fpr the constant-speed turn model, a nonlinear information filter is used in place of the extended Kalman filter. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. The model-matched filter used in multi-sensor environments takes the form of a federated nonlinear information filter. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. In this paper, the structural features and information sharing principle of the federated information filter are discussed. The performance of the suggested algorithm using a Monte Carlo simulation under the two patterns is evaluated.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.220-224
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• 2005

칼만필터 알고리즘을 분포형 유출모형에 적용하였다. 관측 유량과 상태변수인 유역내 저류량을 갱신하고자 Q-S curve를 도입하였고, 갱신된 저류량과 모형에 의해 모의된 저류량의 비율을 유역 내 각 지점의 수위에 적용하므로써 분포화 된 상태변수를 효율적으로 갱신하였다. 갱신된 상태변수와 상태변수 오차의 시간갱신은 몬테 카를로 시뮬레이션을 이용하여 모의하였다.

• Journal of Navigation and Port Research
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• v.28 no.7
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• pp.635-640
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• 2004

In this paper, a tracking algorithm for autonomous navigation of automated guided vehicles (AGVs) operating in container terminals is presented. The developed navigation algorithm takes the form of a federated information filter used to detect other AGVs and avoid obstacles using fused information from multiple sensors. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. It is proved that the information state and the information matrix of the suggested filter, which are weighted in terms of an information sharing factor, are equal to those of a centralized information filter under the regular conditions. Numerical examples using Monte Carlo simulation are provided to compare the centralized information filter and the proposed one.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.4 s.23
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• pp.24-31
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• 2005

This paper presents an implementation of a real-time multi-sensor data fusion algorithm for Flight Test Computer. The sensor data consist of positional information of the target from a radar, a GPS receiver and an INS. The data fusion algorithm is designed by the 21st order distributed Kalman Filter which is based on the PVA model with sensor bias states. A fault detection and correction logics are included in the algorithm for bad measurements and sensor faults. The statistical parameters for the states are obtained from Monte Carlo simulations and covariance analysis using test tracking data. The designed filter is verified by using real data both in post processing and real-time processing.