• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.167-170
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• 2002

In this paper, we propose a new tracking algorithm that achieves good tracking performance in manuevering targets while capping the computation load to“low”Kalman Filter (KF) is generally known to be poor in tracking manuevering targets. IMM, on the other hand, compensates the weakness inherent in the mundane KF and is considered as a promising alternative for tracking maneuvering targets. However, IMM suffers from substantially increased computational load as the number of models increases. To remedy this problem, we propose a new method focused to reducing the computational load and attaining the desirable tracking performance at least as good that of IMM. It is achieved by essentially adopting the structure of IMM and injecting Optimal Two-Stage Kalman Estimator (OTSKE). The representative simulation shows a reduction in computational load with the proposed OTSKE but further reduction is shown achieved (by about 58%) with the Interacting Acceleration Compenstation(IAC)-OTSKE approach.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.12
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• pp.908-917
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• 1987

Most of moving targets are modelled as nonlinear dynamic equations. In recent years, the extended Kalman filter is frequently used for estimating their behaviors. The conditional Gaussian filter is more suitable than extended kalman filter in the filtering problem of nonlinear systems. But extended Kalman filter and conditional Gaussian filter often do not give optimal estimates and fail to track target trajectories because of its properties. Therefore it is desirable to use adaptive techniques to adapt target maneuvers. In this paper, we will discuss adaptive filtering technique using innovation process based on extended Kalman filter in real time, and suggest another maneuver estimation method using MRAS technique.

• Journal of Ocean Engineering and Technology
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• v.36 no.1
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• pp.50-60
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• 2022

The deep ocean engineering basin (DOEB) of the Korea Research Institute of Ship and Ocean Engineering (KRISO) is equipped with an extreme-environment reproduction facility that can analyze the motion characteristics of offshore structures and ships. In recent years, there have been requirements for a wide range of six-degree-of-freedom (6-DOF) motion measurements for performing maneuvering tests and free-running tests of target objects (offshore structures or ships). This study introduces the process of developing a wide-area motion measurement technology by incorporating the auto-tracking technology of the towing carriage system to overcome the existing 6-DOF motion measurement limitation. To realize a wide range of motion measurements, the automatic tracking control system of the towing carriage in the DOEB was designed as a speed control method. To verify the control performance, the characteristics of the towing carriage according to the variation in control gain were analyzed. Finally, a wide range of motions was tested using a model test object (a remotely operated vehicle (ROV)), and the wide-area motion measurement technology was implemented using an automatic tracking control system for a towing carriage.

• 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.

• Journal of Advanced Navigation Technology
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• v.5 no.1
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• pp.74-84
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• 2001

Recently a new algorithm which combines advantages of the IMM and IE methods has been suggested. The combined-IMM/IE algorithm could improve the performance to some extent. However, the problem of large increase of tracking error near the maneuver detection due to the sudden maneuver input has not been solved. In this paper, we propose two schemes which can resolve this limitations of combined-IMM/IE algorithm. For illustrations of the performance of the proposed methods. Monte-Carlo simulations are carried out and the results are analyzed.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1355-1360
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• 1990

This paper describes the application of the recently developed feedback linearization technique to the design of a new command to line-of-sight (CLOS) guidance law for skid-to-turn (STT) missiles. The key idea lies in converting the three dimensional CLOS guidance problem to the tracking problem of a time-varying nonlinear system. Then, using a feeedback linearizing approach to tracking in nonlinear systems, we design a three dimensional CLOS guidance law that can ensure zero miss distance for a randomly maneuvering target. Our result may shed new light on the role of the feedforward acceleration terms used in the earlier CLOS guidance laws. Furthermore, we show that the new CLOS guidance law can be computationally simplified without performance degradation. This is made possible by dropping out the terms in the new CLOS guidance law, which obey the well-known matching condition.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.10
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• pp.873-878
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• 2013

An EOTS (Electro-Optical Tracking System) provides stabilized images while tracking a moving target. This paper presents a novel concept of driving the outer gimbal first for improving the maneuverability of an EOTS, contrary to the conventional inner gimbal mode. It has the advantages of faster positioning performance and stable operation in Nadir-point. Analysis of frequency responses reveal that the present scheme results in a wider control bandwidth and larger gain margin, compared to those of the previous one. The actual experimental results confirm that the maneuvering is stable although the input command has a large angular acceleration.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.3
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• pp.52-58
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• 2009

If a moving target has a linear characteristics, the Kalman filter can estimate relatively accurate the location of a target, but this performance depends on how the dynamic status characteristics of the target is accurately modeled. In many practical problems of tracking a maneuvering target, a simple kinematic model can fairly accurately describe the target dynamics for a wide class of maneuvers. However, since the target can exhibit a wide range of dynamic characteristics, no fixed SKF(Simple Kalman filter) can be matched to estimate, to the required accuracy, the states of the target for every specific maneuver. In this paper, a new AKF(Active Kalman filter) is proposed to solve this problem The process noise covariance level of the Kalman filter is adjusted at each time step according to the study result which uses the neural network algorithm. It is demonstrated by means of a computer simulation that the tracking capability of the proposed AKF(Active Kalman filter) is better than that of the SKF(Simple Kalman Filter).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.139-147
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• 2023

The compact tracking radar system is a pulsed radar tracking system that searches, detects, and tracks targets in real time against aircraft targets with a small RCS(Radar Cross Section) maneuvering at high speed. This paper describes the development of comprehensive performance test equipment to verify the performance of the radar system in a anechoic chamber environment. Describes the design and manufacture of comprehensive performance test equipment to meet requirements, including the generation of simulated target signals to simulate aircraft target signals to verify performance in the laboratory environment of radar systems. It also describes a GUI(Graphic User Interface) program to check performance through a test in conjunction with the tracking radar system. Verify the comprehensive performance test equipment implemented through the performance test.