• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2018년도 공동국제학술발표회
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• pp.72-72
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• 2018

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.598-601
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• 1995

This paper proposes a federated Kalman filter approach which utilizes information from multiple sensors and variable estimation model. Compared with the decentralized Kalman filter, the algorithm proposed in this paper demonstrates much better tracking performance in both maneuvering and constant velocity movement of the target.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.602-605
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• 1995

In this paper, we provide the new alogorithm for maneuvering target tracking in clutter environment using perception net. The perception net, as a structural representation of the sensing capabilities of a system, may supply the constraints that target must be satisfied with. The results form perception net applying to IMMPDA are compared with those obtained from IMMPDA.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 하계종합학술대회 논문집
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• pp.70-74
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• 1989

To track the target trajectory with maneuvers, unknown maneuvering inputs must be estimated. To do this the direct estimation algorithm using generalized least square technique is developed based on the procedure of failure detection and identification(FDI) theory. Through the simulation using maneuvering target scenario, tracking performance and efficiency of the algorithm developed here are investigated.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.108-113
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• 1991

In this paper, an improved method for tracking maneuvering target is proposed. The proposed tracking filter is constructed by combining the input estimation approach with the variable dimension filtering approach. In this approach, the filter also provides the estimated time instant at which target starts maneuver, when the target maneuver is detected. Using this estimated maneuvering time, the maneuver input is estimated and the tracking system changes to the maneuver model. Simulations are performed to demonstrate the efficiency of the proposed tracking filter.

• Journal of Electrical Engineering and information Science
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• 제1권2호
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• pp.39-44
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• 1996

An efficient interacting multiple mode(IMM) approach for tracking a maneuvering target with kinematic constraints is described based on the square root information filter(SRIF). The SRIF is employed instead of the conventional Kalman filter since it exhibits more efficient features in handling the kinematic constraints and improved numerical characteristics. The kinematic constraints are considered in the filtering process as pseudomeasurements where the degree of uncertainty is represented by the magnitude of the pseudomeasurement noise variance. The Monte Carlo simulations for the constant speed, maneuvering target are provided to demonstrate the improved tracking performance of the proposed algorithm.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.93-100
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• 2003

Accuracy in maneuvering target tracking using multiple models relies upon the suit-ability of each target motion model to be used. To construct multiple models, the interacting multiple model (IMM) algorithm and the adaptive IMM (AIMM) algorithm require predefined sub-models and predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers. To solve these problems, this paper proposes the GA-based IMM method as an intelligent tracking method for a maneuvering target. In the proposed method, the acceleration input is regarded as an additive process noise, a sub-model is represented as a fuzzy system to compute the time-varying variance of the overall process noise, and, to optimize the employed fuzzy system, the genetic algorithm (GA) is utilized. The simulation results show that the proposed method has a better tracking performance than the AIMM algorithm.

• 한국항해학회지
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• 제21권1호
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• pp.103-108
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• 1997

Mathematical model of maneuvering motion for a single-screw single-rudder ship is established and several applications to the special situations of maneuvering are attempted. While, the mathematical model for twin-screw twin-rudder ship is not presented so much, because that type of ship is not popular. Lee et al. have examined the characteristics of such ship by captive model tests in 1988, in Japan. This paper proposes new mathematical models for propeller effective wake (1 -${\omega}_p$) and effective neutral rudder angle ${delta}_R$ in the case of twin-screw twin-rudder ship. And some maneuvering motionse are calculated with proposed models and compared with exact simulations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1500-1504
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• 2005

This paper proposes the design methodology of genetic algorithm (GA)-based fuzzy Kalman filter for tracking the maneuvering target. The performance of the standard Kalman Filter (SKF) has been degraded because mismatches between the modeled target dynamics and the actual target dynamics. To solve this problem, we use the method to estimate the increment of acceleration by a fuzzy system using the relation between maneuver filter residual and non-maneuvering one. To optimize the fuzzy system, a genetic algorithm (GA) is utilized and this is then tuned by the fuzzy logic correction. Finally, the tracking performance of the proposed method has been compared with those of the input estimation (IE) technique and the intelligent input estimation (IIE) through computer simulations.

• 제어로봇시스템학회논문지
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• 제5권6호
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• pp.764-776
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• 1999

In this paper, a tracking algorithm for a maneuvering single target in the presence of multiple data from multiple sensors is investigated. Allowing individual sensors to function by themselves, the estimates from individual sensors on the same target are fused for the purpose of improving the state estimate. The filtering method adopted in the local sensors is the variable dimensional filter with input estimatio technique, which consists of a constant velocity model and a constant acceleration model. A posteriori probability for the maneuvering hypothesis is newly derived. It is shown that the relation function of the a posteriori probability is a function of only the covariance of the fused estimates. Simulation results are provided.