• Journal of Advanced Navigation Technology
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• v.4 no.1
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• pp.50-66
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• 2000

For a long time target maneuvers in tracking problem have been a difficult task to handle. Once a maneuvering such as abrupt change in target accelerations occur, the tracking fiter no longer yields a reasonable estimate of the target position. In order to resolve this cumbersome maneuvering problem. Advanced methods have here proposed : Colored noise, IE(Input Estimation), VD(Variable Dimension), IMM(Interaction Multiple Model), Jump-type processes and jerk model, etc. In this paper, tracking performance of the jerk model is analyzed. Jerk model in which the derivative of target acceleration is included as a state recently attracted considerable attraction. Firstly 3-dimensional Kalman filter is described on the basis of jerk model. Then using this filter, Monte-Carlo simulations are carried out and the filter formance with respect to the variation of jerk time-constant is analyzed. Especially, since jerk model's transient performance is expected to be poor, the performance of analysis of transient response of the model is included too.

• Journal of IKEEE
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• v.11 no.4
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• pp.137-144
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• 2007

Fire Control System(FCS) errors can be classified as hardware errors and software errors, and one of the software errors is from target tracking filter which estimates target's location, velocity, acceleration, and so on. It affects function of ballistic calculation equipment significantly. For gun to form predicted hitting point accurately and enhance hitting rate, we need status information of target's future location. Target tracking filter algorithms consist of Single Singer Model, Fixed Gain filter algorithm, IMM, PBIMM and so on. This paper will design IMM tracking filer, which is going to be! applied to domestic warship. Target tracking filter using CV model, Song model and CRT model for IMM tracking filter is made, and tracking ability is analyzed through Monte-Carlo simulation.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.855-861
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• 2007

In real system application, the IMM-based position tracking algorithm requires robust performance, less computing resources and easy design procedure with respect to the uncertain target maneuvering, To solve these problems, an adaptive fuzzy interacting multiple model (AFIMM) algorithm, which is based on the well-defined basis sub-models and well-adjusted mode transition probabilities (MTPs), is proposed. Simulation results show that the proposed algorithm effectively solves the problems in the real system application of the IMM-based position tracking algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.472-478
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• 2006

This paper presents a new fuzzy-neural-network based interacting multiple model (FNNBIMM) algorithm for tracking a maneuvering target. To effectively handle the unknown target acceleration, this paper regards it as additional noise, time-varying variance to target model. Each sub model characterized by the variance of the overall process noise, which is obtained on the basis of each acceleration interval. Since it is hard to approximate this time-varying variance adaptively owing to the unknown acceleration, the FNN is utilized to precisely approximate this time-varying variance. The error back-propagation method is utilized to optimize each FNN. To show the feasibility of the proposed algorithm, a numerical example is provided.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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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.

• 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 the Korea Society for Simulation
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• v.16 no.3
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• pp.65-74
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• 2007

A tactic manager which can change the behavior of a simulation model according to the tactic definition file has been studied and implemented. Based on the DEVS(discrete event system specification) formalism, we generated a simulation model which is equipped with the inter ace to the tactic manager. To demonstrate the effectiveness of the tactic manager, a target motion analysis simulation of the warfare between a submarine and a surface ship is simulated.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.131-136
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• 2003

The problem of maneuvering target tracking has been studied in the field of the state estimation over decades. The Kalman filter has been widely used to estimate the states of the target, but in the presence of a maneuver, its performance may be seriously degraded. In this paper, to solve this problem and track a maneuvering target effectively, DNA coding-based intelligent Kalman filter (DNA coding-based IKF) is proposed. The proposed method can overcome the mathematical limits of conventional methods and can effectively track a maneuvering target with only one filter by using the fuzzy logic based on DNA coding method. The tracking performance of the proposed method is compared with those of the adaptive interacting multiple model (AIMM) method and the GA-based IKF in computer simulations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.3
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• pp.445-451
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• 2002

In this paper, we propose a new tracking algorighm that achieves good tracking performance in manuevering targets while capping the computation load to "low". Kalman Filler (KF) is generally known to be poor in tracking maneuvering 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. approach.