• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1278-1282
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• 2003

A new interacting multiple model (IMM) method using intelligent input estimation (IIE) is proposed to track a maneuvering target. In the proposed method, the acceleration level for each sub-model is determined by IIE-the estimation of the unknown acceleration input by a fuzzy system using the relation between maneuvering filter residual and non-maneuvering one. The genetic algorithm (GA) is utilized to optimize a fuzzy system for a sub-model within a fixed range of acceleration input. Then, multiple models are composed of these fuzzy systems, which are optimized for different ranges of acceleration input. In computer simulation for an incoming ballistic missile, the tracking performance of the proposed method is compared with those of the input estimation (IE) technique and the adaptive interacting multiple model (AIMM) method.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.87-87
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• 2000

In this paper, an integrated design of fault detection, diagnosis and reconfigurable control tot multi-input and multi-output system is proposed. It is based on the interacting multiple model estimation algorithm, which is one of the most cost-effective adaptive estimation techniques for systems involving structural and/or parametric changes. This research focuses on the method to recover the performance of a system with failed actuators by switching plant models and controllers appropriately. The proposed scheme is applied to a fault tolerant control design for flight control system.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.234-239
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• 2006

In this paper, we propose an interacting multiple model (IMM) method using intelligent tracking filter with fuzzy gain to reduce tracking errors for maneuvering targets. In the proposed filter, the unknown acceleration input for each sub-model is determined by mismatches between the modelled target dynamics and the actual target dynamics. After a acceleration input is detected, the state estimates for each sub-filter are modified. To modify the accurate estimation, we propose the fuzzy gain based on the relation between the filter residual and its variation. To optimize each fuzzy system, we utilize the genetic algorithm (GA). The tracking performance of the proposed method is compared with those of the adaptive interacting multiple model(AIMM) method and input estimation (IE) method through computer simulations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.99-102
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• 2003

A new interacting multiple model (IMM) method using genetic algorithm (GA)-based intelligent input estimation(IIE) is proposed to track a maneuvering target. In the proposed method, the acceleration level for each sub-model is determined by IIE-the estimation of the unknown acceleration input by a fuzzy system using the relation between maneuvering filter residual and non-maneuvering one. The GA is utilized to optimize a fuzzy system fur a sub-model within a fixed range of acceleration input. Then, multiple models are composed of these fuzzy systems, which are optimized for different ranges of acceleration input. In computer simulation for an incoming ballistic missile, the tracking performance of the proposed method is compared with those of the input estimation(IE) technique and the adaptive interacting multiple model (AIMM) method.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.943-945
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• 1996

In this paper, we present an algorithm that allows tracking of a target using measurements obtained from a sensor with limited resolution. The Interacting Multiple Model (IMM) algorithm has been shown to be one of the most cost-effective estimation schemes for hybrid systems. The approach consists of IMM algorithm combined with a coupled version of the Joint Probabilistic Data Association Filter for the target that splits into two targets.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.387-393
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• 2002

The enhancement technique of noise signal using mixture HFM (Midden Filter Model) are proposed. Given the parameters of the clean signal and noise, noisy signal is modeled by a linear state-space model with Markov switching parameters. Estimation of state vector is required for estimating original signal. The estimation procedure is based on mixture interacting multiple model (MIMM) and the estimator of speech is given by the weighted sum of parallel Kalman filters operating interactively. Simulation results showed that the proposed method offers performance gains relative to the previous results with slightly increased complexity.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.5
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• pp.445-453
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• 2001

The multiple targets tracking problem has been one of the main issues in the radar applications area in the last decade. Besides the standard Kalman filtering, various methods including the variable dimen-sion filter, input estimation filter, interacting multiple model(IMM) filter, dederated variable dimension filter with input estimation, etc., have proposed to address the tracking and sensor fusion issues. In this pa- per, two existing tracking algorithm, i.e, the IMM filter and the variable dimension filter with input estima-tion(VDIE), are combined for the purpose of improving the tracking performance for maneuvering targets. To evaluate the tracking performance of the proposed algorithm, three typical maneuvering patterns, i.e., waver, pop-up, and high-diver motions, are defined and are applied to the modified IMM filter as well as the standard IMM filter. The smaller RMS tracking errors, in position and velocity, of the modified IMM filter than the standard IMM filter are demonstrated though computer simulations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.425-428
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• 2006

In this paper, we propose an interacting multiple model (IMM) method using intelligent tracking filter with fuzzy gain to reduce tracking errors for maneuvering targets. In the proposed filter, to exactly estimate for each sub-model, we propose the fuzzy gain based on the relation between the filter residual and its variation. To optimize each fuzzy system, we utilize the genetic algorithm (GA). Finally, the tracking performance of the proposed method is compared with those of the adaptive interacting multiple model (AIMM) method and input estimation (IE) method through computer simulations.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.1
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• pp.84-92
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• 2015

This paper proposes a new approach to reduce the target estimation error of the measurement angle, especially applied to the medium and long range surveillance radar. If the target has no maneuver and no change in heading direction for a certain time interval, the predicted angle of interacting multiple model(IMM) from the previous track information can be used to reduce the angle estimation error. The proposed method is simulated in 2 scenarios, a scenario with a non-maneuvering target and a scenario with a maneuvering target. The result shows that the new fusion solution(weighted IMM) with the predicted azimuth and the measured azimuth is worked properly in the two scenarios.

• Journal of Electrical Engineering and Technology
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• v.7 no.2
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• pp.255-263
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• 2012

This paper presents the smart interacting multiple model (SIMM) using the concept of predicted point and maximum noise level. Maximum noise level means the largest value of the mere noises. We utilize the positional difference between measured point and predicted point as acceleration. Comparing this acceleration with the maximum noise level, we extract the acceleration to recognize the characteristics of the target. To estimate the acceleration, we propose an optional algorithm utilizing the proposed method and the Kalman filter (KF) selectively. Also, for increasing the effect of estimation, the weight given at each sub-filter of the interacting multiple model (IMM) structure is varying according to the rate of noise scale. All the procedures of the proposed algorithm can be implemented by an on-line system. Finally, an example is provided to show the effectiveness of the proposed algorithm.