• 한국지능시스템학회논문지
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• 제17권7호
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• pp.855-861
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• 2007

실제 시스템 적용에 있어서, IMM에 기초한 위치 추적 알고리즘은 불확실한 표적 기동에 대해서 강인한 성능, 적은 연산량, 간편한 설계 절차를 필요로 한다. 이 문제들을 해결하기 위해서 잘 정의된 기저 부모델 및 잘 조정된 모델 천이 확률에 기초한 적응 퍼지 IMM 알고리즘을 제안하였다. 시뮬레이션 결과는 제안된 알고리즘이 IMM에 기초한 알고리즘의 실제 적용에서 존재하는 문제점들을 효과적으로 해결할 수 있음을 보여준다.

• 한국항행학회논문지
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• 제5권1호
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• pp.74-84
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• 2001

IMM과 IE의 장점을 혼합한 IMM/IE 혼합 알고리즘이 근래에 제시되었다. 이 혼합방식은 IMM이나 IE 방식의 단점을 어느 정도 보완하였으나 기동이 발생하는 시점에서 필터의 성능이 급격히 저하되는 문제점을 갖고 있다. 본 논문에서는 이 IMM/IE 혼합 알고리즘의 이러한 문제점을 개선하기 위한 두 가지 방안을 제안하고 그 성능을 Monte-Carlo 시뮬레이션으로 예증한다.

• 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.

• International Journal of Control, Automation, and Systems
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• 제6권1호
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• pp.44-53
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• 2008

An interacting multiple model (IMM) estimation algorithm based on the mixing of the predicted state estimates is proposed in this paper for a right continuous jump-linear system model different from the left-continuous system model used to develop the existing IMM algorithm. The difference lies in the modeling of the mode switching time. Performance of the proposed algorithm is compared numerically with that of the existing IMM algorithm for noisy system identification. Based on the numerical analysis, the proposed algorithm is applied to target tracking with a large sampling period for performance comparison with the existing IMM.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 춘계학술대회 및 임시총회
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• pp.166-169
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• 2002

The accuracy in maneuvering target tracking using multiple models is caused by the suitability of each target motion model to be used. The interacting multiple model (IMM) algorithm and the adaptive IMM algorithm require the predefined sub-models and the predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers to construct multiple models. In this paper, to solve these problems intelligently, a genetic algorithm (GA) based-IMM method using fuzzy logic is proposed. In the proposed method, a sub-model is represented as a set of fuzzy rules to model the time-varying variances of the process noises of a new piecewise constant white acceleration model, and the GA is applied to identify this fuzzy model. The proposed method is compared with the AIMM algorithm in simulations.

• 전자공학회논문지B
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• 제30B권12호
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• pp.59-66
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• 1993

In this paper we propose an adaptive update rate tracking algorithm for a phased array radar, based on the interacting multiple model(IMM) algorithm. The purpose of the IMM algorithm hers is twofold: 1) to estimate and predict the target states, and 2) to estimate the level of the process noise. Using the estimate of the process noise level adapted to target dynamics, the update interval is determined to maintain a desired prediction accuracy so that the radar system load is minimized. The adaptive update rate tracking algorithm is implemented for a phased array radar and evaluated with Monte Carlo simulations on various trajectories. The evaluation results of the proposed algorithm and a standard Kalman filter without the adaptive update rate control are presented to compare.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.148-148
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• 2000

In this paper the IMM(Interacting Multiple model) algorithm using the MGEKF(Modified Gain Extended Kalman Filter) which modes are variances of the process noises is proposed to enhance the performance of maneuvering target tracking with bearing and frequency measurements. The state are composed of relative position, relative velocity, relative acceleration and doppler frequency. The mode probability is calculated from the bearing and frequency measurements. The proposed algorithm is tested a series of computer simulation runs.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권1호
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• pp.16-21
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• 2003

The accuracy in maneuvering target tracking using multiple models is resulted in by the suitability of each target motion model to be used. The interacting multiple model (IMM) method and the adaptive IMM (AIMM) method require the predefined sub-models and the predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers in order to construct multiple models. In this paper, to solve these problems, a genetic algorithm(GA) based-IMM method using fuzzy logic is proposed. In the proposed method, the acceleration input is regarded as an additive noise and a sub-model is represented as a set of fuzzy rules to calculate the time-varying variances of the process noises of a new piecewise constant white acceleration model. The proposed method is compared with the AIMM algorithm in simulation.

• 한국전자통신학회논문지
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• 제12권5호
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• pp.763-770
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• 2017

항공기 전면에 장착되는 레이돔은 표적을 탐색 및 추적하는 데에 있어서 기동 중에 발생하는 다양한 이유로 굴절오차를 야기할 수 있다. 이러한 굴절오차는 마이크로파 탐색기가 허상표적을 탐지하고 있는 것을 의미한다. 3차원 공간상에서 항공기에 장착된 레이돔의 굴절률을 추정하는 목적으로 일반적으로 알려진 상호작용 다중모델(Interactive Multiple Model, IMM) 알고리듬을 적용한다. 하지만, 레이돔 굴절률과 같은 불확실한 시스템 모델의 계수를 추정할 수 있음에도 예측값의 범위를 벗어날 때에는 추정 성능을 보장할 수 없다. 본 논문에서는 레이돔 굴절률의 예측값을 IMM 알고리듬의 모드 계수로 두고 예측값을 갱신하는 방법을 제안하며, 제안한 방법의 레이돔 굴절률 추정 성능을 확인한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 통신소사이어티 추계학술대회논문집
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• pp.251-254
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• 2003

In this paper, we develop an effective method for estimating and predicting interference power strength using the IMM(Interacting Multiple Model) algorithm. Based on the proposed interference prediction algorithm, we adjust transmission power of mobile terminals to maintain a certain level of target signal - to - interference- plus -noise- ratio ( SINR ) at the base station. Results of numerical experiments are presented to show a performance profile of the proposed algorithm.