• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
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• pp.303-306
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• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKF uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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• pp.419-422
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• 2005

In this note, the Takagi-Sugeno (T-S) fuzzy-model-based state estimator using standard Kalman filter theory is investigated. In that case, the dynamic system model is represented the T-S fuzzy model with the fuzzy state estimation. The steady state solutions can be found for proposed modeling method and dynamic system for maneuvering targets can be approximated as locally linear system. And then, modeled filter is corrected by the fuzzy gain which is a fuzzy system using the relation between the filter residual and its variation. This paper studies the T-S fuzzy model-based state estimator which the dynamic system can be approximated as linear system.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.311-314
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• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF. To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKP uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

• 한국지능시스템학회논문지
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• 제15권5호
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• pp.593-598
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• 2005

표적의 상태를 추정하기 위해 사용된 칼만 필터는 급 기동을 하거나 비선형적인 운동특성을 가지는 표적이 발생할 때, 모델은 상당한 오차를 유발하며 추적 성능은 현저히 저하될 수 있다. 이러한 문제점을 해결하기 위해서 본 논문에서는 기동하는 표적을 추정하기 위한 새로운 지능형 추정 알고리즘을 제안한다. 제안된 알고리즘은 유전 알고리즘에 기반한 퍼지 칼만 필터를 이용하여 실제 알지 못하는 표적의 가속도를 전체 프로세스 잡음으로 추정한 후, 보정된 필터의 잔여치와 변화를 이용한 퍼지 시스템으로 새로운 퍼지 이득을 추출하여 측정 예측 오차를 보정함으로써 한 개의 필터로 표적 움직임의 비선형성을 효과적으로 다룰 수 있다. 제안된 기법의 우수성을 검증하기 위해서 다중 모델 기법을 사용한 필터와 비교 모의실험을 하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권1호
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• pp.287-304
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• 2012

Spectrum sensing is an important functionality for cognitive users to look for spectrum holes before taking transmission in dynamic spectrum access model. Unlike previous works that assume perfect knowledge of the SNR of the signal received from the primary user, in this paper we consider a realistic case where the SNR of the primary user's signal is unknown to both fusion center and cognitive radio terminals. A Kalman filter based adaptive Takagi and Sugeno's fuzzy system is designed to make the global spectrum sensing decision based on the observed energies from cognitive users. With the capacity of adapting system parameters, the fusion center can make a global sensing decision reliably without any requirement of channel state information, prior knowledge and prior probabilities of the primary user's signal. Numerical results prove that the sensing performance of the proposed scheme outperforms the performance of the equal gain combination based scheme, and matches the performance of the optimal soft combination scheme.

• Journal of Electrical Engineering and Technology
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• 제13권1호
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• pp.287-297
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• 2018

To improve the performance of sensorless induction motor (IM) drives, a novel speed estimation method based on the real-time adaptive extended Kalman filter (RAEKF) is proposed in this paper. In this algorithm, the fuzzy factor is introduced to tune the measurement covariance matrix online by the degree of mismatch between the actual innovation and the theoretical. Simultaneously, the fuzzy factor can be continuously self-tuned tuned by the fuzzy logic reasoning system based on Takagi-Sugeno (T-S) model. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. Furthermore, a simple exponential function based on the fuzzy theory is used to reduce the computational burden, and the real-time performance of the system is improved. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results.

• 한국지능시스템학회논문지
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• 제16권2호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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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.

• 한국지능시스템학회논문지
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• 제13권2호
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• pp.131-136
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• 2003

기동표적 추적의 문제는 상태추정의 분야에서 수 십 년에 걸쳐 연구되어 왔다. 칼만 필터는 표적의 상태를 추정하기 위해 널리 사용되어 왔으나, 기동이 발생할 경우, 그 성능은 현저히 저하될 수 있다. 본 논문에서는 이러한 문제점을 해결하고, 기동표적을 효과적으로 추적하기 위해, DNA 코딩에 기반한 지능형 칼만 필터를 제안한다. 제안된 기법은 DNA 코딩에 기반한 퍼지 논리를 이용함으로써, 기존의 기법들이 가지는 수학적 한계를 극복하고, 기동표적을 효과적으로 추적할 수 있다. 컴퓨터 모의실험을 통하여, 제안된 기법의 추적 성능은 적응 상호작용 다중모델 기법 및 유전 알고리즘 기반 지능형 칼만 필터와 비교된다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.118-121
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• 2002

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 state of the target, but in the presence of a maneuver, its performance may be seliously 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.