• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 추계학술대회논문집A
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• pp.693-698
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• 2000

Aspects of classical information theory, such as rate distortion theory, investigate how to encode and decode information from an independently identically distributed source so that the asymptotic distortion rate between the source and its quantized representation is minimized. However, in most natural dynamics, the source state is highly corrupted by disturbances, and the measurement contains the noise. In recent coder-estimator sequence is developed for state estimation problem based on observations transmitted with finite communication capacity constraints. Unlike classical estimation problems where the observation is a continuous process corrupted by additive noises, the condition is that the observations must be coded and transmitted over a digital communication channel with finite capacity. However, coder-estimator sequence does not provide such a quantitative analysis as a variance for estimation error. In this paper, under the assumption that the estimation error is Gaussian distribution, a variance for coder-estimation sequence is proposed and its fitness is evaluated through simulations with a simple example.

• 한국지능시스템학회논문지
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• 제21권2호
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• pp.178-185
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• 2011

본 논문은 상태변수 평준화 및 되먹임구조를 이용하여 무인잠수정의 위치추정을 개선하기 위한 다중센서 융합 기반의 위치추정 알고리즘을 제안한다. 이를 위해 먼저 상대적으로 오차가 큰 주 센서인 INS와 오차가 작은 보조센서인 DVL에서 측정되는 상태변수를 예측단계 이전에 융합하여 상태변수 평준화 과정을 수행한다. 그 다음, 평준화된 상태변수를 각 필터에 입력하여 예측 및 수정단계의 칼만 필터링 과정을 통해 최종 수정된 상태변수를 융합시키며, 마지막으로 이를 다시 주센서에 되먹임함으로서 무인잠수정의 위치추정을 개선한다. 평가를 위해 무인잠수정의 기동모델에 대한 시뮬레이션을 실시하여 기동경로를 생성하고 제안 알고리즘을 적용하여 위치추정 성능을 확인한다. 평가 결과, 제안 알고리즘이 다중센서 융합 알고리즘 중 가장 우수한 위치추정 성능을 보였으며, 또한 기동침로가 변경되는 구간에서도 강인한 위치추정이 가능하다는 것이 증명되었다.

• Advances in aircraft and spacecraft science
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• 제7권6호
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• pp.495-516
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• 2020

Nonlinear state estimation is a desirable and required technique for many situations in engineering (e.g., aircraft/spacecraft tracking, space situational awareness, collision warning, radar tracking, etc.). Due to high standards on performance in these applications, in the last few decades, there was an increasing demand for methods that are able to provide more accurate results. However, because of the mathematical complexity introduced by the nonlinearities of the models, the nonlinear state estimation uses techniques that, in practice, are not so well-established which, leads to sub-optimal results. It is important to take into account that each method will have advantages and limitations when facing specific environments. The main objective of this paper is to provide a comprehensive overview and interpretation of the most well-known methods for nonlinear state estimation with Gaussian priors. In particular, the Kalman filtering methods: EKF (Extended Kalman Filter), UKF (Unscented Kalman Filter), CKF (Cubature Kalman Filter) and EnKF (Ensemble Kalman Filter) with an aerospace perspective.

• 조명전기설비학회논문지
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• 제17권3호
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• pp.103-109
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• 2003

전력계통의 자동화에 있어 측정되는 데이터의 정확성은 기본적으로 확립되어야 할 요건이다. 측정된 데이터는 통신 장애나 기기 자체의 오동작으로 인해 오차를 포함할 수 있으며, 이러한 오차는 상태추정에 의해 줄어들 수 있으나, 잘못 측정된 일부 값에 의해 상태추정의 전반적인 신뢰도가 저하될 수 있다. 본 논문에서는 가중최소 상태추정과 카이스퀘어 이론에 근거한 잘못 측정된 데이터의 검출 알고리즘이 제안되었으며, 이를 국내 154kV 변전소에 적용하였다. C 언어와 비쥬얼 베이직을 사용하여 GUI 환경의 시뮬레이터를 개발하였으며, 다른 운용 시스템과의 연계를 고려하여 TCP/IP 프로토콜을 탑재하였다.

• 전기의세계
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• 제30권11호
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• pp.728-733
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• 1981

Most of real world systems are highly non-linear. But due to difficulties in analyzing and dealing with it, only the linear system theory is well estabilished. Bilinear system where state and control are linear but not linear jointly is introduced. Here shows that optimal state estimation of stochastic bilinear system requirs infinite dimensional filter, thus onesub-optimal estimator for this system is suggested.

• 대한조선학회논문집
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• 제44권5호
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• pp.534-541
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• 2007

This paper suggests the mathematical method for the estimation of the required engine output for WIG crafts. The engine size of a WIG craft Is a key parameter in the design stage, because WIGs should overcome the hump drag during the take-off. Therefore, it is very important for a WIG designer to estimate required power and state change during take-off. The mathematical method was developed based on the steady planing state model of a planing boat. Through numerical calculations on various take-off states, it was found that the suggested method could give reasonable estimation of required power and state change during take-off.

• 제어로봇시스템학회논문지
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• 제20권2호
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• pp.170-174
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• 2014

In this paper, an unknown input estimation method via the optimal FIR smoother is proposed for linear discrete-time systems. The unknown inputs are represented by random walk processes and treated as auxiliary states in augmented state space models. In order to estimate augmented states which include unknown inputs, the optimal FIR smoother is applied to the augmented state space model. Since the optimal FIR smoother is unbiased and independent of any a priori information of the augmented state, the estimates of each unknown input are independent of the initial state and of other unknown inputs. Moreover, the proposed method can be applied to stochastic singular systems, since the optimal FIR smoother is derived without the assumption that the system matrix is nonsingular. A numerical example is given to show the performance of the proposed estimation method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1469-1472
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• 1997

For system state estimation, existing LMS type esimators widely used. For example Kalman filter is one of them. In this paper, a state estimator is derived for the H$_{\infty}$ norm of the estimation error spectrum matrix to be minimized. For the solution of this problem classical NP interpolation problem is used. Also, by deriving the duality between the filter problem and the well-known H$_{\infty}$ control problem, another solution is obtained. The computer simuation results show that H$_{\infty}$ estimator has less estimation error and so this is better than the existing Kalman filter estimator.or.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권2호
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• pp.566-578
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• 2018

In this paper, we design a segment training based individual channel estimation (STICE) scheme for the classical two-way relay network (TWRN) with multi-pair sources (MPS) and amplify-and-forward (AF). We adopt the linear minimum mean square error (LMMSE) channel estimator to minimize the mean square error (MSE) without channel estimation error, where the optimal power allocation strategy from the relay for different sources is obtained. Then the MSE gains are given with different source pairs among the proposed power allocation scheme and the existing power allocation schemes. Numerical results show that the proposed method outperforms the existing ones.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.303-304
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• 2008

For the state estimation problem, the weighted least squares (WLS) method and the fast decoupled method are widely used at present. However, these algorithms can converge to local optimal solutions. Recently, modern heuristic optimization methods such as Particle Swarm Optimization (PSO) have been introduced to overcome the disadvantage of the classical optimization problem. However, heuristic optimization methods based on populations require a lengthy computing time to find an optimal solution. In this paper, we used PSO to search for the optimal solution of state estimation in power systems. To overcome the shortcoming of heuristic optimization methods, we proposed parallel processing of the PSO algorithm based on the PC cluster system. the proposed approach was tested with the IEEE-118 bus systems. From the simulation results, we found that the parallel PSO based on the PC cluster system can be applicable for power system state estimation.