• 한국군사과학기술학회지
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• 제26권1호
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• pp.10-21
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• 2023

For various target tracking applications, it is well known that the Kalman filter is the optimal estimator(in the minimum mean-square sense) to predict and estimate the state(position and/or velocity) of linear dynamical systems driven by Gaussian stochastic noise. In the case of nonlinear systems, Extended Kalman filter(EKF) and/or Unscented Kalman filter(UKF) are widely used, which can be viewed as approximations of the(linear) Kalman filter in the sense of the conditional expectation. However, to implement EKF and UKF, the exact dynamical model information and the statistical information of noise are still required. In this paper, we propose the recurrent neural-network based Kalman filter, where its Kalman gain is obtained via the proposed GRU-LSTM based neural-network framework that does not need the precise model information as well as the noise covariance information. By the proposed neural-network based Kalman filter, the state estimation performance is enhanced in terms of the tracking error, which is verified through various linear and nonlinear tracking problems with incomplete model and statistical covariance information.

• Smart Structures and Systems
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• 제17권6호
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• pp.903-915
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• 2016

The classical Kalman filter (KF) provides a practical and efficient state estimation approach for structural identification and vibration control. However, the classical KF approach is applicable only when external inputs are assumed known. Over the years, some approaches based on Kalman filter with unknown inputs (KF-UI) have been presented. However, these approaches based solely on acceleration measurements are inherently unstable which leads poor tracking and so-called drifts in the estimated unknown inputs and structural displacement in the presence of measurement noises. Either on-line regularization schemes or post signal processing is required to treat the drifts in the identification results, which prohibits the real-time identification of joint structural state and unknown inputs. In this paper, it is aimed to extend the classical KF approach to circumvent the above limitation for real time joint estimation of structural states and the unknown inputs. Based on the scheme of the classical KF, analytical recursive solutions of an improved Kalman filter with unknown excitations (KF-UI) are derived and presented. Moreover, data fusion of partially measured displacement and acceleration responses is used to prevent in real time the so-called drifts in the estimated structural state vector and unknown external inputs. The effectiveness and performance of the proposed approach are demonstrated by some numerical examples.

• Smart Structures and Systems
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• 제18권3호
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• pp.375-387
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• 2016

The classical Kalman filter (KF) can provide effective state estimation for structural identification and vibration control, but it is applicable only when external inputs are measured. So far, some studies of Kalman filter with unknown inputs (KF-UI) have been proposed. However, previous KF-UI approaches based solely on acceleration measurements are inherently unstable which leads to poor tracking and fictitious drifts in the identified structural displacements and unknown inputs in the presence of measurement noises. Moreover, it is necessary to have the measurements of acceleration responses at the locations where unknown inputs applied, i.e., with collocated acceleration measurements in these approaches. In this paper, it aims to extend the classical KF approach to circumvent the above limitations for general real time estimation of structural state and unknown inputs without using collocated acceleration measurements. Based on the scheme of the classical KF, an improved Kalman filter with unknown excitations (KF-UI) and without collocated acceleration measurements is derived. Then, data fusion of acceleration and displacement or strain measurements is used to prevent the drifts in the identified structural state and unknown inputs in real time. Such algorithm is not available in the literature. Some numerical examples are used to demonstrate the effectiveness of the proposed approach.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1897-1902
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• 2005

The objective of this paper is to estimate the concentration distribution in flow field inside the pipeline based on electrical impedance tomography. Special emphasis is given to the development of dynamic imaging technique for two-phase field undergoing a rapid transient change. Nonlinearity-compensation extended Kalman filter is employed to cope with unexpected measurement uncertainty. The nonlinearity-compensation extended Kalman filter compensates for the influence of measurement uncertainty and solves the instability of extended Kalman filter. Extensive computer simulations are carried out to show that nonlinearity-compensation extended Kalman filter has enhanced estimation performance especially in the unexpected measurement environment.

• Journal of Communications and Networks
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• 제9권3호
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• pp.219-228
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• 2007

In this paper, we present a novel blind adaptive multiuser detector structure and three robust recursive filters to improve the performance in CDMA environments: Sigma point kalman filter (SPKF), particle filter (PF), and Gaussian mixture sigma point particle filter (GMSPPF). Our proposed robust recursive filters have superior performance over a conventional extended Kalman filter (EKF). The proposed multiuser detector algorithms initially use Kalman prediction form to estimated channel parameters, and unknown data symbol be predicted. Second, based on this predicted data symbol, the robust recursive filters (e.g., GMSPPF) is a refined estimation of joint multipaths and time delays. With these estimated multipaths and time delays, data symbol detection is carried out (Kalman correction form). Computer simulations show that the proposed algorithms outperform the conventional blind multiuser detector with the EKF. Also we can see it provides a more viable means for tracking time-varying amplitudes and time delays in CDMA communication systems, compared to that of the EKF for near-far ratio of 20 dB. For this reason, it is believed that the proposed channel estimators can replace well-known filter such as the EKF.

• 대한기계학회논문집A
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• 제24권1호
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• pp.7-15
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• 2000

This paper suggests an auto-tuning I'll) control algorithm that uses the advantage of PID controller and improves the system performance. The PID gains being designed by th- conventional method are tuned through the plant parameter estimation. The Extended Kalman Filter is used for the estimation. It works as an observer and noise filter. Moreover, as the plant state and the uncertain parameter could be estimated simultaneously, the proposed algorithm is very useful in the tracking control of a system with uncertain parameter. The auto-tuning I'll) controller could maintain the system performance in the case that the plant parameters are uncertain or varying. The proposed control algorithm requires a correct estimation of the plant parameter. The controller stability and the performance is considered through the stability criteria and a servo motor model. The Kalman filter estimates the most sensitive plant parameter, which is determined by the sensitivity analysis.

• 전력전자학회논문지
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• 제14권4호
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• pp.261-267
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• 2009

최대전력점 추적기는 태양광 발전시스템의 대표적인 기능이다. 최대 전력점을 추종하기 위해서는 태양전지의 전압과 전류의 측정을 필요로 한다. 만약 측정 신호에 노이즈가 포함되어 있으면 발생되는 전력이 감소되어 태양광 발전의 효율이 감소하게 된다. 노이즈가 포함된 신호에 확장 칼만 필터 이론을 적용하여 최적의 추정된 신호를 얻어 낼 수 있다. 칼만 필터는 랜덤 노이즈가 포함된 신호에서 최적의 신호를 얻어내는데 사용된다. 또한 칼만 필터의 적용결과로 인덕터 전류와 같은 측정하지 않는 신호도 센서리스 추정이 가능하다. 본 논문에서는 시스템 모델링 방법과 확장 칼만 필터 설계 방법이 소개된다. 실험 결과로서 제안된 제어기의 성능을 확인하였다.

• 물과 미래
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• 제28권2호
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• pp.125-136
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• 1995

본 연구의 목적은 탱크모형의 매개변수를 시행착오범으로 산정할 경우, 불확실성을 개선하기 위해 Kalman filter로 매개변수를 실시간 예측하여 저수유출의 예측에 효과적인 알고리즘을 얻고자 하였다. 유역특성을 다양한 구조로 나타낼 수 있는 탱크모형은 각 단 탱크에 부착된 유출공으로부터 유출한 총 유출량이 관측유량에 유사하게 나타나야 하지만 유출환경의 영향으로 수렴성이 좋지 않았다. 이러한을 보완하기 위하여 탱크모형의 매개변수를 Kalman filter의 상태공간 모형에 의하여 실시간으로 추정한 결과, 시간 경과에 따라 추정치와 관측치의 수렴도가 높아 일정한 값을 유지하였으며, 이때의 유출환경을 나타내는 상태공간의 매개변수변화가 정적임을 알 수 있었다. 따라서 Kalman filter에 의한 탱크모형의 매개변수 추정기법은 저수유출 예측에 특히 효율성이 좋았으며 유량이 급변하는 곳에서도 어느 정도 적응하여 기존 탱크모형의 구조를 자동기법으로 정하는 예측시스템 보다 유출예측 시스템에 의한 탱크모형의 구조적 알고리즘이 적합한 모형임을 입증하였다.

• 대한기계학회논문집A
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• 제29권1호
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• pp.59-66
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• 2005

In this paper, a robust stationary Kalman filter is designed by minimizing selected performance indices so that it is less sensitive to uncertainties. The uncertainties include not only stochastic factors such as process noise and measurement noise, but also deterministic factors such as unknown initial estimation error, modeling error and sensing bias. To reduce the effect on the uncertainties, three performance indices that should be minimized are selected based on the quantitative error analysis to both the deterministic and the stochastic uncertainties. The selected indices are the size of the observer gain, the condition number of the observer matrix, and the estimation error variance. The observer gain is obtained by optimally solving the multi-objectives optimization problem that minimizes the indices. The robustness of the proposed filter is demonstrated through the comparison with the standard Kalman filter.

• 제어로봇시스템학회논문지
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• 제14권8호
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• pp.740-744
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• 2008

In this paper, we propose a new time-to-go estimation filter for PN guided missiles. The proposed estimator is derived based on the approximation of the length of the PNG homing trajectory that we newly introduced using the special coordinate system. The coordinate system is convenient for taking the target movement into account. In addition, compared with the previous time-to-go estimation techniques, the parameters required for evaluating the length can be obtained only with the seeker measurements. Moreover, the seeker measurement error statistics can effectively be considered since our filter is derived based on the Kalman filter theory. Simulation result for a typical anti-ship see-skimming missile homing trajectory shows the excellent performance of the proposed filter.