• 대한전기학회논문지:전력기술부문A
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• 제48권5호
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• pp.560-570
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• 1999

This paper formulates the suboptimal robust Kalman filtering problem into two coupled Linear Matrix Inequality (LMI) problems by applying Lyapunov theory to the augmented system which is composed of the state equation in the uncertain linear system and the estimation error dynamics. This formulations not only provide the sufficient conditions for the existence of the desired filter, but also construct the suboptimal robust Kalman filter. The proposed filter can guarantee the optimized upper bound of the estimation error variance for uncertain systems with parametric uncertainties in both the state and measurement matrices. In addition, this paper shows how the problem of finding the minimizing solution subject to Quadratic Matrix Inequality (QMI), which cannot be easily transformed into LMI using the usual Schur complement formula, can be successfully modified into a generic LMI problem.

• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.2107-2113
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• 2014

Particle filtering (PF) has shown its outperforming results compared to that of classical Kalman filtering (KF), particularly for highly nonlinear problems. However, PF may not be universally superior to the extended KF (EKF) although the case (i.e. an example that the EKF outperforms PF) is seldom reported in the literature. Particularly, PF approaches show degraded performance for problems where the state noise is very small or zero. This is because particles become identical within a few iterations, which is so called particle impoverishment (PI) phenomenon; consequently, no matter how many particles are employed, we do not have particle diversity regardless of if the impoverished particle is close to the true state value or not. In this paper, we investigate this PI phenomenon, and show an example problem where a classical KF approach outperforms PF approaches in terms of mean squared error (MSE) criterion. Furthermore, we compare the processing speed of the EKF and PF approaches, and show the better speed performance of classical EKF approaches. Therefore, PF approaches may not be always better option than the classical EKF for nonlinear problems. Specifically, we show the outperforming result of unscented Kalman filter compared to that of PF approaches (which are shown in Fig. 7(c) for processing speed performance, and Fig. 6 for MSE performance in the paper).

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.478-481
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• 1996

Tracking a target of versatile maneuver recently demands a stable adaptation of tracker, and the multiple model techniques are being developed because of its ability to produce useful information of target maneuver. This paper presents the way to apply the multiple model method in a moving-target and moving-platform scenario, and the estimation and prediction results better than those of single Kalman filter.

• 한국항공우주학회지
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• 제40권2호
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• pp.108-117
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• 2012

본 논문은 비선형 필터 기법에 따른 지형참조항법 성능 분석에 관한 연구를 수행하였다. 지형참조항법에 사용되는 기본 필터에는 확장 칼만 필터(EKF)가 있다. 본 연구는 EKF 원형외에 반복형 EKF(IEKF), stochastic linearization(SL) 조건이 추가된 EKF-SL과 unscented Kalman Filter(UKF) 알고리듬을 소개한다. 또한, 연속적(sequential) 필터 외에 일괄적(batch)필터 기법인 칼만 필터 무리(bank of Kalman filters)를 이용한 항법 기술도 비교군으로 추가하고 필터 간 항법 성능을 분석한다. 가상 궤적을 가진 항공기 시뮬레이션을 통해 초기위치 오차가 클 때도 강건한(robust) 필터로 stochastic linearization EKF가 선정되었으며, 다만 빠른 항법 해의 수렴이 요구될 때에는 칼만 필터 무리를 이용한 일괄적 필터가 효과적인 것으로 분석되었다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• Journal of applied mathematics & informatics
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• 제26권1_2호
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• pp.383-387
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• 2008

The problem of recursive filtering for discrete market model with unknown parameters is considered. In this paper, we develop an effective filtering algorithm for discrete market systems with unknown parameters and the error covariance equation determining the accuracy of the proposed algorithm is derived.

• 대한전기학회논문지
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• 제37권9호
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• pp.636-645
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• 1988

A new filter algorithm is suggested improving structurally the conventional extended Kalman filter of which the performance is dependent on the selection of the reference axes, by use of line-of-sight axes and gain rotation technique. The implementation method using microcomputer which implements tracking Kalman filter is introduced in terms of hardware and software. Then, through the simulation the performance of suggested filter is compared with that of conventional extended Kalman filter and the possibility of the real time tracking of moving target is investigated.

• 물과 미래
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• 제18권2호
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• pp.113-117
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• 1985

• 물과 미래
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• 제18권1호
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• pp.26-30
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• 1985

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2000년도 봄 학술발표회논문집
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• pp.207-214
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• 2000

In this paper, a study of the effenctiveness and convergency of five damage measures for structural damage detection of 2-dimensional truss structure using the extended Kalman filtering algorithm is presented. These damage measures are associated with the change in mode shape and displacement due to structural damage. Damage measures contain the change in natural frequency, mode shape, curvature of mode shape, displacement of static force and curvature of displacement of static force. The effectiveness and convergency of these damage measures by using extended Kalman filtering algorithm are demonstrated with the numerical examples.