• International Journal of Aeronautical and Space Sciences
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• 제3권1호
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• pp.19-29
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• 2002

Decentralized filtering for a formation flight instrumentation system by INS/GPS integration is considered in this paper. An elaborate tuning method of the measurement noise covariance is suggested to compensate modeling errors caused by decentralizing the extended Kalman filter. It does not require large data transfer between formation vehicles. Covariance analysis exhibits the superior performance of the proposed approach when compared with the existent decentralized filter and the global filter, which has the target-filter performance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 정기총회 및 추계학술대회 논문집 학회본부
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• pp.323-325
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• 1993

In this paper, the decentralized suboptimal $H_2$ filtering problem is considered. An additional term is added to the centralized optimal $H_2$ filter so that the whole filter is decentralized. We derive a sufficient condition for existence of such decentralized filters. By employing the solution procedure for the exact model matching problem, we obtain a set of decentralized $H_2$ filters, and choose a suboptimal filter from this set of decentralized $H_2$ filters. Naturally the resulting filter is guaranteed to be stable.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1464-1468
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• 1997

We propose a decentralized state estimation method in the multisensor state estimation problem. The proposed method bounds teh maximum energy gain from uknown external disturbances to the estimation errors in the suboptimal case. And we formulate aternative H/sip .inf./ filter gain equatiions with teh idea that the suboptimal H.$^{\infty}$ filter is the special form of Kalman filter filter whose state equations are defined in indefinite inner product spaces. Using alternative filter gain equations we design the decentralized $H^{\infty}$ state estimator which is composed of local filters and central fusion filter that are suboptimal in the $H^{\infty}$ sense. In addition, the proposed update equations between global and local data can reduce unnecessary calculation burden efficently.y.

• 대한전기학회논문지
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• 제45권2호
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• pp.256-264
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• 1996

In this paper, the decentralized suboptimal H$_{2}$ filtering problem is considered. An additional term is added to the centralized optimal H$_{2}$ filter so that the whole filter is decentralized. We derive a necessary and sufficient condition for existence of proposed decentralized filters By employing the solution procedure for the exact model matching problem, we obtain a set of decentralized H$_{2}$ filters, and choose a suboptimal filter from this set of decentralized H$_{2}$ filters.

• Journal of Positioning, Navigation, and Timing
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• 제9권2호
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• pp.99-115
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• 2020

Since GNSS is easily affected by jamming and/or spoofing, alternative navigation systems can be operated as backup system to prepare for outage of GNSS. Alternative navigation systems are being researched over the world, and a multi-radio integrated navigation system using alternative navigation systems such as KNSS, eLoran, Loran-C, DME, VOR has been researched in Korea. Least Square or Kalman filter can be used to estimate navigation parameters in the navigation system. A large number of measurements of the Kalman filter may lead to heavy computational load. The decentralized Kalman filter and the federated Kalman filter were proposed to handle this problem. In this paper, the decentralized Kalman filter and the federated Kalman filter are designed for the multi-radio integrated navigation system and the performance evaluation result are presented. The decentralized Kalman filter and the federated Kalman filter consists of local filters and a master filter. The navigation parameter is estimated by local filters and master filter compensates navigation parameter from the local filters. Characteristics of three Kalman filters for a linear system and nonlinear system are investigated, and the performance evaluation results of the three Kalman filters for multi-radio integrated navigation system are compared.

• 센서학회지
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• 제25권6호
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• pp.418-422
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• 2016

A filtering algorithm based on the decentralized moving average Kalman filter with uncertainties is proposed in this paper. The proposed filtering algorithm presented combines the Kalman filter with the moving average strategy. A decentralized fusion algorithm with the weighted sum structure is applied to the local moving average Kalman filters (LMAKFs) of different window lengths. The proposed algorithm has a parallel structure and allows parallel processing of observations. Hence, it is more reliable than the centralized algorithm when some sensors become faulty. Moreover, the choice of the moving average strategy makes the proposed algorithm robust against linear discrete-time dynamic model uncertainties. The derivation of the error cross-covariances between the LMAKFs is the key idea of studied. The application of the proposed decentralized fusion filter to dynamic systems within a multisensor environment demonstrates its high accuracy and computational efficiency.

• 한국항공우주학회지
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• 제39권11호
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• pp.1013-1020
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• 2011

본 논문에서는 이동 목표물을 추적하기 위하여, 다수 무인항공기의 측정치를 이용한 목표물의 위치와 속도 추정기법을 제안하였다. 항공기와 목표물 사이의 상대거리와 시선각 정보를 이용하여 목표물의 위치, 속도 성분을 추정하는 확장필터를 구성하였다. 다수의 항공기 간 정보교환과 계산이 용이하도록 공분산 역행렬 형태의 정보필터를 설계하였다. 개별 확장필터, 부분 분산형 확장필터, 분산형 확장필터를 이용한 수치 시뮬레이션을 수행하여, 제안된 분산형 확장필터의 이동 목표물 추정 및 추적 성능을 검증하였다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.598-601
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• 1995

This paper proposes a federated Kalman filter approach which utilizes information from multiple sensors and variable estimation model. Compared with the decentralized Kalman filter, the algorithm proposed in this paper demonstrates much better tracking performance in both maneuvering and constant velocity movement of the target.

• Smart Structures and Systems
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• 제4권6호
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• pp.779-794
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• 2008

Observer-based fault detection and isolation (FDI) filter design method is a model-based method. By carefully choosing the observer gain, the residual outputs can be projected onto different independent subspaces. Each subspace corresponds to the monitored structural element so that the projected residual will be nonzero when the associated structural element is damaged and zero when there is no damage. The key point of detection filter design is how to find an appropriate observer gain. This problem can be interpreted in a geometric framework and is found to be equivalent to the problem of finding a decentralized static output feedback gain. But, it is still a challenging task to find the decentralized controller by either analytical or numerical methods because its solution set is, generally, non-convex. In this paper, the concept of detection filter and iterative LMI technique for decentralized controller design are combined to develop an algorithm to compute the observer gain. It can be used to monitor structural element state: healthy or damaged. The simulation results show that the developed method can successfully identify structural damages.

• 대한전기학회논문지:전력기술부문A
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• 제48권6호
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• pp.735-746
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• 1999

We design the robust and inherently fault tolerant decetralized$$H^infty$$ filter for the multisensor state estimation problem when there are insufficient priori informations on the statistical properties of external disturbances. For developing the proposed algorithm, an alternative form of suboptimal$$H^infty$$ filter equations are formulated by applying an alternative form of Kalman filter equations to the indefinite inner product space state model of suboptimal$$H^infty$$ filtering problems. The decentralized$$H^infty$$ filter that consists of local and central fusion filters can be designed effciently using the proposed alternative$$H^infty$$ filiter gain equations. The proposed decentralized$$H^infty$$ filter is robust against un-known external disturbances since it bounds the maximum energy gain from the external disturbances to the estimation errors under the prescribed level$$r^2$$ in both local and central fusion filters and is also fault tolerant due to its inherent redundancy. In addition, the central fusion equations between the global and local data can reduce the unnecessary calculation burden effectively. Computer simulations are made to ceritfy the robustness and fault tolerance of the proposed algorithm.