An Optimal Receding Horizon FIR Filter for Continuous-Time Linear Systems

Kim, Du Yong; Shin, Vladimir

doi:10.1109/sice.2006.315619

Cited by 13 publications

(20 citation statements)

References 3 publications

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“…To find , we can apply the optimal MAKF to the subsystem (4) [9][10][11][12]. We obtain the following differential equations:…”

Section: Decentralized Moving Average Filtermentioning

confidence: 99%

“…Equations (9)- (11) defining the unknown weights and fusion error covariance depend on the local covariances , which have been determined by (5), and the local cross-covariances given by (12) as given in Theorem 2. …”

Section: Decentralized Moving Average Filtermentioning

confidence: 99%

“…In this case, all fused LMAKFs with the same moving average time interval utilize finite measurements over the most recent time interval [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Decentralized Moving Average Filtering with Uncertainties

Song¹

2016

Journal of Sensor Science and Technology

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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.

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“…To find , we can apply the optimal MAKF to the subsystem (4) [9][10][11][12]. We obtain the following differential equations:…”

Section: Decentralized Moving Average Filtermentioning

confidence: 99%

Section: Decentralized Moving Average Filtermentioning

confidence: 99%

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Decentralized Moving Average Filtering with Uncertainties

Song¹

2016

Journal of Sensor Science and Technology

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“…Then using the receding horizon strategy [8,[26][27][28][29][30][31], standard mixed CD Kalman filter [32] and discrete Kalman filter for systems with time delays [33,34] we propose new local receding horizon filter for dynamic system (12). The proposed 'local receding horizon filter', which we refer to as 'LRHF', includes two parts.…”

Section: Local Receding Horizon Mixed CD Filtermentioning

confidence: 99%

Distributed mixed continuous‐discrete receding horizon filter for multisensory uncertain active suspension systems with measurement delays

Song

Shin

2013

IET Control Theory & Applications

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This study presents a new robust filtering method in modelling an active multisensory suspension system with measurement delays and parameteric uncertainties in a state-space dynamical model. To achieve good performance of the system, a new distributed fusion receding horizon filtering frameworks are constructed to couple the continuous dynamics with the multisensory discrete measurements, and to coordinately deal with the parametric uncertainty and time-delays. The novel filtering algorithm is proposed based on the receding horizon strategy, standard mixed continuous-discrete Kalman filtering and discrete Kalman filtering for systems with time-delays in order to achieve high estimation accuracy and stability under parametric uncertainties. The key theoretical contributions of this study are the derivation of the error cross-covariance equations between the local receding horizon filters in order to compute the optimal matrix fusion weights. The high accuracy and efficiency of the new filter are demonstrated through its implementation and performance and then compared to the existing vehicle active suspension system.

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“…Because of its complicated structure and limited application (not applicable to the feedback control problem), receding horizon strategy is purely applied into Kalman filtering by the same authors which is called the receding horizon Kalman filter (RHKF) [5]. Recently biased property of the receding horizon filter [5] is proved and new stochastic receding horizon filter (ORHF) is proposed in [6][7][8]. In this filter the initial conditions for window is obtained and it gives us the unbiased estimate.…”

Section: Introductionmentioning

confidence: 99%

Window length selection in linear receding horizon filtering

Yoon

Kim

Shin

2008

2008 International Conference on Control, Automation and Systems

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A receding horizon filtering problem for linear systems with norm-bounded time-varying uncertainties is considered. The main goal of this paper is to choose the reasonable window length (WL) which enables us to adjust the modeling uncertainty considering not only computational cost but also the accuracy. The decision of the WL is an important step for receding horizon filter designing. This paper presents a novel algorithm for decision of optimal WL. Two methods are proposed. The first algorithm decides the optimal WL by considering lower bound and upper bound of the uncertainty. Secondly hybrid approach which is the combination of the Kalman filter and the optimal receding horizon filter for suitable situations respectively. The performance of the receding horizon filter with proposed WL is illustrated and compared to other finite memory filters.

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An Optimal Receding Horizon FIR Filter for Continuous-Time Linear Systems

Cited by 13 publications

References 3 publications

Decentralized Moving Average Filtering with Uncertainties

Decentralized Moving Average Filtering with Uncertainties

Distributed mixed continuous‐discrete receding horizon filter for multisensory uncertain active suspension systems with measurement delays

Window length selection in linear receding horizon filtering

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