Least-squares state estimation in time-delay systems with colored observation noise: An innovations approach

Mishra, Jagabandhu; Rajamani, V. S.

doi:10.1109/tac.1975.1100858

Cited by 27 publications

(11 citation statements)

References 7 publications

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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%

“…Using the discrete Kalman filter equations with time delays [33,34] on the subinterval [t i +m , t i +m+1 ] and calculated values (23) we can obtain recursive measurement update equations for local receding horizon estimatê…”

Section: Measurement Update Partmentioning

confidence: 99%

“…Based on aforementioned literature, and to the best of the authors' knowledge, there are no existing results for the distributed fusion mixed CD receding horizon filtering for multisensory uncertain active suspension systems with measurement delays. Motivated by the above problems, we focus on estimating the state of a mixed CD stochastic active suspension system with time-delays, using a receding horizon strategy [1,8,[26][27][28][29], standard mixed CD Kalman filter [32] and discrete Kalman filter for systems with time-delays [33,34] in order to achieve high estimation accuracy and stability under parametric uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Local Receding Horizon Mixed CD Filtermentioning

confidence: 99%

Section: Measurement Update Partmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Before presenting the DFF, it is needed to explain how local estimates ( ) y k , where the index " i " is fixed. Then, the local estimate can be represented by the following filtering equations [4,12]:…”

Section: Problem Statement For Linear Systemsmentioning

confidence: 99%

Distributed Fusion Filter on Images with Time Delays

Lee

Kwon

Shin

2011

2011 Eighth International Conference Computer Graphics, Imaging and Visualization

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This paper focuses on a distributed image fusion filtering algorithm and fusion formulas for time delayed multiple pixels received from multiple sensors (cameras). Since local cross-covariances between images are important values to implement fusion formulas, we present exact formulas for cross-covariances which are a vital factor for calculating matrix weights in image processing. Subsequent analysis of the proposed fusion algorithm is presented through a typical example demonstrating the effectiveness of the proposed fusion algorithm.

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“…The general approaches to design a filter for these systems include the augmented optimal Kalman filter by an augmented state space representation, which brings a high implementation cost, and the optimal filter by directly applying the projection theory [1,2]. When multiple sensors measure the states of the same stochastic system, generally we have two different types of methods to process the measured sensor data.…”

Section: Introductionmentioning

confidence: 99%

Scalar-weighted fusion estimators for systems with multiple sensors and multiple delayed measurements

Sun

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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This paper is concerned with the distributed fusion estimation problem for discrete-time stochastic linear system with multiple sensors having multiple delayed measurements and correlated noise. Distributed weighted fusion optimal estimators are given based on local optimal estimators from single sensor and the optimal scalar-weighted fusion algorithm in the linear minimum variance sense. Compared with the augmented optimal estimators, the distributed fusion estimators with scalar weights are more reliable and have the reduced computation cost since they have the parallel structure. The estimation error cross-covariance matrices between any two-sensor subsystems are derived. Applying to a tracking system with three sensors shows the effectiveness. I. INTRODUCTIONtate estimation for time-delay systems has been widely studied due to a lot of applications in signal processing, communication and control systems. The general approaches to design a filter for these systems include the augmented optimal Kalman filter by an augmented state space representation, which brings a high implementation cost, and the optimal filter by directly applying the projection theory [1,2]. When multiple sensors measure the states of the same stochastic system, generally we have two different types of methods to process the measured sensor data. The first method is the centralized filter [3] where all measured sensor data are communicated to a central site for processing. Its advantage is to involve minimal information loss. However, it can result in severe computational overhead. The second method is the distributed filter. Its advantage is that the parallel structures would lead to increase the input data rates and make fault detection and isolation easy. Various distributed filters have been reported for systems without time delays [4][5][6][7] including the federated square-root filter [4], the maximum likelihood fusion filter with assumption of normal distribution [5], the unified fusion rules in weighted least square and best linear unbiased estimate sense [6] and the fusion filter weighted by matrices in the linear minimum variance sense [7]. The Kalman filter for multiple time-delay systems is given by re-organized innovation approach in [8], which involves the computation of multiple filters with the same dimension as the system in series. However, the approach is difficult to be applied when there are possible Manuscript

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Least-squares state estimation in time-delay systems with colored observation noise: An innovations approach

Cited by 27 publications

References 7 publications

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

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

Distributed Fusion Filter on Images with Time Delays

Scalar-weighted fusion estimators for systems with multiple sensors and multiple delayed measurements

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