Kalman filter-based identification for systems with randomly missing measurements in a network environment

Shi, Yang; Fang, Huazhen

doi:10.1080/00207170903273987

Cited by 230 publications

(89 citation statements)

References 25 publications

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“…By using the LMI method and delay-dependent technique, sufficient conditions are derived for the exponentially mean-square stability of the filteringerror dynamics. Shi and Fang [49] considered the problem of parameter estimation and output estimation for systems in a transmission control protocol (TCP). The input and output missing data is modeled as two separate Bernoulli processes characterised by probabilities of missing data, and a recursive algorithm for parameter estimation by modifying the KF-based algorithm is developed.…”

Section: Filtering For Linear Systemsmentioning

confidence: 99%

Recent progress in networked control systems — A survey

Xia

Gao

Yan

et al. 2015

Int. J. Autom. Comput.

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For the past decades, networked control systems (NCSs), as an interdisciplinary subject, have been one of the main research highlights and many fruitful results from different aspects have been achieved. With these growing research trends, it is significant to consolidate the latest knowledge and information to keep up with the research needs. In this paper, the results of different aspects of NCSs, such as quantization, estimation, fault detection and networked predictive control, are summarized. In addition, with the development of cloud technique, cloud control systems are proposed for the further development of NCSs.

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Section: Filtering For Linear Systemsmentioning

confidence: 99%

Recent progress in networked control systems — A survey

Xia

Gao

Yan

et al. 2015

Int. J. Autom. Comput.

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“…Rarely do the conditions necessary for optimality actually exist, and yet, the filter apparently works well for many applications in spite of this situation. Application of Kalman filter in dynamic prediction for corporate financial state consists of five steps [27,28]:…”

Section: Dynamic Prediction Models Based On Kalman Filtering Algorithmmentioning

confidence: 99%

Application of Kalman Filtering in Dynamic Prediction for Corporate Financial Distress

Zhuang¹

2018

Kalman Filters - Theory for Advanced Applications

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This chapter aims to dynamically improve the method of predicting financial distress based on Kalman filtering. Financial distress prediction (FDP) is an important study area of corporate finance. The widely used discriminant models currently for financial distress prediction have deficiencies in dynamics. Based on the state-space method, we establish two models that are used to describe the dynamic process and discriminant rules of financial distress, respectively, that is, a process model and a discriminant model. These two models collectively are called dynamic prediction models for financial distress. The operation of the dynamic prediction is achieved by Kalman filtering algorithm, and further, a general n-step-ahead prediction algorithm based on Kalman filtering is derived for prospective prediction. We also conduct an empirical study for China's manufacturing industry, and the results have proved the accuracy and advance of predicting financial distress in such case.

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“…Furthermore, when the sensors send their measurements to the processing center via a communication network some additional network-induced phenomena, such as random delays or measurement losses, inevitably arise during this transmission process, which can spoil the fusion estimators performance and motivate the design of fusion estimation algorithms for systems with one (or even several) of the aforementioned uncertainties (see e.g., [12][13][14][15][16][17][18][19][20][21][22][23][24], and references therein). All the above cited papers on signal estimation with random transmission delays assume independent random delays at each sensor and mutually independent delays between the different sensors; in [25] this restriction was weakened and random delays featuring correlation at consecutive sampling times were considered, thus allowing to deal with some common practical situations (e.g., those in which two consecutive observations cannot be delayed).…”

Section: Introductionmentioning

confidence: 99%

Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission

2017

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Abstract:In this paper, the information fusion estimation problem is investigated for a class of multisensor linear systems affected by different kinds of stochastic uncertainties, using both the distributed and the centralized fusion methodologies. It is assumed that the measured outputs are perturbed by one-step autocorrelated and cross-correlated additive noises, and also stochastic uncertainties caused by multiplicative noises and randomly missing measurements in the sensor outputs are considered. At each sampling time, every sensor output is sent to a local processor and, due to some kind of transmission failures, one-step correlated random delays may occur. Using only covariance information, without requiring the evolution model of the signal process, a local least-squares (LS) filter based on the measurements received from each sensor is designed by an innovation approach. All these local filters are then fused to generate an optimal distributed fusion filter by a matrix-weighted linear combination, using the LS optimality criterion. Moreover, a recursive algorithm for the centralized fusion filter is also proposed and the accuracy of the proposed estimators, which is measured by the estimation error covariances, is analyzed by a simulation example.

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Kalman filter-based identification for systems with randomly missing measurements in a network environment

Cited by 230 publications

References 25 publications

Recent progress in networked control systems — A survey

Recent progress in networked control systems — A survey

Application of Kalman Filtering in Dynamic Prediction for Corporate Financial Distress

Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission

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