Kalman Filtering with Scheduled Measurements

You, Keyou; Xiao, Nan; Xie, Lihua

doi:10.1007/978-1-4471-6615-3_14

Cited by 11 publications

(20 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some modified KFs are proposed to improve the performance of the system. For example, in order to reduce the number of measurements to be transmitted from sensor to estimator, the design of transmission scheduler and estimator for linear discrete-time stochastic systems was considered [38] . However, NCSs are mostly nonlinear systems.…”

Section: Filteringmentioning

confidence: 99%

Recent progress in networked control systems — A survey

Xia

Gao

Yan

et al. 2015

Int. J. Autom. Comput.

View full text Add to dashboard Cite

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.

show abstract

Section: Filteringmentioning

confidence: 99%

Recent progress in networked control systems — A survey

Xia

Gao

Yan

et al. 2015

Int. J. Autom. Comput.

View full text Add to dashboard Cite

show abstract

“…Sui et al [28] studied the optimization of certain sensor scheduling frameworks for the CMSA/CA protocol. Other relevant works on sensor scheduling include [29,30,31,32,33], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Stability of Kalman filtering with a random measurement equation: Application to sensor scheduling with intermittent observations

et al. 2019

View full text Add to dashboard Cite

Studying the stability of the Kalman filter whose measurements are randomly lost has been an active research topic for over a decade. In this paper we extend the existing results to a far more general setting in which the measurement equation, i.e., the measurement matrix and the measurement error covariance, are random. Our result also generalizes existing ones in the sense that it does not require the system matrix to be diagonalizable. For this general setting, we state a necessary and a sufficient condition for stability, and address their numerical computation. An important application of our generalization is a networking setting with multiple sensors which transmit their measurements to the estimator using a sensor scheduling protocol over a lossy network. We demonstrate how our result is used for assessing the stability of a Kalman filter in this multi-sensor setting.

show abstract

“…The previous work (Sijs and Lazar, 2011;Wu et al, 2013;You and Xie, 2013) gave some approximating algorithms for event-based estimator under the assumption that the predicted estimate or the innovation is Gaussian. Sijs and Lazar (2011) considered the state estimation under general event based sampling, and provided a stable approximation algorithm by using Gaussian sums.…”

Section: Introductionmentioning

confidence: 99%

“…Sijs and Lazar (2011) considered the state estimation under general event based sampling, and provided a stable approximation algorithm by using Gaussian sums. Wu et al (2013), and You and Xie (2013) devised scheduling schemes that the output is communicated based on measurement innovation, and provided recursive algorithms for the discretetime state estimation with the assumption of Gaussian prediction. However, the above Gaussian assumption is not guaranteed to be true.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Periodic and Event-Based Sampling for Linear State Estimation

Wang

2014

IFAC Proceedings Volumes

View full text Add to dashboard Cite

Kalman Filtering with Scheduled Measurements

Cited by 11 publications

References 12 publications

Recent progress in networked control systems — A survey

Recent progress in networked control systems — A survey

Stability of Kalman filtering with a random measurement equation: Application to sensor scheduling with intermittent observations

Comparison of Periodic and Event-Based Sampling for Linear State Estimation

Contact Info

Product

Resources

About