State estimation for asynchronous multirate multisensor nonlinear dynamic systems with missing measurements

Yan, Liping; Xiao, Bo; Xia, Yuanqing; Fu, Mengyin

doi:10.1002/acs.2266

Cited by 20 publications

(10 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, we can easily obtain (23) where Ex (i) (t − 1|t − 1)w(t − 1) = 0 has been used. This proof is completed.…”

Section: Local Optimal Estimation Algorithmmentioning

confidence: 99%

Distributed fusion estimator for multi‐sensor asynchronous sampling systems with missing measurements

Sun

2016

IET signal process.

View full text Add to dashboard Cite

The fusion estimation problem for a class of multi-sensor asynchronous sampling linear stochastic systems with missing measurements is considered, where the state is updated uniformly and each sensor non-uniformly samples one measurement at most within a state update period. Based on the sampled measurement data of each sensor, the optimal local state estimators are designed at the state and measurement points by using the projection theory. The crosscovariance matrices between estimation errors of any two local estimators are derived. Based on the obtained local estimators and cross-covariance matrices, the distributed optimal fusion estimator is given by using matrix-weighted fusion estimation algorithm in the linear minimum variance sense. A simulation example verifies the effectiveness of the algorithms.

show abstract

“…, we can easily obtain (23) where Ex (i) (t − 1|t − 1)w(t − 1) = 0 has been used. This proof is completed.…”

Section: Local Optimal Estimation Algorithmmentioning

confidence: 99%

Distributed fusion estimator for multi‐sensor asynchronous sampling systems with missing measurements

Sun

2016

IET signal process.

View full text Add to dashboard Cite

show abstract

“…Different from the single-rate estimator design with packet dropouts which are treated as stochastic parameters, an unknown input observer was proposed, where packet dropouts are represented as zero-mean white input noises of the linear time-variant estimation error system. Yan et al [94] considered the state estimation for a kind of nonlinear multirate multisensor asynchronous sampling dynamic system. N sensors observed a single target independently at multiple sampling rates, and the dynamic system was formulated at the highest sampling rate.…”

Section: Data Fusion In Ncssmentioning

confidence: 99%

“…The multiscale system theory and the modified sigma point KF were used to design the fusion method. Measurements randomly missing with Bernoulli distribution could also be allowed in [94]. There are also some significant results which are concerned with the communication constraints and energy consumption.…”

Section: Data Fusion In Ncssmentioning

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

“…However, the above literatures do not consider unreliable measurements. The optimal state estimation with unreliable measurements and multirate sensors is researched in [4,30] without consideration of the noise correlation. For multirate dynamic systems, an optimal sequential fusion estimation algorithm with correlated noise and unreliable measurements is presented in [31], where the correlation of the measurement noise and the system noise at the same time step is not considered, and the distributed fusion of the observations is not considered.…”

Section: Introductionmentioning

confidence: 99%

Optimal distributed Kalman filtering fusion for multirate multisensor dynamic systems with correlated noise and unreliable measurements

Yan

et al. 2018

IET signal process.

Self Cite

View full text Add to dashboard Cite

An optimal distributed fusion estimation problem is concerned in this study for a kind of linear dynamic multirate sensors systems with correlated noise and stochastic unreliable measurements. The system is formulated at the finest scale with multiple sensors at different scales observing a common target independently with different sampling rates. The noise between different sensors is relevant, moreover, is also correlated with the system noise. The authors derive the local state estimation algorithms under the circumstance of total reliable measurements and stochastic unreliable measurements occur occasions, and the optimal distributed Kalman filter fusion algorithm, respectively. The authors provide a simulation example to illustrate the effectiveness and feasibility of the proposed algorithm.

show abstract

State estimation for asynchronous multirate multisensor nonlinear dynamic systems with missing measurements

Cited by 20 publications

References 24 publications

Distributed fusion estimator for multi‐sensor asynchronous sampling systems with missing measurements

Distributed fusion estimator for multi‐sensor asynchronous sampling systems with missing measurements

Recent progress in networked control systems — A survey

Optimal distributed Kalman filtering fusion for multirate multisensor dynamic systems with correlated noise and unreliable measurements

Contact Info

Product

Resources

About