Estimation with lossy measurements: jump estimators for jump systems

By S. Craig Smith, P. Seiler. IEEE Transactions on Automatic Control, 2003

307 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/ed522f480b877a3b42a9bce8c46a01e49821e972

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zidong Wang
2. Xiaohui Liu
3. Ling Shi
4. Peng Shi
5. Subhrakanti Dey
6. D.W.C. Ho
7. Minyi Huang
8. Menglin Li

In Computer Networks and Communications

1. Qing-Long Han
2. Peng Shi
3. Zidong Wang
4. W. M. Heemels
5. Xinghuo Yu
6. Luca Schenato
7. Huijun Gao
8. Fanbiao Li

In Control and Systems Engineering

1. Qing-Long Han
2. W. M. Heemels
3. Huijun Gao
4. Xinghuo Yu
5. Zidong Wang
6. Peng Shi
7. Andrew R. Teel
8. Fanbiao Li

Citations in the corpus

• Optimal Denial-of-Service Attack Scheduling With Energy Constraint Over Packet-Dropping Networks (2018, methodology): “) Bernoulli process [22]–[24] or a time-homogeneous binary Markov process [25], [26].”
• Networked Control Systems With Communication Constraints: Tradeoffs Between Transmission Intervals, Delays and Performance (2010, background): “Indeed, the effects of quantization are studied in [3], [12], [20], [22], [28], [36], [45], of packet dropouts in [41], [42], of time-varying transmission intervals and delays in [14], [32], and [10], [16], [24], [27], [35], [55], respectively , and of communication constraints in [2], [11], [26],…”
• Foundations of Control and Estimation Over Lossy Networks (2007, unknown): “In [13], Smith et al. considered a suboptimal but computationally efficient estimator that can be applied when the arrival process is modeled as a Markov chain, which is more general than a Bernoulli process.”
• Stability of Kalman filtering with Markovian packet losses (2007, methodology): “Inthe more recent research on network models, (Fletcher, Rangan, & Goyal, 2004; Smith & Seiler, 2003) considered state estimation with lossy measurements resulting from time-varying channel conditions.”
• A survey on Markovian jump systems: Modeling and design (2015, background): “A Markov process is able to represent such effect [109,112].”
• To Zero or to Hold Control Inputs With Lossy Links? (2009, background): “A large number of works in the literature have analyzed estim ation and filter design under lossy communication between the sens ors and the controller [2][3][4][5][6][7][8][9].”
• Robust H/sub /spl infin// filtering for stochastic time-delay systems with missing measurements (2006, methodology): “The filtering problem with missing measurement has been studied in [4] and [23], and the filters guaranteeing expected estimation error covariance have been designed based on jump Riccati equations.”
• A Survey of Recent Results in Networked Control Systems (2007, background): “In the one depicted in Fig. 3(a), every raw sensor measurement yk is sent to the remote estimator via the network, but may not arrive there if there is a packet dropout [32], [60], [ 61 ].”
• Kalman filtering with intermittent observations (2004, unknown): “Section 7 concludes the paper.”
• Stochastic consensus over noisy networks with Markovian and arbitrary switches (2010, unknown): “We have the assertions.”