Optimal linear state estimation over a packet-dropping network using linear temporal coding

Authors:
Lidong He;Dongfang Han;Xiaofan Wang;Ling Shi
Affiliations:
Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China;School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074, China;Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China;Electronic and Computer Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
Venue:
Automatica (Journal of IFAC)
Year:
2013

Citing 7
Cited 0

Estimation from lossy sensor data: jump linear modeling and Kalman filtering

Proceedings of the 3rd international symposium on Information processing in sensor networks
Guest Editors' Introduction: Overview of Sensor Networks

Computer
Brief paper: Optimal linear estimation for systems with multiple packet dropouts

Automatica (Journal of IFAC)
Kalman filtering with faded measurements

Automatica (Journal of IFAC)
Redundant data transmission in control/estimation over wireless networks

ACC'09 Proceedings of the 2009 conference on American Control Conference
Optimal recursive estimation with uncertain observation

IEEE Transactions on Information Theory
Optimal linear estimation for networked systems with communication constraints

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.14

Visualization

Abstract

We consider the problem of linear minimum mean square error estimation for a discrete-time system over a packet-dropping network. In order to improve the estimation performance, different from the standard approach of sending the current measurement data, we choose sending a linear combination of the current measurement and the measurement collected at the previous time, a method called linear temporal coding. We assume the packet arrival sequence is unknown and the noise contained in the packet may come from sensor or communication channel. In an effort to cope with colored noise caused by measurement combination, after comparing with the classic state augmentation approach and measurement differencing approach, we derive a recursive estimation algorithm by means of orthogonal projection principle and innovation sequence approach. Our algorithm consists of two parts: smooth and estimate. For large measurement noise case, numerical example shows the benefit of using linear temporal coding strategy, compared with directly sending the current data. On the contrary, when communication noise plays the dominating role, for scalar system we prove there is no benefit to choose this scheme.