Automatica (Journal of IFAC)
Asymptotic analysis of multiple description quantizers
IEEE Transactions on Information Theory
Achievable rates for multiple descriptions
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
The validity of the additive noise model for uniform scalar quantizers
IEEE Transactions on Information Theory
Design of multiple description scalar quantizers
IEEE Transactions on Information Theory
A robust codec for transmission of very low bit-rate video over channels with bursty errors
IEEE Transactions on Circuits and Systems for Video Technology
Brief paper: Observer design for wired linear networked control systems using matrix inequalities
Automatica (Journal of IFAC)
Proceedings of the 4th Annual International Conference on Wireless Internet
H∞ filtering of networked discrete-time systems with random packet losses
Information Sciences: an International Journal
Robustification and optimization of a Kalman filter with measurement loss using linear precoding
ACC'09 Proceedings of the 2009 conference on American Control Conference
Remote fault detection system design with online channel reliability information
International Journal of Systems Science - Fault Diagnosis and Fault Tolerant Control
On Kalman filtering over fading wireless channels with controlled transmission powers
Automatica (Journal of IFAC)
Hi-index | 22.15 |
For state estimation over a communication network, efficiency and reliability of the network are critical issues. The presence of packet dropping and communication delay can greatly impair our ability to measure and predict the state of a dynamic process. In this paper, multiple description (MD) codes, a type of network source codes, are used to compensate for this effect on Kalman filtering. We consider two packet dropping models: in one model, packet dropping occurs according to an independent and identically distributed (i.i.d.) Bernoulli random process and in the other model, packet dropping is bursty and occurs according to a Markov chain. We show that MD codes greatly improve the statistical stability and performance of Kalman filter over a large set of packet loss scenarios in both cases. Our conclusions are verified by simulation results.