Name
Abstract | ||
|---|---|---|
Consider a communication system in which a filtered and quantized signal is sent over a channel with erasures and (potentially) additive noise. Linear MMSE estimation is achieved in such a system by Kalman filtering. Allowing any Markov erasure process and any Markov-state jump linear signal generation model, it is shown that the estimation performance at the receiver can be computed as a deterministic optimization with linear matrix inequality (LMI) constraints rather than a pseudorandom Simulation. Furthermore, in contrast to the case without erasures, the filtering in the transmitter should not necessarily be MMSE prediction (whitening): a procedure is given to find a locally optimal prefilter. The main tools are recent LMI characterizations of asymptotic state estimation error covariance and Output estimation error variance for discrete-time jump linear systems in which the discrete portion of the system state is a Markov chain. As another application of this framework, a novel analysis and optimization of a "streaming" version of multiple description coding based on subsampling is outlined. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ICIP.2004.1421805 | ICIP: 2004 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1- 5 |
Keywords | Field | DocType |
discrete time,kalman filter,markov processes,channel coding,linear matrix inequality,markov chain,communication system,image reconstruction,multiple description coding | Multiple description coding,Markov process,Control theory,Computer science,Markov chain,Filter (signal processing),Kalman filter,Quantization (signal processing),Linear matrix inequality,Covariance | Conference |
ISSN | Citations | PageRank |
1522-4880 | 0 | 0.34 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alyson K. Fletcher | 1 | 552 | 41.10 |
| Sundeep Rangan | 2 | 3101 | 163.90 |
| Vivek K. Goyal | 3 | 2031 | 171.16 |
| Kannan Ramchandran | 4 | 9401 | 1029.57 |