### Abstract

This work is focused on computing-via a deterministic optimization with linear matrix inequality (LMI) constraints, rather than a pseudorandom simulation-the performance of predictive quantization schemes under various scenarios for loss and degradation of encoded prediction error samples. The ability to make this computation then allows for the optimization of prediction filters with the aim of minimizing overall mean squared error (including the effects of losses) rather than to minimize the variance of the unquantized prediction error sequence. The main tools are recent characterizations of asymptotic state estimation error covariance and output estimation error variance in terms of LMIs. These characterizations apply to discrete-time jump linear systems in which the discrete portion of the system state is a Markov chain. Translating to the signal processing terminology, this means that the signal model is "piecewise ARMA," as is standard in many forms of speech processing.

Original language | English (US) |
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Title of host publication | IEEE International Symposium on Information Theory - Proceedings |

Pages | 427 |

Number of pages | 1 |

State | Published - 2004 |

Event | Proceedings - 2004 IEEE International Symposium on Information Theory - Chicago, IL, United States Duration: Jun 27 2004 → Jul 2 2004 |

### Other

Other | Proceedings - 2004 IEEE International Symposium on Information Theory |
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Country | United States |

City | Chicago, IL |

Period | 6/27/04 → 7/2/04 |

### Fingerprint

### ASJC Scopus subject areas

- Electrical and Electronic Engineering

### Cite this

*IEEE International Symposium on Information Theory - Proceedings*(pp. 427)

**Robust predictive quantization : A new analysis and optimization framework.** / Fletcher, Alyson K.; Rangan, Sundeep; Goyal, Vivek K.; Ramchandran, Kannan.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*IEEE International Symposium on Information Theory - Proceedings.*pp. 427, Proceedings - 2004 IEEE International Symposium on Information Theory, Chicago, IL, United States, 6/27/04.

}

TY - GEN

T1 - Robust predictive quantization

T2 - A new analysis and optimization framework

AU - Fletcher, Alyson K.

AU - Rangan, Sundeep

AU - Goyal, Vivek K.

AU - Ramchandran, Kannan

PY - 2004

Y1 - 2004

N2 - This work is focused on computing-via a deterministic optimization with linear matrix inequality (LMI) constraints, rather than a pseudorandom simulation-the performance of predictive quantization schemes under various scenarios for loss and degradation of encoded prediction error samples. The ability to make this computation then allows for the optimization of prediction filters with the aim of minimizing overall mean squared error (including the effects of losses) rather than to minimize the variance of the unquantized prediction error sequence. The main tools are recent characterizations of asymptotic state estimation error covariance and output estimation error variance in terms of LMIs. These characterizations apply to discrete-time jump linear systems in which the discrete portion of the system state is a Markov chain. Translating to the signal processing terminology, this means that the signal model is "piecewise ARMA," as is standard in many forms of speech processing.

AB - This work is focused on computing-via a deterministic optimization with linear matrix inequality (LMI) constraints, rather than a pseudorandom simulation-the performance of predictive quantization schemes under various scenarios for loss and degradation of encoded prediction error samples. The ability to make this computation then allows for the optimization of prediction filters with the aim of minimizing overall mean squared error (including the effects of losses) rather than to minimize the variance of the unquantized prediction error sequence. The main tools are recent characterizations of asymptotic state estimation error covariance and output estimation error variance in terms of LMIs. These characterizations apply to discrete-time jump linear systems in which the discrete portion of the system state is a Markov chain. Translating to the signal processing terminology, this means that the signal model is "piecewise ARMA," as is standard in many forms of speech processing.

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UR - http://www.scopus.com/inward/citedby.url?scp=5044248654&partnerID=8YFLogxK

M3 - Conference contribution

SP - 427

BT - IEEE International Symposium on Information Theory - Proceedings

ER -