State Estimation With Coarsely Quantized, High-Data-Rate Measurements

Renwick E. Curry; Pitu Mirchandani; Charles F. Price

doi:10.1109/TAES.1975.308127

State Estimation With Coarsely Quantized, High-Data-Rate Measurements

Renwick E. Curry, Pitu Mirchandani, Charles F. Price

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

This paper treats the problem of estimating a signal corrupted by noise that is sampled and quantized at a high data rate. Local and global processors are proposed to achieve data compression that permits near optimal extraction of information. Two techniques-maximum likelihood and minimum transform chi square, which are in the class of best asymptotically normal estimators are investigated for the local processor. Simulation results are presented to demonstrate the feasibility of the approach.

Original language	English (US)
Pages (from-to)	613-621
Number of pages	9
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	AES-11
Issue number	4
DOIs	https://doi.org/10.1109/TAES.1975.308127
State	Published - Jul 1975
Externally published	Yes

ASJC Scopus subject areas

Aerospace Engineering
Electrical and Electronic Engineering

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10.1109/TAES.1975.308127

Cite this

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title = "State Estimation With Coarsely Quantized, High-Data-Rate Measurements",

abstract = "This paper treats the problem of estimating a signal corrupted by noise that is sampled and quantized at a high data rate. Local and global processors are proposed to achieve data compression that permits near optimal extraction of information. Two techniques-maximum likelihood and minimum transform chi square, which are in the class of best asymptotically normal estimators are investigated for the local processor. Simulation results are presented to demonstrate the feasibility of the approach.",

author = "Curry, {Renwick E.} and Pitu Mirchandani and Price, {Charles F.}",

year = "1975",

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AB - This paper treats the problem of estimating a signal corrupted by noise that is sampled and quantized at a high data rate. Local and global processors are proposed to achieve data compression that permits near optimal extraction of information. Two techniques-maximum likelihood and minimum transform chi square, which are in the class of best asymptotically normal estimators are investigated for the local processor. Simulation results are presented to demonstrate the feasibility of the approach.

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State Estimation With Coarsely Quantized, High-Data-Rate Measurements

Abstract

ASJC Scopus subject areas

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