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.
- Publication:
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IEEE Transactions on Aerospace Electronic Systems
- Pub Date:
- July 1975
- DOI:
- 10.1109/TAES.1975.308127
- Bibcode:
- 1975ITAES..11..613C
- Keywords:
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- Data Compression;
- Maximum Likelihood Estimates;
- Signal Processing;
- Signal To Noise Ratios;
- State Estimation;
- Digital Filters;
- Information Theory;
- Optimization;
- Communications and Radar