.png){kind=icon}
.png){kind=icon}
返回
Generating dithering noise for maximum likelihood estimation from quantized data
.png){kind=icon}
OA
AI
DOI：10.1016/j.automatica.2012.11.028.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The Quantization Theorem I (QT I) implies that the likelihood function can be reconstructed from quantized sensor observations, given that appropriate dithering noise is added before quantization. We present constructive algorithms to generate such dithering noise. The application to maximum likelihood estimation (MLE) is studied in particular. In short, dithering has the same role for amplitude quantization as an anti-alias filter has for sampling, in that it enables perfect reconstruction of the dithered but unquantized signal's likelihood function. Without dithering, the likelihood function suffers from a kind of aliasing expressed as a counterpart to Poisson's summation formula which makes the exact MLE intractable to compute. With dithering, it is demonstrated that standard MLE algorithms can be re-used on a smoothed likelihood function of the original signal, and statistically efficiency is obtained. The implication of dithering to the Cramer-Rao Lower Bound (CRLB) is studied, and illustrative examples are provided. (C) 2012 Elsevier Ltd. All rights reserved.
Keyword:
Maximum likelihood
Estimation
Quantization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。