CRLB based optimal noise enhanced parameter estimation using quantized observations

Date
2010-02-22
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Source Title
IEEE Signal Processing Letters
Print ISSN
1070-9908
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Publisher
IEEE
Volume
17
Issue
5
Pages
477 - 480
Language
English
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Abstract

In this letter, optimal additive noise is characterized for parameter estimation based on quantized observations. First, optimal probability distribution of noise that should be added to observations is formulated in terms of a CramerRao lower bound (CRLB) minimization problem. Then, it is proven that optimal additive noise can be represented by a constant signal level, which means that randomization of additive signal levels is not needed for CRLB minimization. In addition, the results are extended to the cases in which there exists prior information about the unknown parameter and the aim is to minimize the Bayesian CRLB (BCRLB). Finally, a numerical example is presented to explain the theoretical results.

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