On a unified framework for linear nuisance parameters

Yongchang Hu, Geert Leus

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
18 Downloads (Pure)

Abstract

Estimation problems in the presence of deterministic linear nuisance parameters arise in a variety of fields. To cope with those, three common methods are widely considered: (1) jointly estimating the parameters of interest and the nuisance parameters; (2) projecting out the nuisance parameters; (3) selecting a reference and then taking differences between the reference and the observations, which we will refer to as “differential signal processing.” A lot of literature has been devoted to these methods, yet all follow separate paths. Based on a unified framework, we analytically explore the relations between these three methods, where we particularly focus on the third one and introduce a general differential approach to cope with multiple distinct nuisance parameters. After a proper whitening procedure, the corresponding best linear unbiased estimators (BLUEs) are shown to be all equivalent to each other. Accordingly, we unveil some surprising facts, which are in contrast to what is commonly considered in literature, e.g., the reference choice is actually not important for the differencing process. Since this paper formulates the problem in a general manner, one may specialize our conclusions to any particular application. Some localization examples are also presented in this paper to verify our conclusions.

Original languageEnglish
Article number4
Pages (from-to)1-14
Number of pages14
JournalEurasip Journal on Advances in Signal Processing
Volume2017
Issue number1
DOIs
Publication statusPublished - 2017

Keywords

  • Best linear unbiased estimator (BLUE)
  • Differential signal processing
  • Joint estimation
  • Linear nuisance parameters
  • Orthogonal subspace projection (OSP)
  • Source localization

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