On the Cramér-Rao Bound and the Number of Resolvable Sources in the Presence of Nonuniform Noise for Underdetermined DOA Estimation

Yibao Liang, Wei Liu, Qing Shen, Wei Cui, Siliang Wu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

In this paper, the closed-form stochastic Cramér-Rao Bound (CRB) expression for underdetermined direction-of- arrival (DOA) estimation is derived in the presence of nonuniform noise. By examining the information regularity condition and the number-of-equations condition, it is found that the number of resolvable uncorrelated sources exploiting sparse arrays is upper bounded by the number of positive sensor positions in the virtual difference co-array, which is identical to those in the presence of uniform noise. This indicates that the redundant sensor positions in the difference co-array can provide extra degrees of freedoms (DOFs) for identifiability of more unknown parameters associated with the covariance matrix of the noise, without sacrificing the number of resolvable sources.

Original languageEnglish
Title of host publicationICSP 2020 - 2020 IEEE 15th International Conference on Signal Processing Proceedings
EditorsYuan Baozong, Ruan Qiuqi, Zhao Yao, An Gaoyun
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages93-98
Number of pages6
ISBN (Electronic)9781728144795
DOIs
Publication statusPublished - 6 Dec 2020
Event15th IEEE International Conference on Signal Processing, ICSP 2020 - Virtual, Beijing, China
Duration: 6 Dec 20209 Dec 2020

Publication series

NameInternational Conference on Signal Processing Proceedings, ICSP
Volume2020-December

Conference

Conference15th IEEE International Conference on Signal Processing, ICSP 2020
Country/TerritoryChina
CityVirtual, Beijing
Period6/12/209/12/20

Keywords

  • Cramér-Rao bound (CRB)
  • direction-of-arrival (DOA) estimation
  • nonuniform noise
  • sparse array
  • underdetermined

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