Localization Using Blind RSS Measurements

Yongchang Hu, Jiani Liu, Bingbing Zhang

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)

Abstract

Localization using received signal strength (RSS) measurements becomes popular due to the simplicity of practical implementation. Traditional RSS measurements are obtained after successful demodulation such that the impact of the background noise (BGN) is ignored. However, critical information for demodulation might be expensive or difficult to obtain in hostile or harsh environments. In this case, the RSS measurements need to be blindly collected without demodulation and hence characterized by a recent model with the BGN power (already validated by real-life data). This kind of measurement is referred to as 'blind RSS measurement'. In this letter, we introduce four models for the localization using the blind RSS measurements, respectively considering the BGN power and the transmit power to be known or unknown. A general semi-definite programming solution that applies to all these models is proposed. The corresponding Cramér-Rao lower bounds are presented, indicating a significant impact of the BGN power on the estimation accuracy. Numerical results show the proposed method yields a good and reliable performance with different models.

Original languageEnglish
Article number8493351
Pages (from-to)464-467
Number of pages4
JournalIEEE Wireless Communications Letters
Volume8
Issue number2
DOIs
Publication statusPublished - 2019

Keywords

  • background noise
  • Cramér-Rao lower bound
  • Localization
  • received signal strength
  • semidefinite programming

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