Closed-form performance for location estimation based on quantized data in sensor networks

Yujiao Zheng, Ruixin Niu, Pramod K. Varshney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

For a large and dense sensor network, the impact of sensor density is investigated on the performance of a maximum likelihood (ML) location estimator using quantized sensor data. The ML estimator fuses quantized data transmitted from local sensors to estimate the location of a source. A Gaussian-like isotropic signal decay model is adopted to make the problem tractable. This model is suitable for situations such as passive sensors monitoring a target emitting acoustic signals. The exact Cramér-Rao lower bound (CRLB) on the estimation error is derived. In addition, an approximate closed-form CRLB by using the Law of Large Numbers is obtained. The closed-form results indicate that the Fisher information is a linearly increasing function of the sensor density. Even though the results are derived assuming a large number of sensors, numerical results show that the closed-form CRLB is very close to the exact CRLB for both high and relatively low sensor densities.

Original languageEnglish (US)
Title of host publication13th Conference on Information Fusion, Fusion 2010
StatePublished - Dec 1 2010
Event13th Conference on Information Fusion, Fusion 2010 - Edinburgh, United Kingdom
Duration: Jul 26 2010Jul 29 2010

Publication series

Name13th Conference on Information Fusion, Fusion 2010

Other

Other13th Conference on Information Fusion, Fusion 2010
CountryUnited Kingdom
CityEdinburgh
Period7/26/107/29/10

Keywords

  • Cramér-Rao lower bound
  • Localization
  • Location estimation
  • Quantization
  • Sensor networks

ASJC Scopus subject areas

  • Information Systems

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