Lower Bound on the Localization Error in Infinite Networks with Random Sensor Locations

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Abstract

We present novel lower bounds on the mean square error (MSE) of the location estimation of an emitting source via a network where the sensors are deployed randomly. The sensor locations are modeled as a homogenous Poisson point process. In contrast to previous bounds that are a function of the specific locations of all the sensors, we present Cramér-Rao bound (CRB) type bounds on the expected mean square error; that is, we first derive the CRB on the MSE as a function of the sensors' location, and then take expectation with respect to the distribution of the sensors' location. Thus, these bounds are not a function of a particular sensor configuration, but rather of the sensor statistics. Hence, these novel bounds can be evaluated prior to sensor deployment and provide insights into design issues such as the necessary sensor density, the effect of the channel model, the effect of the signal power, and others. The derived bounds are simple to evaluate and provide a good prediction of the actual network performance. Numerical results show that the novel bounds give a good approximation even in other deployment scenarios such as a square or a triangular grid.

Original languageEnglish
Article number8186282
Pages (from-to)1228-1241
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume66
Issue number5
DOIs
StatePublished - 1 Mar 2018

Bibliographical note

Publisher Copyright:
© 1991-2012 IEEE.

Keywords

  • Cramér-Rao bound (CRB)
  • Driction of arrival (DOA)
  • Poisson point process
  • estimation
  • likelihood
  • source localization
  • stochastic geometry

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