• DocumentCode
    567476
  • Title

    Accuracy studies for TDOA and TOA localization

  • Author

    Kaune, Regina

  • Author_Institution
    Dept. SDF, Univ. of Bonn, Wachtberg, Germany
  • fYear
    2012
  • fDate
    9-12 July 2012
  • Firstpage
    408
  • Lastpage
    415
  • Abstract
    In sensor networks, passive localization can be performed by exploiting the received signals of unknown emitters. In this paper, the Time of Arrival (TOA) measurements are investigated. Often, the unknown time of emission is eliminated by calculating the difference between two TOA measurements where Time Difference of Arrival (TDOA) measurements are obtained. In TOA processing, additionally, the unknown time of emission is to be estimated. Therefore, the target state is extended by the unknown time of emission. A comparison is performed investigating the attainable accuracies for localization based on TDOA and TOA measurements given by the Cramér-Rao Lower Bound (CRLB). Using the Maximum Likelihood estimator, some characteristic features of the cost functions are investigated indicating a better performance of the TOA approach. But counterintuitive, Monte Carlo simulations do not support this indication, but show the comparability of TDOA and TOA localization.
  • Keywords
    Monte Carlo methods; maximum likelihood estimation; time-of-arrival estimation; CRLB; Cramér-Rao lower bound; Monte Carlo simulations; TDOA localization; TOA localization; TOA processing; maximum likelihood estimator; passive localization; sensor networks; time difference of arrival; time of arrival measurements; Accuracy; Covariance matrix; Maximum likelihood estimation; Noise measurement; Time measurement; Vectors; CRLB; TDOA; TOA; multilateration; sensor networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Fusion (FUSION), 2012 15th International Conference on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-1-4673-0417-7
  • Electronic_ISBN
    978-0-9824438-4-2
  • Type

    conf

  • Filename
    6289832