Citation
Nikolay A. Atanasov, Roberto Tron, Victor Preciado, George Pappas. "Joint Estimation and Localization in Sensor Networks". 2014 IEEE Conference on Decision and Control (CDC), December, 2014.

Abstract
This paper addresses the problem of collaborative tracking of dynamic targets in wireless sensor networks. A novel distributed linear estimator, which is a version of a distributed Kalman filter, is derived. We prove that the filter is mean square consistent in the case of static target estimation. When large sensor networks are deployed, it is common that the sensors do not have good knowledge of their locations, which affects the target estimation procedure. Unlike most existing approaches for target tracking, we investigate the performance of our filter when the sensor poses need to be estimated by an auxiliary localization procedure. The sensors are localized via a distributed Jacobi algorithm from noisy relative measurements. We prove strong convergence guarantees for the localization method and in turn for the joint localization and target estimation approach. The performance of our algorithms is demonstrated in simulation on environmental monitoring and target tracking tasks.

Electronic downloads

• https://www.src.org/library/publication/p069893/. (Link to paper on SRC website.)
• http://dx.doi.org/10.1109/CDC.2014.7040469

• HTML

  Nikolay A. Atanasov, Roberto Tron, Victor Preciado, George
  Pappas. <a
  href="http://www.terraswarm.org/pubs/301.html"
  >Joint Estimation and Localization in Sensor
  Networks</a>, 2014 IEEE Conference on Decision and
  Control (CDC), December, 2014.

• Plain text

  Nikolay A. Atanasov, Roberto Tron, Victor Preciado, George
  Pappas. "Joint Estimation and Localization in Sensor
  Networks". 2014 IEEE Conference on Decision and Control
  (CDC), December, 2014.

• BibTeX

  @inproceedings{AtanasovTronPreciadoPappas14_JointEstimationLocalizationInSensorNetworks,
      author = {Nikolay A. Atanasov and Roberto Tron and Victor
                Preciado and George Pappas},
      title = {Joint Estimation and Localization in Sensor
                Networks},
      booktitle = {2014 IEEE Conference on Decision and Control (CDC)},
      month = {December},
      year = {2014},
      abstract = {This paper addresses the problem of collaborative
                tracking of dynamic targets in wireless sensor
                networks. A novel distributed linear estimator,
                which is a version of a distributed Kalman filter,
                is derived. We prove that the filter is mean
                square consistent in the case of static target
                estimation. When large sensor networks are
                deployed, it is common that the sensors do not
                have good knowledge of their locations, which
                affects the target estimation procedure. Unlike
                most existing approaches for target tracking, we
                investigate the performance of our filter when the
                sensor poses need to be estimated by an auxiliary
                localization procedure. The sensors are localized
                via a distributed Jacobi algorithm from noisy
                relative measurements. We prove strong convergence
                guarantees for the localization method and in turn
                for the joint localization and target estimation
                approach. The performance of our algorithms is
                demonstrated in simulation on environmental
                monitoring and target tracking tasks.},
      URL = {http://terraswarm.org/pubs/301.html}
  }
  

Posted by Nikolay A. Atanasov on 14 Apr 2014.
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