Estimating directional data from network topology for improving tracking performance

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This work proposes a novel approach for tracking a moving target in non-line-of-sight (NLOS) environments based on range estimates extracted from received signal strength (RSS) and time of arrival (TOA) measurements. By exploiting the known architecture of reference points to act as an improper antenna array and the range estimates, angle of arrival (AOA) of the signal emitted by the target is first estimated at each reference point. We then show how to take advantage of these angle estimates to convert the problem into a more convenient, polar space, where a linearization of the measurement models is easily achieved. The derived linear model serves as the main building block on top of which prior knowledge acquired during the movement of the target is incorporated by adapting a Kalman filter (KF). The performance of the proposed approach was assessed through computer simulations, which confirmed its effectiveness in combating the negative effect of NLOS bias and superiority in comparison with its naive counterpart, which does not take prior knowledge into consideration.

Original languageEnglish
Article number30
JournalJournal of Sensor and Actuator Networks
Issue number2
Publication statusPublished - 20 May 2019


  • Angle of arrival (AOA)
  • Kalman filter (KF)
  • Non-line-of-sight (NLOS)
  • Received signal strength (RSS)
  • Target tracking
  • Time of arrival (TOA)


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