G2A Localization: Aerial Vehicles Localization Using a Ground Crowdsourced Network

7 pages•Published: December 23, 2019

Hazem Sallouha, Alessandro Chiumento and Sofie Pollin

Abstract

In this paper, we address the ground-to-air (G2A) localization problem using a crowd- sourced network with a mix of synchronized and unsynchronized receivers. First, we use a dynamic model to represent the offset and the skew of the unsynchronized receivers. This model is then used with a Kalman filter (KF) to compensate for the drifts of the unsynchronized receivers’ clocks. Subsequently, the location of the aerial vehicle (AV) is estimated using another KF with the multilateration (MLAT) method and the dynamic model of the AV. We demonstrate the performance advantages of our method through a dataset collected by the OpenSky network. Our results show that the proposed dual KF method decreases the average localization error by orders of magnitude compared with a solo multilateration method. In particular, the proposed method brings the average localization error from tens of kilometers down to hundreds of meters, based on the considered dataset.

Keyphrases: dynamic clock model, kalman filter, localization, multilateration, synchronization, tdoa, unmanned aerial vehicle (uav)

In: Christina Pöpper and Martin Strohmeier (editors). Proceedings of the 7th OpenSky Workshop 2019, vol 67, pages 37-43.

Links:	https://easychair.org/publications/paper/qGHP
	https://doi.org/10.29007/pqt3

BibTeX entry

@inproceedings{OpenSky19:G2A_Localization_Aerial_Vehicles,
  author    = {Hazem Sallouha and Alessandro Chiumento and Sofie Pollin},
  title     = {G2A Localization: Aerial Vehicles Localization Using a Ground Crowdsourced Network},
  booktitle = {Proceedings of the 7th OpenSky Workshop 2019},
  editor    = {Christina Pöpper and Martin Strohmeier},
  series    = {EPiC Series in Computing},
  volume    = {67},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/qGHP},
  doi       = {10.29007/pqt3},
  pages     = {37-43},
  year      = {2019}}

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