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Optimal Estimation of Sensor Biases for Asynchronous Multi-Sensor Registration

Wenqiang Pu,Ya-Feng Liu,Junkun Yan,Hongwei Liu,Z. Luo

Published 2017 in arXiv.org

ABSTRACT

An important step in the asynchronous multi-sensor registration problem is to estimate sensor range and azimuth biases from their noisy asynchronous measurements. The estimation problem is generally very challenging due to highly nonlinear transformation between the global and local coordinate systems as well as measurement asynchrony from different sensors. In this paper, we propose a novel nonlinear least square (LS) formulation for the problem by only assuming that a reference target moves with an unknown constant velocity. We also propose a block coordinate decent (BCD) optimization algorithm, with a judicious initialization, for solving the problem. The proposed BCD algorithm alternately updates the range and azimuth bias estimates by solving linear least square problems and semidefinite programs (SDPs). The proposed algorithm is guaranteed to find the global solution of the problem and the true biases in the noiseless case. Simulation results show that the proposed algorithm significantly outperforms the existing approaches in terms of the root mean square error (RMSE).

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    arXiv.org

  • Publication date

    2017-10-08

  • Fields of study

    Mathematics, Computer Science, Engineering

  • Identifiers
    arXiv 1710.02808
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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