Variational Bayesian Cubature RTS Smoothing for Transfer Alignment of DPOS

Bo Wang*, Wen Ye, Yanhong Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

Multi-task remote sensing sensors have become attractive development directions of aerial remote sensing system, which rely on distributed position and orientation system (DPOS) to provide multi-node motion parameters to achieve superior performance. DPOS depends on transfer alignment from its master system to slave inertial measurement units to obtain multi-node motion information. However, for DPOS, there are many factors like carrier maneuver mode and external disturbance which will result in time-varying measurement noise and further degrade transfer alignment performance of DPOS obviously. In this work, a transfer alignment method based on variational Bayesian cubature RTS smoothing is developed to improve the accuracy of DPOS, which is implemented by combining the cubature RTS smoothing algorithm and variational Bayesian estimation method to deal with the time-varying measurement noise. A semi-physical simulation based on real flight experiment has been conducted, the results show that the motion parameter accuracy has achieved noticeable enhancement than the existing cubature RTS smoothing algorithm.

Original languageEnglish
Article number8928525
Pages (from-to)3270-3279
Number of pages10
JournalIEEE Sensors Journal
Volume20
Issue number6
DOIs
Publication statusPublished - 15 Mar 2020
Externally publishedYes

Keywords

  • Variational Bayesian estimation
  • cubature RTS smoothing
  • distributed position and orientation system
  • transfer alignment

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Wang, B., Ye, W., & Liu, Y. (2020). Variational Bayesian Cubature RTS Smoothing for Transfer Alignment of DPOS. IEEE Sensors Journal, 20(6), 3270-3279. Article 8928525. https://doi.org/10.1109/JSEN.2019.2958335