A New Outlier-Robust Student's t Based Gaussian Approximate Filter for Cooperative Localization

• Published in: IEEE/ASME transactions on mechatronics Vol. 22; no. 5; pp. 2380 - 2386
• Main Authors: Yulong Huang, Yonggang Zhang, Bo Xu, Zhemin Wu, Chambers, Jonathon
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustic measurements; Autonomous underwater vehicles; Autonomous underwater vehicles (AUVs); Bayes methods; Bayesian analysis; cooperative localization; Covariance matrices; Degrees of freedom; Gaussian process; heavy-tailed noise; Localization; Matrix algebra; Matrix methods; Noise measurement; nonlinear filtering; outlier; Parameter estimation; Robustness; State space models; State-space methods; Student's <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> t</tex-math> </inline-formula> </named-content> distribution; variational Bayesian; Velocity measurement
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2017.2744651

In this paper, a new outlier-robust Student's t based Gaussian approximate filter is proposed to address the heavy-tailed process and measurement noises induced by the outlier measurements of velocity and range in cooperative localization of autonomous underwater vehicles (AUVs). The state vector, scale matrices, and degrees of freedom (DOF) parameters are jointly estimated based on the variational Bayesian approach by using the constructed Student's t based hierarchical Gaussian state-space model. The performances of the proposed filter and existing filters are tested in the cooperative localization of an AUV through a lake trial. Experimental results illustrate that the proposed filter has better localization accuracy and robustness than existing state-of-the-art outlier-robust filters.