A Novel Robust Gaussian Approximate Smoother Based on EM for Cooperative Localization With Sensor Fault and Outliers

In this article, a novel robust Gaussian approximation smoother based on expectation-maximization (EM) algorithm is proposed for cooperative localization (CL) with faulty Doppler velocity log (DVL) and heavy-tailed measurement noise. In our model, the autonomous underwater vehicle (AUV) velocity inf...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 14
Main Authors: Xu, Bo, Guo, Yu, Wang, Lianzhao, Zhang, Jiao
Format: Journal Article
Language:English
Published: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Acoustic measurements
Algorithms
Autonomous underwater vehicles
Cooperative localization (CL)
Covariance matrices
Covariance matrix
Estimation
expectation–maximization (EM) algorithm
Gaussian process
Gaussian smoother
heavy-tailed noise
Localization
Mathematical model
maximum likelihood (ML) estimation
Maximum likelihood estimation
Navigation
Noise
Noise measurement
Outliers (statistics)
Random variables
Robustness
State space models
Student’s t distribution
Underwater acoustics
unknown input
variational Bayesian (VB) approach
ISSN:0018-9456, 1557-9662
Online Access:Get full text
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Summary:In this article, a novel robust Gaussian approximation smoother based on expectation-maximization (EM) algorithm is proposed for cooperative localization (CL) with faulty Doppler velocity log (DVL) and heavy-tailed measurement noise. In our model, the autonomous underwater vehicle (AUV) velocity information that is not available due to DVL failure and the bias in the underwater acoustic modem are considered as unknown inputs. Then, the Student's t distribution is used to model the heavy-tailed measurement noise. An EM algorithm is also developed for the state-space model with heavy-tailed measurement noise. The state, noise covariance matrices, and auxiliary random variables are regarded as hidden variables to obtain the maximum likelihood estimation of unknown inputs. Gaussian smoother where modified process and measurement noise covariance are inferred by variational Bayesian (VB) approach is applied to estimate the state. The experimental results illustrate that given the heavy-tailed noise, the proposed method estimates the unknown input, and the state with a high level of accuracy. The proposed algorithm can be used as a backup algorithm for CL in cases where DVL is not available due to failure.
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content type line 14
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2020.3034977