Robust Huber-Based Iterated Divided Difference Filtering with Application to Cooperative Localization of Autonomous Underwater Vehicles

Gao, Wei; Liu, Yalong; Xu, Bo

doi:10.3390/s141224523

Cited by 26 publications

(16 citation statements)

References 39 publications

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“…To solve the outlier problem, the Huber-based nonlinear Kalman filter (HNKF) has been derived based on the Huber technique and statistical linearized method [14], [15]. However, the influence function of the HNKF doesn't redescend, which may result in limited estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

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

Huang

Zhang

et al. 2017

IEEE/ASME Trans. Mechatron.

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145

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In this paper, a new outlier-robust Student's t based Gaussian approximate filter is proposed to address the heavytailed 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 estimated based on the variational Bayesian approach by using the constructed Student's t based hierarchical Gaussian statespace 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.

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Section: Introductionmentioning

confidence: 99%

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

Huang

Zhang

et al. 2017

IEEE/ASME Trans. Mechatron.

Self Cite

145

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show abstract

“…With the aid of GPS signal, which can be obtained close to surface, the localization error can be alleviated, but it’s hard to realize in deep-water. Another method is to use acoustic baseline in AUVs, whose localization error is bounded [ 6 , 7 , 8 ]. Unfortunately, there is a high cost in equipment and limited working range for using static beacons.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, there is a high cost in equipment and limited working range for using static beacons. In this case, cooperative navigation (CN) based on acoustic range observation has been proposed [ 6 , 9 , 10 , 11 ]. High accuracy inertial sensors are installed in a small amount of AUVs in a CN system, which can assist the other AUVs that equipped with low precision dead-reckoning (DR) by using acoustic modems [ 5 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Both EKF and UKF require the Gaussian assumption of measurement noise. However, there may be outliers of velocity and range in practical cooperative localization of AUVs, which results in a heavy-tailed non-Gaussian measurement noise [ 4 , 6 , 21 ]. Physically misalignment of body framework in Doppler velocity log (DVL) or water lock may induce outlier measurements of velocity [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Maximum Correntropy Based Unscented Particle Filter for Cooperative Navigation with Heavy-Tailed Measurement Noises

Ying

Zhang

Wang

et al. 2018

Sensors

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In this paper, a novel robust particle filter is proposed to address the measurement outliers occurring in the multiple autonomous underwater vehicles (AUVs) based cooperative navigation (CN). As compared with the classic unscented particle filter (UPF) based on Gaussian assumption of measurement noise, the proposed robust particle filter based on the maximum correntropy criterion (MCC) exhibits better robustness against heavy-tailed measurement noises that are often induced by measurement outliers in CN systems. Furthermore, the proposed robust particle filter is computationally much more efficient than existing robust UPF due to the use of a Kullback-Leibler distance-resampling to adjust the number of particles online. Experimental results based on actual lake trial show that the proposed maximum correntropy based unscented particle filter (MCUPF) has better estimation accuracy than existing state-of-the-art robust filters for CN systems with heavy-tailed measurement noises, and the proposed MCUPF has lower computational complexity than existing robust particle filters.

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“…The method has been successfully used for robust state estimation, inertial navigation system and visual tracking applications (Wang et al, 2010;Gao et al, 2014;Hou, 2014). The development of a RKF uses the Huber technique applied to a linear regression problem at each…”

mentioning

confidence: 99%

Robust Huber-Based Cubature Kalman Filter for GPS Navigation Processing

2016

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A robust state estimation technique based on the Huber-based Cubature Kalman Filter (HCKF) is proposed for Global Positioning System (GPS) navigation processing. The Cubature Kalman Filter (CKF) employs a third-degree spherical-radial cubature rule to compute the Gaussian weighted integration, such that the numerical instability induced by round-off errors can be avoided. In GPS navigation, the filter-based estimation of the position and velocity states can be severely degraded due to contaminated measurements caused by outliers or deviation from a Gaussian distribution assumption. For the signals contaminated with non-Gaussian noise or outliers, a robust scheme combining the Huber M-estimation methodology and the CKF framework is beneficial where the Huber M-estimation methodology is used to reformulate the measurement information of the CKF. GPS navigation processing using the HCKF algorithm has been carried out and the performance has been compared to those based on the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and CKF approaches. Simulation and experimental results presented in this paper confirm the effectiveness of the method. K E Y WO R D S 1. GPS navigation.2. Unscented Kalman filter. 3. Cubature Kalman filter 4. Huber M-estimation.

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Robust Huber-Based Iterated Divided Difference Filtering with Application to Cooperative Localization of Autonomous Underwater Vehicles

Cited by 26 publications

References 39 publications

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

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

Maximum Correntropy Based Unscented Particle Filter for Cooperative Navigation with Heavy-Tailed Measurement Noises

Robust Huber-Based Cubature Kalman Filter for GPS Navigation Processing

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