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Sensors 2014, 14(12), 24523-24542; doi:10.3390/s141224523

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

College of Automation, Harbin Engineering University, Harbin 150001, China
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Received: 2 September 2014 / Revised: 20 November 2014 / Accepted: 15 December 2014 / Published: 19 December 2014
(This article belongs to the Section Physical Sensors)
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Abstract

A new algorithm called Huber-based iterated divided difference filtering (HIDDF) is derived and applied to cooperative localization of autonomous underwater vehicles (AUVs) supported by a single surface leader. The position states are estimated using acoustic range measurements relative to the leader, in which some disadvantages such as weak observability, large initial error and contaminated measurements with outliers are inherent. By integrating both merits of iterated divided difference filtering (IDDF) and Huber’s M-estimation methodology, the new filtering method could not only achieve more accurate estimation and faster convergence contrast to standard divided difference filtering (DDF) in conditions of weak observability and large initial error, but also exhibit robustness with respect to outlier measurements, for which the standard IDDF would exhibit severe degradation in estimation accuracy. The correctness as well as validity of the algorithm is demonstrated through experiment results. View Full-Text
Keywords: robustness; nonlinear state estimation; Huber-based iterated divided difference filtering; autonomous underwater vehicles; cooperative localization robustness; nonlinear state estimation; Huber-based iterated divided difference filtering; autonomous underwater vehicles; cooperative localization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Gao, W.; Liu, Y.; Xu, B. Robust Huber-Based Iterated Divided Difference Filtering with Application to Cooperative Localization of Autonomous Underwater Vehicles. Sensors 2014, 14, 24523-24542.

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