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

Sensors

Wei Gao²  Yalong Liu¹ 

[1] College of Automation, Harbin Engineering University, Harbin 150001, China;

关键词: robustness;    nonlinear state estimation;    Huber-based iterated divided difference filtering;    autonomous underwater vehicles;    cooperative localization;   

DOI  :  10.3390/s141224523

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.