Cooperative Positioning for Multi-AUVs Based on Factor Graph and Maximum Correntropy
نویسندگان
چکیده
منابع مشابه
Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory
Cooperative localization (CL) is considered a promising method for underwater localization with respect to multiple autonomous underwater vehicles (multi-AUVs). In this paper, we proposed a CL algorithm based on information entropy theory and the probability hypothesis density (PHD) filter, aiming to enhance the global localization accuracy of the follower. In the proposed framework, the follow...
متن کاملMulti-label Active Learning Based on Maximum Correntropy Criterion: Towards Robust and Discriminative Labeling
Multi-label learning is a challenge problem in computer vision fields. Since annotating a multilabel instance costs greatly, multi-label classification has become a hot topic research. State-of-theart active learning methods either annotate all the relevant samples without diagnosing discriminative information in the labels or annotate only limited discriminative samples manually, that has weak...
متن کاملKernel recursive maximum correntropy
Zongze Wu 1 , Jiahao Shi 1 , Xie Zhang 1 , Wentao Ma 2 , Badong Chen 2* , Senior Member, IEEE 1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510640, China 2. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China * Fax: 86-29-82668672,Tel:86-29-82668802 ext. 8009, [email protected] Abstract—I...
متن کاملMaximum Correntropy Kalman Filter
—Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously. To improve the robustness of KF against impulsive noises, we propose in ...
متن کاملRegularized maximum correntropy machine
In this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned by minimizing transitional loss functions are sensitive to the noisy and outlying labels of training samples, because the transitional loss functions are equally applied to all the samples. To solve this problem, we propose to learn the cl...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2948864