文献类型：期刊文献

英文题名：Smoothing Boundary Layer-Based Iterative CKF and Its Application to Attitude Estimation in Cooperative USV Swarms

作者：Shi, Chunfeng Chen, Xiyuan Jiang, Xuyang Zhong, Yulu

第一作者：Shi, Chunfeng

通信作者：Chen, XY[1]

机构：[1]Southeast Univ, Sch Instrument Sci & Engn, State Key Lab Comprehens PNT Network & Equipment T, Nanjing 210096, Peoples R China;[2]Southeast Univ, Key Lab MicroInertial Instrument & Adv Nav Technol, Minist Educ, Nanjing 210096, Peoples R China;[3]Southeast Univ, Key Lab Microinertial Instrument & Adv Nav Technol, Minist Educ, Nanjing 210096, Peoples R China;[4]Guizhou Inst Technol, Sch Aerosp Engn, Guiyang 550025, Guizhou, Peoples R China

第一机构：Southeast Univ, Sch Instrument Sci & Engn, State Key Lab Comprehens PNT Network & Equipment T, Nanjing 210096, Peoples R China

通信机构：corresponding author), Southeast Univ, Sch Instrument Sci & Engn, State Key Lab Comprehens PNT Network & Equipment T, Nanjing 210096, Peoples R China.

年份：2025

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20254319387176);Scopus(收录号：2-s2.0-105019586172);WOS:【SCI-EXPANDED(收录号:WOS:001591770300001)】；

基金：This work was supported in part by Guizhou Provincial Key Technology R&D Program under Grant XKBF [2025] 032, in part by the National Natural Science Foundation of China under Grant 61873064, and in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX23_0233.

语种：英文

外文关键词：Navigation; Estimation; Accuracy; Pollution measurement; Adaptation models; Iterative methods; Vectors; Uncertainty; Noise measurement; Kalman filters; Attitude estimation; cooperative navigation; cubature Kalman filter; multiuncrewed surface vessel; smoothing boundary layer (SBL)

摘要：Accurate attitude estimation is critical for navigation systems of uncrewed surface vehicle (USV) swarms, particularly under the challenges posed by cooperative operations in dynamic marine environments. In such swarms, significant misalignment errors arise not only from individual vessel motions induced by wind and waves but also from inter vessel kinematic coupling and asynchronous observation constraints, severely degrading cooperative navigation performance. To address this issue, this article proposes an attitude estimation method for a cooperative navigation system. By fully leveraging the advantages of the swarm, cooperative measurements are introduced as auxiliary information. Building upon the iterative cubature Kalman filter based on maximum a posteriori estimation, a novel measurement quality assessment approach utilizing a smooth boundary layer in the temporal-spatial dimension is designed to mitigate model mismatches in nonlinear conditions. A sliding window technique is employed to control the temporal span dynamically, adaptively adjusting the iteration count and computing an online correction for contaminated auxiliary information through a full-dimensional weighting matrix. Simulations and experimental results demonstrate that the proposed method effectively addresses model mismatches and achieves superior attitude estimation accuracy compared to existing approaches.