文献类型：期刊文献

英文题名：DMRCKF: A resampling-free robust CKF for USV swarm cooperative navigation under uncertain measurements

作者：Shi, Chunfeng Chen, Xiyuan Zhong, Yulu Gao, Ning

第一作者：Shi, Chunfeng

通信作者：Chen, XY[1];Chen, XY[2];Chen, XY[3]

机构：[1]Southeast Univ, 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]Guizhou Inst Technol, Sch Aerosp Engn, Guiyang 550025, Peoples R China

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

通信机构：corresponding author), Southeast Univ, State Key Lab Comprehens PNT Network & Equipment T, Nanjing 210096, Peoples R China;corresponding author), Southeast Univ, Key Lab Microinertial Instrument & Adv Nav Technol, Minist Educ, Nanjing 210096, Peoples R China;corresponding author), Guizhou Inst Technol, Sch Aerosp Engn, Guiyang 550025, Peoples R China.|贵州理工学院;

年份：2026

卷号：362

期号：P2

外文期刊名：OCEAN ENGINEERING

收录：;EI(收录号：20262320842339);Scopus(收录号：2-s2.0-105040700417);WOS:【SCI-EXPANDED(收录号:WOS:001792663800001)】；

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

语种：英文

外文关键词：Robust cubature Kalman Filter; Random time-delay; Denied measurements; Outliers; Cooperative navigation

摘要：Unmanned Surface Vessel (USV) swarms rely heavily on cooperative multi-sensor information fusion for oceanographic tasks. However, in practical maritime environments, cooperative navigation is frequently affected by uncertain measurements caused by wireless interference, vessel occlusion, dynamic swarm topology, and harsh sea conditions. These factors often lead to randomly occurring delays, denials, and outliers in cooperative measurements, which degrade navigation performance. To address these challenges, an enhanced robust Cubature Kalman Filter (CKF) method is proposed. Measurement delays are modeled as biases, and a nonlinear model for delay characterization is derived. A resampling-free robust CKF framework is introduced to maintain continuous posterior information transmission under frequent signal denials. To mitigate the impact of measurement outliers, an adaptive Mahalanobis distance (MD)-based method is incorporated, eliminating the need for manual threshold tuning under the considered uncertain measurement conditions. Simulations and lake experiments validate the proposed method and demonstrate its improved performance over existing approaches in handling measurement delays, denials, and outliers.