非高斯噪声背景的水下INS-LBL组合导航方法  

Underwater INS-LBL integrated navigation algorithm under non-gaussian noise

作　　者：成月 曹园山 赵俊波[1] 李锦 葛锡云[1] CHENG Yue;CAO Yuanshan;ZHAO Junbo;LI Jin;GE Xiyun(China Ship Scientific Research Center,Wuxi 214082,China)

机构地区：[1]中国船舶科学研究中心,江苏无锡214082

出　　处：《舰船科学技术》2024年第15期121-124,共4页Ship Science and Technology

摘　　要：自主水下航行器(AUV)是水下自主协同作业的重要装备,在水下获取持续可靠的高精度定位导航信息是AUV实施任务的重要前提。针对实际环境中系统噪声不再满足高斯分布、难以获得准确模型,进一步造成传统组合导航系统无法精准定位的问题,设计了一种基于最大熵卡尔曼滤波的水下INS-LBL组合导航方法。以INS与LBL之间的水声伪距为系统量测量,建立组合导航系统模型,削弱水下声通信产生的时延误差;采用最大熵卡尔曼滤波算法实现数据融合,提高复杂噪声干扰下的系统定位精度及鲁棒性。仿真结果表明,在非高斯噪声背景下,基于最大熵卡尔曼滤波的水下INS-LBL组合导航方法能够有效抑制干扰,提高系统定位精度。Autonomous underwater vehicles(AUV) arethe mainequipments for underwater operations,and obtaining accurate positioning is the prerequisite for AUVs to complete tasks.Due to the fact that the system noise in actual environment is non-Gaussian and it is difficult to obtain an exact model,further causing low positioning accuracy,an INS-LBL integrated navigation algorithm based on maximum correntropy Kalman filter(MCKF) is proposed.Firstly,establish the integrated navigation model based on pseudorangebetween INS and LBL as the system measurement to reduce the acoustic delay.In addition,the MCKF is used to improve positioning accuracyand robustness under complex noise interference.Simulation results demonstrate that even innon-Gaussian noise environment,theproposed algorithm can suppress noise to ensure the high positioning accuracy.

关 键 词：最大熵卡尔曼滤波 水下导航 长基线 自主水下航行器 

分 类 号：TJ630.1[兵器科学与技术—武器系统与运用工程]