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
成月 1曹园山 1赵俊波 1李锦 1葛锡云1
作者信息
- 1. 中国船舶科学研究中心,江苏无锡 214082
- 折叠
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.
Abstract
Autonomous underwater vehicles(AUV)arethe mainequipments for underwater operations,and obtaining accurate positioning is the prerequisite for AU Vs to complete tasks.Due to the fact that the system noise in actual environ-ment is non-Gaussian and it is difficult to obtain an exact model,further causing low positioning accuracy,an INS-LBL in-tegrated navigation algorithm based on maximum correntropy Kalman filter(MCKF)is proposed.Firstly,establish the integ-rated 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Key words
maximum correntropy Kalman filter/underwater navigation/long baseline/AUV引用本文复制引用
出版年
2024
NETL
NSTL
万方数据