[1]朱倚娴,程向红,周玲,等.组合导航系统中异步多传感器信息融合算法[J].东南大学学报(自然科学版),2018,48(2):195-200.[doi:10.3969/j.issn.1001-0505.2018.02.001]
 Zhu Yixian,Cheng Xianghong,Zhou Ling,et al.Information fusion algorithm for asynchronous multi-sensors in integrated navigation systems[J].Journal of Southeast University (Natural Science Edition),2018,48(2):195-200.[doi:10.3969/j.issn.1001-0505.2018.02.001]
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组合导航系统中异步多传感器信息融合算法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
48
期数:
2018年第2期
页码:
195-200
栏目:
仪器科学与技术
出版日期:
2018-03-20

文章信息/Info

Title:
Information fusion algorithm for asynchronous multi-sensors in integrated navigation systems
作者:
朱倚娴1程向红1周玲12刘全1
1东南大学微惯性仪表与先进导航技术教育部重点实验室, 南京 210096; 2运城学院物理与电子工程系, 运城 044000
Author(s):
Zhu Yixian1 Cheng Xianghong1 Zhou Ling12 Liu Quan1
1Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology of Ministry of Education, Southeast University, Nanjing 210096, China
2Department of Physics and Electronic Engineering, Yuncheng University, Yuncheng 044000, China
关键词:
组合导航系统 异步多传感器 信息融合 多尺度 Kalman滤波
Keywords:
integrated navigation system asynchronous multi-sensors information fusion multiscale Kalman filter
分类号:
U666.1
DOI:
10.3969/j.issn.1001-0505.2018.02.001
摘要:
针对组合导航系统中多个传感器采样频率不同且存在量测滞后的问题,提出一种基于多尺度数据分块的组合导航信息融合算法.建立最高采样率下的系统模型,通过状态和观测的分块得到基于多尺度的系统模型,利用不同尺度上的观测信息在各尺度上进行Kalman滤波,并经融合最终获得基于全局的状态估计值.将该算法用于SINS/DVL/TAN组合导航系统仿真,结果表明,在异步多传感器量测的情况下,基于多尺度数据分块的信息融合算法与非等间隔Kalman滤波算法相比,北向速度最大误差减小24.1%,纬度最大误差减小23.8%,东向速度最大误差和经度最大误差均略有减小.因此,信息融合算法具有较高的滤波精度,有利于提高系统的导航定位精度.
Abstract:
Aimed at multiple sensor measurements with different rates and the delayed measurement, an information fusion algorithm based on multiscale estimation theory and data segmentation was proposed.A system model based on the maximum sampling rate was established. Then, multiscale dynamic models were bulit by partitioning the state and measurement information. Kalman filters were performed by the measurements on different scales. Finally, the global state estimation was obtained by using the data fusion method. To verify its validity, the algorithm was used in an integrated navigation system with strapdown inertial navigation system(SINS)/Doppler velocity log(DVL)/terrain aided navigation(TAN). The simulation results show that under the situation measured by asynchronous multi-sensors, the algorithm has the higher degree of accuracy compared with the in-coordinate interval Kalman filtering algorithm. The maximum errors of the north velocity and the latitude decrease by 24.1% and 23.8%,respectively. The maximum errors of the east velocity and the longitude are slightly decreased. Thus, the positioning precision of the navigation system by the algorithm can be improved.

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备注/Memo

备注/Memo:
收稿日期: 2017-10-09.
作者简介: 朱倚娴(1989—),女,博士生; 程向红(联系人),女,博士,教授,博士生导师,xhcheng@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(61374215, 61773116).
引用本文: 朱倚娴,程向红,周玲,等.组合导航系统中异步多传感器信息融合算法[J].东南大学学报(自然科学版),2018,48(2):195-200. DOI:10.3969/j.issn.1001-0505.2018.02.001.
更新日期/Last Update: 2018-03-20