基于改进变分贝叶斯滤波的SINS/DVL/LBL组合导航技术

赵俊波; 葛锡云; 成 月; 李 锦

doi:10.11993/j.issn.2096-3920.2021.01.008

基于改进变分贝叶斯滤波的SINS/DVL/LBL组合导航技术

doi: 10.11993/j.issn.2096-3920.2021.01.008

中国船舶科学研究中心, 江苏无锡, 214082

基金项目: 江苏省自然科学基金项目(BK20180171); 海南省重大科技计划项目(ZDKJ2019002).

详细信息

作者简介:
赵俊波(1990-), 男, 硕士, 工程师, 主要研究方向为水下组合导航技术.

中图分类号: TJ630 TP391.9
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- 文章访问数: 180
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- PDF下载量: 165
- 被引次数: 0
出版历程
- 收稿日期: 2020-04-20
- 修回日期: 2020-05-23
- 刊出日期: 2021-03-01

SINS/DVL/LBL-integrated Navigation Technology Based on Improved Variational Bayesian Filtering

China Ship Scientific Research Center, Wuxi 214082, China

摘要

摘要: 为解决水下航行器捷联惯性导航系统(SINS)与多普勒计程仪(DVL)、长基线(LBL)定位设备组合导航问题, 提出使用集中式滤波方案, 并建立了SINS/DVL/LBL组合导航模型。在组合导航过程中, 考虑到使用经典的卡尔曼滤波方法会存在由量测噪声方差时变和野值干扰而导致滤波精度下降的问题, 通过将变分贝叶斯滤波与IGGⅢ权函数相结合, 提出了一种改进变分贝叶斯滤波方法。组合导航仿真结果表明: 文中滤波方法具有较强的自适应能力和抗野值能力, 其滤波精度优于经典的卡尔曼滤波方法和变分贝叶斯滤波方法。
- 水下航行器 /
- 组合导航 /
- 变分贝叶斯 /
- IGGⅢ权函数 /
- 野值
Abstract: To solve the integrated navigation problem of an undersea vehicle strapdown inertial navigation system (SINS), Doppler velocity log(DVL), and long baseline(LBL) location equipment, a centralized filtering scheme is proposed in this study and a SINS/DVL/LBL-integrated navigation model is established. During integrated navigation, using the classical Kalman filtering method leads to the problem in which the filtering accuracy is reduced due to time-varying measurement noise variance and the interference of outliers. Accordingly, an improved variational Bayesian filtering method is proposed by combining a variational Bayesian filter with an IGGIII weight function. Integrated navigation simulation is conducting using MATLAB software, and results show that the proposed filtering method has strong self-adaptive and anti-outlier abilities as well as a higher filtering accuracy than the classical Kalman filtering and variational Bayesian filtering methods.
- undersea vehicle /
- integrated navigation /
- variational Bayesian /
- IGGⅢ weight function /
- outliers

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参考文献(15)

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