基于紧组合的SINS/DVL/USBL导航算法

章彩霞; 刘锡祥; 黄永江; 陈世杰; 陶育杰

doi:10.11993/j.issn.2096-3920.2022-0076

基于紧组合的SINS/DVL/USBL导航算法

doi: 10.11993/j.issn.2096-3920.2022-0076

章彩霞^{1, 2,},
刘锡祥^{1, 2, ,},
黄永江^{1, 2},
陈世杰^{1, 2},
陶育杰^{1, 2}

1.
东南大学仪器科学与工程学院, 江苏无锡, 214026
2.
微惯性仪表与先进导航技术教育部重点实验室, 江苏南京, 210096

基金项目: 国家自然科学基金项目资助(51979041、61973079).

详细信息

作者简介:
章彩霞(1998-), 女, 在读硕士, 主要研究方向为导航技术

通讯作者:
刘锡祥(1976-), 男, 教授, 博导, 主要研究方向为惯性导航、组合导航与信息融合技术

中图分类号: TJ630.32; U666.7
计量
- 文章访问数: 222
- HTML全文浏览量: 87
- PDF下载量: 55
- 被引次数: 0
出版历程
- 收稿日期: 2022-11-18
- 修回日期: 2023-01-13
- 录用日期: 2023-02-14
- 网络出版日期: 2023-12-06

SINS/DVL/USBL Navigation Algorithm Based on Tight Integration

ZHANG Caixia^{1, 2
,},
LIU Xixiang^{1, 2
, ,},
HUANG Yongjiang^{1, 2},
CHEN Shijie^{1, 2},
TAO Yujie^{1, 2}

1.
School of Instrument Science and Engineering, Southeast University, Wuxi 214026, China
2.
Key Laboratory of Micro-inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China

摘要

摘要: 针对自主水下航行器在水下复杂环境下对于高精度、高可靠性的导航定位需求, 提出了一种捷联式惯导系统(SINS)/多普勒测速仪(DVL)/超短基线(USBL)定位系统紧组合导航算法, 构建了基于频移量测的SINS/DVL紧组合量测方程和基于相对测量信息量测的SINS/USBL紧组合量测方程, 通过集中卡尔曼滤波的方法, 对SINS、DVL和USBL的信息进行融合处理。针对水下复杂环境造成导航精度下降的问题, 充分考虑DVL和USBL的数据野值情况, 利用卡方检验法进行判断后对故障数据隔断处理, 实时更新量测方程维数, 保证系统精度。仿真验证可知, 该算法对比其他不同组合模型算法具有较高的定位精度, 对比传统的基于速度量测和相对位置量测的SINS/DVL/USBL集中滤波方法精度可提升约23%; 在DVL数据失效时, 定位误差较正常情况仅增长5.2%; 在USBL数据失效时, 定位误差较正常情况增长165.4%, 鲁棒性和稳定性显著优于基于其他量测的SINS/DVL/USBL的集中滤波导航算法, 可实现高精度和高可靠性的水下导航定位。
- 自主水下航行器 /
- 紧组合 /
- 捷联式惯导系统 /
- 多普勒测速仪 /
- 超短基线 /
- 信息融合
Abstract: For autonomous undersea vehicle’s requirement of high-precision and high-reliability navigation and positioning in complex underwater environments, a tightly integrated navigation algorithm involving strapdown inertial navigation system(SINS)/Doppler velocity log(DVL)/ultra-short baseline(USBL) positioning system was proposed. A SINS/DVL tightly integrated measurement equation based on frequency shift measurement and a SINS/USBL tightly integrated measurement equation based on relative measurement information were established. SINS, DVL, and USBL information were fused using a concentrated Kalman filter. In view of the decline of navigation accuracy caused by the complex underwater environment, the DVL and USBL data outliers were fully considered, and the fault data were detected and isolated by Chi-square detection. In addition, the measurement equation dimensions were updated in real time to ensure the precision of the system. The simulation results show that the proposed algorithm has higher positioning precision than other integrated model algorithms. Compared with the traditional SINS/DVL/USBL concentrated filtering method based on velocity measurement and relative position measurement, the precision can be improved by about 23%. In the case of DVL data failure, the positioning error only increases by 5.2% compared with the normal condition. In the case of USBL data failure, the positioning error increases by 165.4% compared with the normal condition, and the robustness and stability are significantly better than the SINS/DVL/USBL concentrated filtering navigation algorithm based on other measurements. Therefore, it can achieve high-precision and high-reliability underwater navigation and positioning.
- autonomous undersea vehicle /
- tight integration /
- strapdown inertial navigation system /
- Doppler velocity log /
- ultra-short baseline /
- information fusion

HTML全文

图 1 SINS/DVL/USBL组合导航系统结构图

Figure 1. SINS/DVL/USBL integrated navigation system structrue

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图 2 DVL十字型四波束Janus配置

Figure 2. DVL cross-type four-beam Janus configuration

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图 3 仿真轨迹

Figure 3. Simulation track

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图 4 东向位置误差

Figure 4. East position error

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图 5 北向位置误差

Figure 5. North position error

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图 6 水平定位误差

Figure 6. Level position error

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图 7 DVL失效数据情况

Figure 7. DVL failure data situation

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图 8 USBL失效数据情况

Figure 8. USBL failure data situation

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图 9 DVL数据失效情况下的水平定位误差

Figure 9. Level positioning error in case of DVL data failure

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图 10 USBL数据失效情况下的水平定位误差

Figure 10. Level positioning error in case of USBL data failure

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表 1 传感器误差参数

Table 1. The error parameters of sensors

设备参数	误差
SINS陀螺仪零漂/((°)/h)	0.02
SINS陀螺仪随机游走/$({ {({}^\circ)}/{\sqrt {\text{h} } } })$	0.005
SINS加速度计零偏/μg	50
SINS加速度计游走/$({ { {\text{μg} } } / {\sqrt { {\rm{Hz} } } } })$	50
DVL比例因子误差/%	0.5
DVL安装角误差/(°)	$[{0.3 }\;\;\;{ - 0.2 }\;\;\;{0.6 }]$
USBL方位角、高度角误差/(°)	$0.1$、$0.1$
USBL斜距误差系数/%	0.5
USBL安装角误差/(°)	$[{0.5 }\;\;\;{ - 0.5 }\;\;\;{ { {0.5}} } ]$
PS常值偏置/m	0.05
PS随机偏置/m	0.01

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表 2 不同组合导航方法水平定位误差RMSE对比

Table 2. Comparison of level positioning error RMSE

组合导航方法	水平定位误差RMSE/m
$S {D_T}$	3.224 2
$S {U_T}$	1.852 9
$S {D_L}{U_T}$	0.579 4
$S {D_T}{U_T}$	0.446 5

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表 3 数据失效情况下不同量测的集中滤波方法水平定位误差RMSE

Table 3. Level positioning error RMSE of the concentrated filter methods based on different measurements in case of data invalidation

数据情况	$S {D_L}{U_T}$定位误差RMSE/m	$S {D_T}{U_T}$定位误差RMSE/m
数据正常	0.5794	0.4465
DVL数据异常	0.8091	0.4698
USBL数据异常	1.6710	1.1848

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