Application of neural network aided Kalman filtering to SINS/GPS

CUI Liu-zheng; GAO Si-yuan; JIA Hong-guang; CHU Hai-rong; JIANG Rui-kai

doi:10.3788/OPE.20142205.1304

您当前的位置：

首页 >

文章列表页 >

Application of neural network aided Kalman filtering to SINS/GPS

更新时间：2020-08-13

- Application of neural network aided Kalman filtering to SINS/GPS
- Optics and Precision Engineering Vol. 22, Issue 5, Pages: 1304-1311(2014)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院大学北京,100039
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20142205.1304
  CLC： V421.6;U666.11
- Received：04 February 2013，
  
  Revised：01 April 2013，
  
  Published：25 May 2014
- 稿件说明：
移动端阅览
崔留争, 高思远, 贾宏光等. 神经网络辅助卡尔曼滤波在组合导航中的应用[J]. 光学精密工程, 2014,22(5): 1304-1311

CUI Liu-zheng, GAO Si-yuan, JIA Hong-guang etc. Application of neural network aided Kalman filtering to SINS/GPS[J]. Editorial Office of Optics and Precision Engineering, 2014,22(5): 1304-1311
崔留争, 高思远, 贾宏光等. 神经网络辅助卡尔曼滤波在组合导航中的应用[J]. 光学精密工程, 2014,22(5): 1304-1311 DOI： 10.3788/OPE.20142205.1304.

CUI Liu-zheng, GAO Si-yuan, JIA Hong-guang etc. Application of neural network aided Kalman filtering to SINS/GPS[J]. Editorial Office of Optics and Precision Engineering, 2014,22(5): 1304-1311 DOI： 10.3788/OPE.20142205.1304.

摘要

为使基于微机电系统的捷联惯性导航/全球定位（MEMS-SINS/GPS）组合导航系统在GPS接收机无法正常工作时，仍能提供满足精度要求的导航信息，提出了径向基函数神经网络（RBFNN）辅助自适应卡尔曼滤波（AKF）的信息融合方法。首先，基于该方法设计了由神经网络训练与预测两种模式构成的组合导航系统。在GPS可用时，对RBFNN进行在线训练；在GPS失锁时，由RBFNN预测AKF更新过程的量测输入。然后，建立了RBFNN与AKF的数学模型，并设计了RBFNN的训练策略与AKF的自适应算法。最后，通过跑车实验验证了该信息融合方法的有效性。实验结果表明，在GPS断开时间为40s和100s时，系统的位置精度分别优于15m和90m。该信息融合方法能在GPS失锁时对导航误差发散进行有效阻尼，是适用于小型无人机、制导炸弹与车辆的一种低成本、高鲁棒性、中等精度的导航方案。

Abstract

To allow Micro-electro-mechanical System(MEMS)-based SINS/GPS integrated navigation systems to meet the accuracy requirements during GPS outages

a Radial Basis Function Neural Network (RBFNN) aided Adaptive Kalman Filtering (AKF) information fusion method was proposed.Firstly

the system structure consisting of dual modes of RBFNN training and prediction was designed.The RBFNN was trained while GPS signals were available and the inputs for AKF measurement updates were predicted during the GPS outages.Then

the mathematic models for RBFNN and AKF were built and the training strategy for RBFNN and the adaptive algorithm for AKF were designed.Finally

the performance of the proposed information fusion method was validated using real field test data.Test and experiment results show that the position precisions are better than 15 m and 90 m during GPS outages at 40 s and 100 s

respectively.The proposed information fusion method can effectively damp the divergence of the navigation error during GPS outages and can provide a low-cost

high-robustness

and medium-accuracy navigation scheme for small Unmanned Aerial Vehicles(UAVs)

guided bombs and land vehicles.

关键词

Keywords

references

AGGARWAL P, SYED Z, NOURELDIN A, et al..MEMS-based Integrated Navigation [M].MA:Artech House, 2010.

谢刚.GPS原理与接收机设计 [M].北京:电子工业出版社, 2009. XIE G.Principles of GPS and Receiver Design [M].Beijing:Publishing House of Electronics Industry, 2009.(in Chinese)

GROVES P D.Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems [M].MA:Artech House, 2008.

罗大成, 王仕成, 曾洪贵, 等. 紧耦合GPS/ INS 组合导航技术仿真研究[J]. 系统工程与电子技术, 2009, 31(12):2929-2933. LUO D CH,WANG SH CH, ZENG H G, et al..Simulation research on the technology of the tightly-coupled GPS/ INS integration [J].Systems Engineering and Electronics, 2009, 31(12):2929-2933.(in Chinese)

WANG X L, LI Y F.An innovative scheme for SINS/GPS ultra-tight integration system with low-grade IMU [J].Aerospace Science and Technology, 2012, 23(1), 452-460.

GREWAL M S, ANDREWS A P.Applications of Kalman filtering in aerospace 1960 to the present [J].IEEE Control Systems, 2010, 30(3), 69-78.

GREWAL M S, ANDREWS A P.Kalman Filtering:Theory and Practice Using Matlab [M].3rd edition, Hoboken:Wiley & Sons, Inc, 2008.

韩辅君, 徐静, 宋世忠. 基于低成本多传感器的自适应组合滤波[J]. 光学精密工程, 2011, 19(12):3007-3015. HAN F J, XU J, SONG SH ZH.Adaptive attitude estimation filtering with low-cost multi-sensors for MAHRS [J].Opt.Precision Eng. , 2011,19(12):3007-3015.(in Chinese)

HAMID W A, NOURELDIN A, EL-SHEIMY N.Adaptive fuzzy prediction of low-cost inertial-based positioning errors [J].IEEE Transactions on Fuzzy Systems,2007, 15(3):519-529.

NOURELDIN A, KARAMAT T B.Performance enhancement of MEMS-based INS/GPS integration for low-cost navigation applications [J].IEEE Transactions on Vehicular Technology, 2009, 58(3), 1077-1096.

MALLESWARAN M, VAIDEHI V, JEBARSI M.Neural networks review for performance enhancement in GPS/INS integration.2012 International Conference on Recent Trends in Information Technology(ICRTIT), 2012,34-39.

NOURELDIN A, EL-SHAFI A, BAYOUMI M.GPS/INS integration utilizing dynamic neural networks for vehicular navigation [J].Information Fusion, 2011, 12(1):48-57.

ENGELBRECHT A P.Computation Intelligence:An Introduction [M].2nd ed, NJ:Wiley Publishing Inc., 2009.

WANG J J, WANG J L, SINCLAIR D, et al..A neural network and Kalman filter hybrid approach for GPS/INS integration.International Symposium on GPS/GNSS, Juju:12th IAIN Congress, 2006,277-282.

CHIANG K W, HUANG Y W.An intelligent navigator for seamless INS/GPS integrated land vehicle navigation applications [J].Applied Soft Computing,2008,8(1):722-733.

林雪原, 鞠建波. 利用神经网络预测的GPS/SINS组合导航系统算法研究[J]. 武汉大学学报:信息科学版, 2011, 36(5):602-604. LIN X Y, JU J B.GPS/SINS integrated navigation algorithm based on neural network prediction [J].Geomatics and Information Science of Wuhan University, 2011, 36(5):602-604.(in Chinese)

CHIANG K W, CHANG H W.Intelligent sensor positioning and orientation through constructive neural network-embedded INS/GPS integration algorithms [J].Sensors, 2010, 10(10):9252-9285.

CHIANG K W,NOURELDIN A, EL-SHEIMY N.A new weight updating method for INS/GPS integration architectures based on neural networks [J].Measurement Science and Technology, 2004, 15(10):2053-2061.

TITTERTON D H,WESTON J L.Strapdown Inertial Navigation Technology [M].2nd edition,VA:AIAA, 2004.

魏彤, 郭蕊. 自适应卡尔曼滤波在无刷直流电机系统参数辨识中的应用[J]. 光学精密工程, 2012, 20(10):2308-2314. WEI T, GUO R.Application of adaptive Kalman filter to system identification of brushless DC motor [J].Opt.Precision Eng. ,2012, 20(10):2308-2314.(in Chinese)

Views

620

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Polarized light/MIMU integrated navigation SHKF attitude solving algorithm for motion acceleration suppression

Adaptive visual inertial geomagnetic tightly coupled positioning system

Multi-machine cooperative passive localization based on MEMS/GNSS

Fuzzy IAE-UKF integrated navigation method introducing system uncertainty

Application of multi-level information fusion for wear particle recognition of ferrographic images

Related Author

ZHANG Ran

LIU Chen

ZHOU Zijian

PEI Sen

JIN Rencheng

ZHAO Kaichun

WANG Kunfeng

WAN Zhenhua

Related Institution

Key Laboratory for Micro/Nano Technology and System of Liaoning Province ofDalian University of Technology

Faculty of Mechanical Engineering， Guangxi University

School of Instrument Science and Optoelectronic Engineering， Beijing Information Science and Technology University

Department of Precision Instrument， Tsinghua University

Space Vehicle Survival Technology and Effectiveness Evaluation Laboratory

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰