IDEAS home Printed from https://ideas.repec.org/a/hin/complx/3039061.html
   My bibliography  Save this article

Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Author

Listed:
  • Lijun Song
  • Zhongxing Duan
  • Bo He
  • Zhe Li

Abstract

The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

Suggested Citation

  • Lijun Song & Zhongxing Duan & Bo He & Zhe Li, 2018. "Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment," Complexity, Hindawi, vol. 2018, pages 1-7, January.
    • Handle: RePEc:hin:complx:3039061
    DOI: 10.1155/2018/3039061
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2018/3039061.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2018/3039061.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2018/3039061?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lei Yu & Junyi Hou, 2018. "Large-Screen Interactive Imaging System with Switching Federated Filter Method Based on 3D Sensor," Complexity, Hindawi, vol. 2018, pages 1-11, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:3039061. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.