Information Fusion Algorithms in Ins/Smns Integrated Navigation System

DOI

10.2991/iccasm.2012.350How to use a DOI?

Keywords

Information fusion, Improved Kalman filter, Variable step-size LMS, Normalized LMS

Abstract

As the math model and the noise statistical information are difficult to be addressed precisely and the Scene Matching Navigation System (SMNS) output is stochastic, limited and probably mismatching, only a few algorithms suitable for Inertial Navigation System /Scene Matching Navigation System (INS/SMNS) integrated navigation system for information fusion were developed. In order to find new algorithms suitable for this system, this paper studies the issue as follows. Firstly, this paper presents the improved Kalman filter by applying the methods of extrapolation, discretizing system model in unequal interval and eliminating the measurement output delay to the common Kalman filter. Secondly, this paper studies the variable step-size LMS algorithm and normalized LMS algorithm basing onH optimal estimation. Lastly this paper applies them to the INS/SMNS integrated navigation. Simulation results demonstrate that they are suitable for INS/SMNS integrated navigation.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jianyuan Xu
AU  - Jingjing Zhao
PY  - 2012/08
DA  - 2012/08
TI  - Information Fusion Algorithms in Ins/Smns Integrated Navigation System
BT  - Proceedings of the 2012 International Conference on Computer Application and System Modeling (ICCASM 2012)
PB  - Atlantis Press
SP  - 1368
EP  - 1372
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasm.2012.350
DO  - 10.2991/iccasm.2012.350
ID  - Xu2012/08
ER  -