Kalman filtering for navigation application
In 1960, R.E. Kalman published his papers on a recursive predictive filter that is based on the use of state space techniques and recursive algorithm. Since then, the Kalman filter has been the subject of extensive research and application, particularly in the field of navigation. Nowadays, most o...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
2011
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/46500 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-46500 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-465002023-07-07T15:57:57Z Kalman filtering for navigation application Zhou, JingJing. Ling Keck Voon School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering In 1960, R.E. Kalman published his papers on a recursive predictive filter that is based on the use of state space techniques and recursive algorithm. Since then, the Kalman filter has been the subject of extensive research and application, particularly in the field of navigation. Nowadays, most of the navigation systems use not only the Global Positioning System (GPS) but also an Inertial Navigation System (INS) to help driver to find his way. These two systems complement each other and improve the navigation accuracy and reliability. And the Kalman filter provides the basis for this application. In this report, the task is to program an indirect Kalman filter in Matlab to estimate the error states of the INS and correct the navigation states with GPS measurements to prevent divergence due to modeling errors. The study of Kalman filtering includes a description of the standard Kalman filter and its algorithm with 2 main steps: the prediction and correction steps. Interesting examples, such as applying the Kalman filter to estimate the Cumulative Grade Point Average (CGPA) were explored to provide an understanding with its practical aspects. The elementary study of INS is based on Matlab program of simINS.m, which contributed by DSO. Progressively, error state equations of INS were established and indirect feedforward Kalman filter was used to estimate the error states, thereby correct the navigation states. The results are verified against results from original INS simulation, which without Kalman filter optimization. Bachelor of Engineering 2011-12-13T03:07:53Z 2011-12-13T03:07:53Z 2011 2011 Final Year Project (FYP) http://hdl.handle.net/10356/46500 en Nanyang Technological University 93 p. application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering |
spellingShingle |
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Zhou, JingJing. Kalman filtering for navigation application |
description |
In 1960, R.E. Kalman published his papers on a recursive predictive filter that is based on the use of state space techniques and recursive algorithm. Since then, the Kalman filter has been the subject of extensive research and application, particularly in the field of navigation.
Nowadays, most of the navigation systems use not only the Global Positioning System (GPS) but also an Inertial Navigation System (INS) to help driver to find his way. These two systems complement each other and improve the navigation accuracy and reliability. And the Kalman filter provides the basis for this application.
In this report, the task is to program an indirect Kalman filter in Matlab to estimate the error states of the INS and correct the navigation states with GPS measurements to prevent divergence due to modeling errors.
The study of Kalman filtering includes a description of the standard Kalman filter and its algorithm with 2 main steps: the prediction and correction steps. Interesting examples, such as applying the Kalman filter to estimate the Cumulative Grade Point Average (CGPA) were explored to provide an understanding with its practical aspects.
The elementary study of INS is based on Matlab program of simINS.m, which contributed by DSO. Progressively, error state equations of INS were established and indirect feedforward Kalman filter was used to estimate the error states, thereby correct the navigation states. The results are verified against results from original INS simulation, which without Kalman filter optimization. |
author2 |
Ling Keck Voon |
author_facet |
Ling Keck Voon Zhou, JingJing. |
format |
Final Year Project |
author |
Zhou, JingJing. |
author_sort |
Zhou, JingJing. |
title |
Kalman filtering for navigation application |
title_short |
Kalman filtering for navigation application |
title_full |
Kalman filtering for navigation application |
title_fullStr |
Kalman filtering for navigation application |
title_full_unstemmed |
Kalman filtering for navigation application |
title_sort |
kalman filtering for navigation application |
publishDate |
2011 |
url |
http://hdl.handle.net/10356/46500 |
_version_ |
1772827196545564672 |