liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
Modular General-Purpose Data Filtering for Tracking
Linköping University, Department of Electrical Engineering. (Regler)
2008 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In nearly allmodern tracking systems, signal processing is an important part with state estimation as the fundamental component. To evaluate and to reassess different tracking systems in an affordable way, simulations that are in accordance with reality are largely used. Simulation software that is composed of many different simulating modules, such as high level architecture (HLA) standardized software, is capable of simulating very realistic data and scenarios.

A modular and general-purpose state estimation functionality for filtering provides a profound basis for simulating most modern tracking systems, which in this thesis work is precisely what is created and implemented in an HLA-framework. Some of the most widely used estimators, the iterated Schmidt extended Kalman filter, the scaled unscented Kalman filter, and the particle filter, are chosen to form a toolbox of such functionality. An indeed expandable toolbox that offers both unique and general features of each respective filter is designed and implemented, which can be utilized in not only tracking applications but in any application that is in need of fundamental state estimation. In order to prepare the user to make full use of this toolbox, the filters’ methods are described thoroughly, some of which are modified with adjustments that have been discovered in the process.

Furthermore, to utilize these filters easily for the sake of user-friendliness, a linear algebraic shell is created, which has very straight-forward matrix handling and uses BOOST UBLAS as the underlying numerical library. It is used for the implementation of the filters in C++, which provides a very independent and portable code.

Place, publisher, year, edition, pages
2008. , 120 p.
Keyword [en]
Extended kalman, unscented kalman, particle filter, tracking, data filtering, data fusion, hla
National Category
Control Engineering Signal Processing
URN: urn:nbn:se:liu:diva-14917ISRN: LITH-ISY-EX--08/4230--SEOAI: diva2:25591
Subject / course
Automatic Control and Communication
Available from: 2008-10-09 Created: 2008-09-30 Last updated: 2012-01-23Bibliographically approved

Open Access in DiVA

fulltext(1308 kB)1037 downloads
File information
File name FULLTEXT03.pdfFile size 1308 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Department of Electrical Engineering
Control EngineeringSignal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 1037 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 448 hits
ReferencesLink to record
Permanent link

Direct link