liu.seSök publikationer i DiVA
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Estimating the Shape of Targets with a PHD Filter
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan. (Sensor Fusion)
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan. (Sensor Fusion)ORCID-id: 0000-0002-3450-988X
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan. (Sensor Fusion)
2011 (Engelska)Ingår i: Proceedings of the 14th International Conference on Information Fusion, 2011Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper presents a framework for tracking extended targets which give rise to a structured set of measurements per each scan. The concept of a measurement generating point (MGP) which is defined on the boundary of each target is introduced. The tracking framework contains an hybrid statespace where MGP:s and the measurements are modeled by random finite sets and target states by random vectors. The target states are assumed to be partitioned into linear and nonlinear components and a Rao-Blackwellized particle filter is used for their estimation. For each state particle, a probability hypothesis density (PHD) filter is utilized for estimating the conditional set of MGP:s given the target states. The PHD kept for each particle serves as a useful means to represent information in the set of measurements about the target states. The early results obtained show promising performance with stable target following capability and reasonable shape estimates.

Ort, förlag, år, upplaga, sidor
2011.
Nyckelord [en]
Tracking, Data association, Particle filter, Kalman filter, Estimation, PHD filter, Extended target, Rao-Blackwellized particle filter
Nationell ämneskategori
Signalbehandling Reglerteknik
Identifikatorer
URN: urn:nbn:se:liu:diva-69945ISBN: 978-1-4577-0267-9 (tryckt)OAI: oai:DiVA.org:liu-69945DiVA, id: diva2:433343
Konferens
14th International Conference on Information Fusion, 5-8 July, Chicago, Illinois, USA
Projekt
CADICSTillgänglig från: 2011-08-12 Skapad: 2011-08-09 Senast uppdaterad: 2014-03-27Bibliografiskt granskad
Ingår i avhandling
1. Sensor Fusion for Automotive Applications
Öppna denna publikation i ny flik eller fönster >>Sensor Fusion for Automotive Applications
2011 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Mapping stationary objects and tracking moving targets are essential for many autonomous functions in vehicles. In order to compute the map and track estimates, sensor measurements from radar, laser and camera are used together with the standard proprioceptive sensors present in a car. By fusing information from different types of sensors, the accuracy and robustness of the estimates can be increased.

Different types of maps are discussed and compared in the thesis. In particular, road maps make use of the fact that roads are highly structured, which allows relatively simple and powerful models to be employed. It is shown how the information of the lane markings, obtained by a front looking camera, can be fused with inertial measurement of the vehicle motion and radar measurements of vehicles ahead to compute a more accurate and robust road geometry estimate. Further, it is shown how radar measurements of stationary targets can be used to estimate the road edges, modeled as polynomials and tracked as extended targets.

Recent advances in the field of multiple target tracking lead to the use of finite set statistics (FISST) in a set theoretic approach, where the targets and the measurements are treated as random finite sets (RFS). The first order moment of a RFS is called probability hypothesis density (PHD), and it is propagated in time with a PHD filter. In this thesis, the PHD filter is applied to radar data for constructing a parsimonious representation of the map of the stationary objects around the vehicle. Two original contributions, which exploit the inherent structure in the map, are proposed. A data clustering algorithm is suggested to structure the description of the prior and considerably improving the update in the PHD filter. Improvements in the merging step further simplify the map representation.

When it comes to tracking moving targets, the focus of this thesis is on extended targets, i.e., targets which potentially may give rise to more than one measurement per time step. An implementation of the PHD filter, which was proposed to handle data obtained from extended targets, is presented. An approximation is proposed in order to limit the number of hypotheses. Further, a framework to track the size and shape of a target is introduced. The method is based on measurement generating points on the surface of the target, which are modeled by an RFS.

Finally, an efficient and novel Bayesian method is proposed for approximating the tire radii of a vehicle based on particle filters and the marginalization concept. This is done under the assumption that a change in the tire radius is caused by a change in tire pressure, thus obtaining an indirect tire pressure monitoring system.

The approaches presented in this thesis have all been evaluated on real data from both freeways and rural roads in Sweden.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2011. s. 93
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1409
Nyckelord
Kalman filter, PHD filter, extended targets, tracking, sensor fusion, road model, single track model, bicycle model
Nationell ämneskategori
Signalbehandling
Identifikatorer
urn:nbn:se:liu:diva-71594 (URN)978-91-7393-023-9 (ISBN)
Disputation
2011-11-25, Key 1, Hus Key, Campus Valla, Linköpings universitet, Linköping, 13:15 (Engelska)
Opponent
Handledare
Projekt
SEFS -- IVSSVR - ETT
Tillgänglig från: 2011-10-26 Skapad: 2011-10-24 Senast uppdaterad: 2019-12-19Bibliografiskt granskad

Open Access i DiVA

fulltext(292 kB)707 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 292 kBChecksumma SHA-512
ae4dbfd91442949b66b06146e9ec4e196122dd78ebe4a9b0729f206f2d04d19809d0c4059fa77481da4d39c81feb214f1328f7311d642c454405cb22ede8833f
Typ fulltextMimetyp application/pdf

Personposter BETA

Lundquist, ChristianGranström, KarlOrguner, Umut

Sök vidare i DiVA

Av författaren/redaktören
Lundquist, ChristianGranström, KarlOrguner, Umut
Av organisationen
ReglerteknikTekniska högskolan
SignalbehandlingReglerteknik

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 707 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

isbn
urn-nbn

Altmetricpoäng

isbn
urn-nbn
Totalt: 306 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf