A multisensor data fusion-based target tracking system

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

In this paper a multi sensor data fusion-based target tracking system is presented. The system includes neurofuzzy multisensor data fusion (MSDF) in order to overcome the limitation of the use of a single sensor. It has the capability of minimising the noise that contaminates the sensor measurements and excludes the faulty (invalid) measurements from use in the estimation process. Despite being a simple algorithm, it can deal with the data fusion problem using noisy non-linear sensors as well as linear sensors. A neuro-tuzzy kinematics process model is also employed in this target tracking system to cope with the lack of a priori knowledge of the target dynamics. Although no a priori statistical knowledge of the target dynamics and the sensors are involved in the estimation process, the performance of the proposed system is comparable with the Extended Kalman Filter-based target tracking system which uses the exactly known process model of the target.

Original languageEnglish
Title of host publicationIEEE ICIT 2002 - 2002 IEEE International Conference on Industrial Technology
Subtitle of host publication"Productivity Reincarnation Through Robotics and Automation"
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages427-432
Number of pages6
ISBN (Electronic)0780376579
DOIs
Publication statusPublished - 1 Jan 2002
EventIEEE International Conference on Industrial Technology, IEEE ICIT 2002 - Bangkok, Thailand
Duration: 11 Dec 200214 Dec 2002

Publication series

NameProceedings of the IEEE International Conference on Industrial Technology
Volume1

Conference

ConferenceIEEE International Conference on Industrial Technology, IEEE ICIT 2002
Country/TerritoryThailand
CityBangkok
Period11/12/0214/12/02

Keywords

  • Kalman filter
  • Multisensor data fusion
  • Neuro-fuzzy methods
  • Target tracking

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