Comparison of three Kalman filters for an indoor passive tracking system

Shuo Shen, Chen Xia, Robert Sprick, Lance C. Pérez, Steve Goddard

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Wireless sensor networks can be used for the localization and tracking of moving targets. However, the range measurements are noisy and Kalman filters are frequently used to improve the tracking accuracy. Three different tracking algorithms, namely, a Standard Kalman Filter (SKF), an Extended Kalman Filter (EKF), and a Modified Kalman Filter (MKF) [1] are empirically studied in terms of accuracy and latency for a range-based indoor tracking system. The experimental results show that the filtering techniques improve the tracking accuracy when the target is moving rapidly. However, different forms of Kalman filters introduce different levels of latency which affects the real-time tracking performance.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Electro/Information Technology, EIT 2007
Pages284-289
Number of pages6
DOIs
StatePublished - 2007
Event2007 IEEE International Conference on Electro/Information Technology, EIT 2007 - Chicago, IL, United States
Duration: May 17 2007May 20 2007

Publication series

Name2007 IEEE International Conference on Electro/Information Technology, EIT 2007

Other

Other2007 IEEE International Conference on Electro/Information Technology, EIT 2007
CountryUnited States
CityChicago, IL
Period5/17/075/20/07

ASJC Scopus subject areas

  • Computer Science(all)
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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