AOA target tracking with new IMM PF algorithm

Dah Chung Chang, Meng Wei Fan

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

1 Scopus citations

Abstract

The state estimation technique based on the Kalman filter (KF) is widely used in many communication applications. The KF is only optimal for linear modeling with independent and identically distributed (i.i.d.) random variables and Gaussian noises. In some complicated problems, the system model is not unique and the measurement equation is nonlinear. The particle filter (PF) along with interacting multiple models (IMM) becomes an attractive solution. In this paper, a new particle filtering method based on IMM algorithm is proposed. The new IMMPF algorithm is developed for an angle-of-arrival (AOA) tracking problem with bearings-only measurements. Simulation results show that the IMMPF algorithm outperforms the IMM extended KF algorithm and achieves a root mean square tracking performance which is quite close to the posterior Cramer-Rao lower bound (CRLB).

Original languageEnglish
Title of host publication2014 IEEE 57th International Midwest Symposium on Circuits and Systems, MWSCAS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages729-732
Number of pages4
ISBN (Electronic)9781479941346, 9781479941346
DOIs
StatePublished - 23 Sep 2014
Event2014 IEEE 57th International Midwest Symposium on Circuits and Systems, MWSCAS 2014 - College Station, United States
Duration: 3 Aug 20146 Aug 2014

Publication series

NameMidwest Symposium on Circuits and Systems
ISSN (Print)1548-3746

Conference

Conference2014 IEEE 57th International Midwest Symposium on Circuits and Systems, MWSCAS 2014
Country/TerritoryUnited States
CityCollege Station
Period3/08/146/08/14

Keywords

  • IMM
  • Kalman filtering
  • particle filtering
  • posterior CRLB
  • resampling
  • State estimation

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