Interacting multiple model particle filtering using new particle resampling algorithm

Dah Chung Chang, Meng Wei Fan

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


The state estimation technique based on the Kaiman 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 resampling method is proposed for the PF to alleviate the degeneracy effect of particle propagation. 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
Article number7037301
Pages (from-to)3215-3219
Number of pages5
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
StatePublished - 2014
Event2014 IEEE Global Communications Conference, GLOBECOM 2014 - Austin, United States
Duration: 8 Dec 201412 Dec 2014


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


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