@inproceedings{d24a096f8434421492fe0e0babebe939,
title = "AOA target tracking with new IMM PF algorithm",
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).",
keywords = "IMM, Kalman filtering, particle filtering, posterior CRLB, resampling, State estimation",
author = "Chang, {Dah Chung} and Fan, {Meng Wei}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; null ; Conference date: 03-08-2014 Through 06-08-2014",
year = "2014",
month = sep,
day = "23",
doi = "10.1109/MWSCAS.2014.6908518",
language = "???core.languages.en_GB???",
series = "Midwest Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "729--732",
booktitle = "2014 IEEE 57th International Midwest Symposium on Circuits and Systems, MWSCAS 2014",
}