@inproceedings{a7d1a28e83a4435b8a9c943d29e4511e,
title = "Particle filtering for systems with unknown noise probability distributions",
abstract = "In recent years particle filtering has become a powerful tool for tracking signals and time-varying parameters of dynamical systems. These methods require a mathematical representation of the dynamics of the system evolution, together with assumptions of probabilistic models. In this paper, a new class of particle filtering methods that do not assume an explicit mathematical form of the probability distributions of the noise in the system is presented. As a consequence, the proposed techniques are more robust than standard particle filters. Besides the theoretical development of a specific method in the new class, experimental results that demonstrate its performance in the problem of target tracking are provided.",
keywords = "Cost function, Distributed computing, Filtering, Monte Carlo methods, Particle filters, Power engineering and energy, Power engineering computing, Signal processing algorithms, State estimation, Target tracking",
author = "J. M{\'i}guez and Shanshan Xu and Bugallo, \{M. F.\} and Djuri{\'c}, \{P. M.\}",
note = "Publisher Copyright: {\textcopyright} 2003 IEEE.; IEEE Workshop on Statistical Signal Processing, SSP 2003 ; Conference date: 28-09-2003 Through 01-10-2003",
year = "2003",
doi = "10.1109/SSP.2003.1289505",
language = "English",
series = "IEEE Workshop on Statistical Signal Processing Proceedings",
publisher = "IEEE Computer Society",
pages = "522--525",
booktitle = "Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003",
}