@InProceedings{Supelec695,
author = {RĂ©mi Chou and Yvo Boers and Martin Podt and Matthieu Geist},
title = {{Performance Evaluation for Particle Filters}},
year = {2011},
booktitle = {{14th International Conference on Information Fusion (FUSION 2011)}},
publisher = {IEEE},
pages = {7 pages},
address = {Chicago, USA},
url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp\'earnumber=5977602},
abstract = {Performance evaluation in particle filtering problems is commonly
performed via point estimator comparison. However, in
non-Gaussian cases, this can be not always meaningful and entire
particle clouds need to be compared. The Kullback- Leibler
divergence (KLD) can be used for such a particle cloud
comparison. In contrast to KLD estimates commonly used in
particle filtering applications, we present an estimator of the
KLD being applicable to any cloud of particles. This estimator is
applied to a performance evaluation scheme generally relevant to
any particle filter, of which abilities are equal to no other
known scheme in the literature. Through simulations and concrete
examples, we will show that it is suitable to practically compare
particle clouds, which have a limited number of particles, have a
different size, are close to each other and have an high
dimensionality.}
}