Set-membership PHD filter
The paper proposes a novel Probability Hypothesis
Density (PHD) filter for linear system in which initial state,
process and measurement noises are only known to be bounded
(they can vary on compact sets, e.g., polytopes). This means that
no probabilistic assumption is imposed on the distributions of
initial state and noises besides the knowledge of their supports.
These are the same assumptions that are used in set-membership
estimation. By exploiting a formulation of set-membership estimation
in terms of set of probability measures, we derive the
equations of the set-membership PHD filter, which consist in
propagating in time compact sets that include with guarantee
the targets’ states. Numerical simulations show the effectiveness
of the proposed approach and the comparison with a sequential
Monte Carlo PHD filter which instead assumes that initial state
and noises have uniform distributions.
PAPI Francesco;
BENAVOLI Alessio;
2014-11-17
IEEE
JRC84768
978-605-86311-1-3,
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6641211,
https://publications.jrc.ec.europa.eu/repository/handle/JRC84768,
Additional supporting files
| File name | Description | File type | |
|---|
