Abstract:

A novel particle filter bandwidth adaption for kernel particle
filter (BAKPF) is proposed. Selection of the kernel bandwidth
is a critical issue in kernel density estimation (KDE). The plug-in
method is adopted to get the global fixed bandwidth by optimizing
the asymptotic mean integrated squared error (AMISE) firstly.
Then, particle-driven bandwidth selection is invoked in the KDE. To
get a more effective allocation of the particles, the KDE with adaptive
bandwidth in the BAKPF is used to approximate the posterior
probability density function (PDF) by moving particles toward the
posterior. A closed-form expression of the true distribution is given.
The simulation results show that the proposed BAKPF performs
better than the standard particle filter (PF), unscented particle filter
(UPF) and the kernel particle filter (KPF) both in efficiency and
estimation precision.