  In a variety of problems, the number and state of multiple moving targets are
unknown and are subject to be inferred from their measurements obtained by a
sensor with limited sensing ability. This type of problems is raised in a
variety of applications, including monitoring of endangered species, cleaning,
and surveillance. Particle filters are widely used to estimate target state
from its prior information and its measurements that recently become available,
especially for the cases when the measurement model and the prior distribution
of state of interest are non-Gaussian. However, the problem of estimating
number of total targets and their state becomes intractable when the number of
total targets and the measurement-target association are unknown. This paper
presents a novel Gaussian particle filter technique that combines Kalman filter
and particle filter for estimating the number and state of total targets based
on the measurement obtained online. The estimation is represented by a set of
weighted particles, different from classical particle filter, where each
particle is a Gaussian distribution instead of a point mass.
