A Kalman-Particle Filter for Estimating the Number and State of Multiple Targets

The problem of estimating the number and state of multiple targets using a sensor with limited sensing ability is raised in a variety of applications, including monitoring of endangered species, civilian security, and military surveillance. The particle filter is widely used to solve this problem since Kalman filter’s disadvantage on estimating non-Gaussian distribution. However, the problem becomes intractable when the number of total targets are unknown and one measurement is associated serval targets. This paper presents a novel filter technique which 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 instead of a point mass in the system state.