A Gaussian Particle Filter Approach for Sensors to Track Multiple Moving Targets
This addresses a critical challenge in applications like surveillance and monitoring, though it appears incremental as it builds on existing particle filter methods.
The paper tackles the problem of estimating the number and state of multiple moving targets from sensor measurements when the number of targets and measurement-target associations are unknown, by introducing a Gaussian particle filter that combines Kalman and particle filters, achieving tractable estimation represented by weighted Gaussian particles.
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.