On Multitarget Track Extraction and Maintenance using Sequential Monte Carlo methods

摘要

This contribution addresses the problem of tracking multiple moving objects simultaneously over time given measurements with false alarms and missing detections. Such a task becomes particularly intricate if the initial target number and targets states are unknown and if the individual targets are not separable. We employ a sequential Bayesian framework based on the Finite Set Statistic approach in order to estimate the number of targets and the target states simultaneously. The iterative filtering equations are solved numerically using Particle Filter techniques. We describe a method for sequential tack extraction of multiple targets without relying on external information and present results for small groups of ground moving targets.