Group Target Tracking with the Gaussian Mixture Probability Hypothesis Density Filter

The probability hypothesis density (PHD) filter was originally devised to address non-conventional tracking problems such as group target processing, tracking in high target density, tracking closely spaced targets and detecting targets of interest in a dense multi-target background. The intention was to track overall group behaviour, and then attempt to track individual targets and then attempt to detect and track individual targets only as the quantity and quality of the data permits. Despite this, most practical implementations of the PHD filter have been applied to standard multi-target tracking problems and there have been few implementations of the PHD filter for tackling groups of targets. In this work, we investigate some practical strategies for group tracking with the Gaussian mixture implementation of the PHD filter.