Mixture reduction techniques and probabilistic intensity models for multiple hypothesis tracking of targets in clutter

• Two Gaussian mixture reduction algorithms (MRAs) for a Multiple Hypothesis Tracker (MHT) are described.
• The preferred MRA yields a mixture with no more than two components.
• Both algorithms outperform Probabilistic Data Association (PDA) and an alternative MHT MRA described in the literature.
• A scheme for incorporating intensity information into a Bayesian tracker in an infrared (IR) sensor is also presented.

A linear combination of Gaussian components, i.e. a Gaussian ‘mixture’, is used to represent the target probability density function (pdf) in Multiple Hypothesis Tracking (MHT) systems. The complexity of MHT is typically managed by ‘reducing’ the number of mixture components. Two complementary MHT mixture reduction algorithms are proposed and assessed using a simulation involving a cluttered infrared (IR) seeker scene. A simple means of incorporating intensity information is also derived and used by both methods to provide well balanced peak-to-track association weights. The first algorithm (MHT-2) uses the Integral Squared Error (ISE) criterion, evaluated over the entire posterior MHT pdf, in a guided optimization procedure, to quickly fit at most two components. The second algorithm (MHT-PE) uses many more components and a simple strategy, involving Pruning and Elimination of replicas, to maximize hypothesis diversity while keeping computational complexity under control.