Efficient calculation of information reduction factor for tracking in clutter

A reformulation of the information reduction factor (IRF) for tracking in the presence of measurement uncertainty is presented. A simple integral representation is employed to derive an expression for the IRF that can be calculated without approximation in terms of one-dimensional integrals. This significantly reduces the computational requirements of the calculation and is relevant to the offline calculation of tracking performance and the selection of detection thresholds to optimise tracking accuracy. A comparison with Monte Carlo integration shows that the new technique is approximately 380 times faster.