A Mahalanobis Distance for Multi-Target Tracking

We propose the use of a novel Mahalanobis distance for comparing point processes in the context of multi-target tracking and sensor fusion. In drawing on methods analogous to free field theory, we derive analytic expressions for key models such as the Poisson and Bernoulli point processes. Moreover, we present efficient Monte Carlo integration schemes that enable practical computation in cases where the point processes are represented by Gaussian mixtures. Simulation results confirm that our proposed distance can be applied in practice.