Variance estimation and ranking of Gaussian mixture distributions in target tracking applications

Variance estimation and ranking methods are developed for stochastic processes modeled by Gaussian mixture distributions. It is shown that the variance estimate from a Gaussian mixture distribution has the same properties as a variance estimate from a single Gaussian distribution based on a reduced number of samples. Hence, well known tools of variance estimation and ranking of single Gaussian distributions can be applied to Gaussian mixture distributions. As an application example, optimization of sensor processing order in the sequential multi-target multi-sensor joint probabilistic data association algorithm is presented.