Recursive expectation-maximization (EM) algorithms for time-varying parameters with applications to multiple target tracking

We investigate the application of expectation-maximization (EM) algorithms to the classical problem of multiple target tracking (MTT) for a known number of targets. Conventional algorithms, which deal with this problem, have a computational complexity that depends exponentially on the number of targets, and usually divide the problem into a localization stage and a tracking stage, The new algorithms achieve a linear dependency and integrate these two stages. Three optimization criteria are proposed, using deterministic and stochastic dynamic models for the targets.