Gaussian mixtures (GM) provide a flexible and numerically robust means for the treatment of nonlinearities as well as for the integration of context knowledge into target tracking algorithms. Contextual information lead to constraints on the target state which can be incorporated in the time prediction step of a tracking filter (model of the target dynamics) as well as in the measurement update step in terms of a constraint likelihood function. In this paper, we present examples for each possibility: road-map assisted target tracking and integration of terrain map data for target localization. The algorithms are applied to the problem of airborne passive emitter localization and demonstrate enhanced tracking and localization precision for moving and for stationary ground based emitters.