Earlier papers address the latter problems, but dropouts remain a problem in many environments.
We present an approach that lifts the overoptimistic independence assumption used in earlier work, and use Bayes nets to represent the dependencies between objects of the model. Simulation supports the method's feasibility. The model is readily extensible to allow for prior distributions, as well as other types of sensing operations. Sensor fusion, sonar-based mapping, probabilistic reasoning, Bayes nets.