A Unified View of State Estimation in Switching Environments

In many practical situations, dynamic systems are subjected to abrupt structural and parametric changes at random instants of time. A general mathematical framework is presented for classifying the existing state estimation and hypothesis-testing problems arising in systems subjected to random structural and parametric disturbances. The mathematical approach is based on an event-driven, linear stochastic system model comprising a hybrid (i.e., continuous and discrete) state space. It is shown that the problems of multitarget tracking in surveillance theory, Markov chain-driven systems, estimation under uncertain observations, maneuvering target tracking and system failure detection are special cases of this general formulation.