1. A method for estimating the state of an object, the method comprising the steps of: providing a plurality of interacting models, each model representing a potential operating mode of the object and defined by at least one extrinsic parameter constrained to lie within a predetermined range of values; making measurements of at least one aspect of the state of the object with at least one sensor; using at least one processor responsive to the output of the sensor: calculating mixed estimates of the state of the object and their mixed covariances; predicting the mixed estimates and the mixed covariances of each of the plurality of models to the time of the measurement of the at least one aspect of the state of the object; computing filter gains for each of the plurality of models according to the predicted mixed estimates and mixed covariances; processing the measurements of the at least one aspect of the state of the object in each model, yielding a measurement-updated object state and covariance for each model; calculating model probabilities from the measurement-updated state and covariance; and generating an overall estimate of the state and covariance of the object from the model-conditioned updated states, model-conditioned covariances, and model probabilities.