1. A method for classifying range profiles, comprising: gathering non-sensor based sources of information giving structural details of objects of interest; selecting, from the non-sensor based sources of information, features of the objects of interest that are configured to appear most prominent as peaks of backscatter in a sensor based observation of the objects of interest; generating, for each of the objects of interest, a probabilistic model representing, for one or more different orientations of the respective object of interest, possible sequences of distances between the features of the respective object of interest selected from the non-sensor based sources of information that are configured to appear as distinct peaks of backscatter in sensor based range data for the object, wherein the possible sequences of distances are derived from a first probabilistic representation of each of the features of the respective object of interest; classifying a given sensor based range profile by deriving an observed sequence of distances from the spacing of distinct peaks of backscatter in the given sensor based range profile and by calculating, for each of the probabilistic models, a probability that the respective probabilistic model represents the observed sequence of distances, wherein the object of interest represented by the probabilistic model that represents the observed sequence of distances with the greatest probability is associated with the given sensor based range profile; and generating classification results for at least one of the probabilistic models so as to predict the potential performance of a classifier.
1. A method for classifying range profiles, comprising: gathering non-sensor based sources of information giving structural details of objects of interest; selecting, from the non-sensor based sources of information, features of the objects of interest that are configured to appear most prominent as peaks of backscatter in a sensor based observation of the objects of interest; generating, for each of the objects of interest, a probabilistic model representing, for one or more different orientations of the respective object of interest, possible sequences of distances between the features of the respective object of interest selected from the non-sensor based sources of information that are configured to appear as distinct peaks of backscatter in sensor based range data for the object, wherein the possible sequences of distances are derived from a first probabilistic representation of each of the features of the respective object of interest; classifying a given sensor based range profile by deriving an observed sequence of distances from the spacing of distinct peaks of backscatter in the given sensor based range profile and by calculating, for each of the probabilistic models, a probability that the respective probabilistic model represents the observed sequence of distances, wherein the object of interest represented by the probabilistic model that represents the observed sequence of distances with the greatest probability is associated with the given sensor based range profile; and generating classification results for at least one of the probabilistic models so as to predict the potential performance of a classifier. 8. The method according to claim 1 , wherein the non-sensor based sources of information include at least one of: engineering drawings, photographs of the objects of interest, and scale models of the objects of interest.