Classification of range profiles
First Claim
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.
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Abstract
A method and apparatus are provided for classifying range profiles, generated for example by a radar, lidar or sonar. In the method, each in a set of objects of interest is modeled with a probabilistic model. The probabilistic model represents the probabilities of occurrence of different possible sequences of distances between selected features of the object, in different orientations, that are likely to result in peaks of backscatter in a range profile of the object. The probabilistic model is derived from a first probabilistic representation of each selected feature, generated to represent the uncertainty in locating the feature and the uncertainty in observing the feature in a range profile. Classification is achieved by calculating, for each probabilistic model, the probability that the model would generate a given sequence of distances between observed backscatter events in a given range profile. The model generating the given sequence with the greatest probability identifies the object likely to have produced the given range profile. Preferably, the probabilistic models comprise Hidden Markov Models (HMMs).
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Citations
20 Claims
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1. A method for classifying range profiles, comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A non-transitory computer program product encoded with instructions that, when executed by one or more processors, causes a process to be carried out, the process comprising:
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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. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20)
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Specification