CLASSIFICATION OF RANGE PROFILES
First Claim
1. A method for classifying range profiles, comprising:
- (i) generating, for each in a set of objects of interest, a probabilistic model representing, for one or more different orientations of the object, possible sequences of distances between features of the object selected as being likely to result in distinct peaks in range data for the object, wherein the possible sequences of distances are derived from a first probabilistic representation of each said selected feature; and
(ii) classifying a given range profile by deriving an observed sequence of distances from the spacing of distinct peaks in the given range profile and by calculating, for each of one or more probabilistic models generated at step (i), the probability that the model generates the observed sequence of distances, the object whose model generates the observed sequence with the greatest probability being associated with the given range profile.
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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 modelled 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
15 Claims
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1. A method for classifying range profiles, comprising:
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(i) generating, for each in a set of objects of interest, a probabilistic model representing, for one or more different orientations of the object, possible sequences of distances between features of the object selected as being likely to result in distinct peaks in range data for the object, wherein the possible sequences of distances are derived from a first probabilistic representation of each said selected feature; and (ii) classifying a given range profile by deriving an observed sequence of distances from the spacing of distinct peaks in the given range profile and by calculating, for each of one or more probabilistic models generated at step (i), the probability that the model generates the observed sequence of distances, the object whose model generates the observed sequence with the greatest probability being associated with the given range profile. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 14, 15)
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9. An apparatus for classifying range profiles, comprising:
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an input for receiving a range profile to be classified; a store for storing, for each of one or more objects of interest, a probabilistic model derived from a first probabilistic representation of features of the object selected as being likely to result in distinct peaks in range data for the object; and classifying means for deriving a sequence of distances from the spacing of distinct peaks in a received range profile, for calculating, for the received range profile and for each of one or more probabilistic models stored in the store, the probability that the stored probabilistic model generates the derived sequence of distances, and for associating the received range profile with an object from said one or more objects of interest whose probabilistic model generates the derived sequence with the highest probability. - View Dependent Claims (10, 11, 12, 13)
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Specification