Pattern classifier and method for associating tracks from different sensors
First Claim
1. An interceptor-based sensor comprising:
- a track clustering element to cluster tracks of objects to generate track clusters based on an uncertainty associated with each track;
a feature generating element to generate feature vectors for the track clusters in one or more directions with respect to a cluster under test, the feature vectors comprising one or more of a cluster count feature vector (N), a cluster population density feature vector (P), a cluster proximity feature vector (r), a cluster-weighted centroid feature vector (L) and a cluster scattering feature vector (θ
); and
a track selection element to associate tracks provided by another sensor based on belief functions generated from the feature vectors, the track selection element to select one of the tracks corresponding to a track of interest.
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Abstract
An interceptor-based sensor clusters tracks of objects to generate track clusters based on an uncertainty associated with each track, and generates feature vectors for a cluster under test using the relative placement and the population of other track clusters. The feature vectors may include one or more of a cluster count feature vector (N), a cluster population density feature vector (P), a cluster proximity feature vector (r), a cluster-weighted centroid feature vector (L) and a cluster scattering feature vector (θ). The interceptor-based sensor generates belief functions (μ) from corresponding feature vectors of clusters of tracks generated from a ground-based sensor and the interceptor-based sensor. The interceptor-based sensor may also associate the tracks with a cluster having a track of interest identified by a ground-based sensor based on the belief functions and may select one of the tracks for intercept of a corresponding object within the threat object cloud.
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Citations
32 Claims
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1. An interceptor-based sensor comprising:
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a track clustering element to cluster tracks of objects to generate track clusters based on an uncertainty associated with each track; a feature generating element to generate feature vectors for the track clusters in one or more directions with respect to a cluster under test, the feature vectors comprising one or more of a cluster count feature vector (N), a cluster population density feature vector (P), a cluster proximity feature vector (r), a cluster-weighted centroid feature vector (L) and a cluster scattering feature vector (θ
); anda track selection element to associate tracks provided by another sensor based on belief functions generated from the feature vectors, the track selection element to select one of the tracks corresponding to a track of interest. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method of associating tracks from different sensors comprising:
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clustering tracks of objects to generate track clusters based on an uncertainty associated with each track; generating feature vectors for the track clusters in one or more directions with respect to a cluster under test, the feature vectors comprising one or more of a cluster count feature vector (N), a cluster population density feature vector (P), a cluster proximity feature vector (r), a cluster-weighted centroid feature vector (L) and a cluster scattering feature vector (θ
);associating tracks provided by another sensor based on belief functions generated from the feature vectors; and selecting one of the tracks corresponding to a track of interest. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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25. A pattern classifier comprising:
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a track clustering element to cluster tracks of objects provided by a first sensor to generate track clusters based on an uncertainty associated with each track; a feature generating element to generate feature vectors for the track clusters in one or more directions with respect to a cluster under test, the feature vectors comprising one or more of a cluster count feature vector (N), a cluster population density feature vector (P), a cluster proximity feature vector (r), a cluster-weighted centroid feature vector (L) and a cluster scattering feature vector (θ
); anda track selection element to associate tracks provided by a second sensor (204) based on belief functions generated from the feature vectors, the track selection element to select one of the tracks corresponding to a track of interest. - View Dependent Claims (26, 27)
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28. A missile-defense system comprising:
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a ground-based sensor to acquire a threat cloud comprising a missile from a track-state estimate and covariance provided by an overhead sensor; and an interceptor to receive track-state vectors of objects in the threat cloud tracked by the ground-based sensor, the interceptor comprising a track clustering element to cluster tracks of objects to generate track clusters based on an uncertainty associated with each track, a feature generating element to generate feature vectors for the track clusters in one or more directions with respect to a cluster under test, and a track selection element to associate tracks provided by another sensor based on belief functions generated from the feature vectors, the track selection element to select one of the tracks corresponding to a track of interest, wherein the feature vectors comprise one or more of a cluster count feature vector (N), a cluster population density feature vector (P), a cluster proximity feature vector (r), a cluster-weighted centroid feature vector (L) and a cluster scattering feature vector (θ
). - View Dependent Claims (29, 30, 31)
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32. A machine-accessible medium that provides instructions, which when accessed, cause a machine to perform operations comprising:
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clustering tracks of objects to generate track clusters based on an uncertainty associated with each track; generating feature vectors for the track clusters in one or more directions with respect to a cluster under test, the feature vectors comprising one or more of a cluster count feature vector (N), a cluster population density feature vector (P), a cluster proximity feature vector (r), a cluster-weighted centroid feature vector (L) and a cluster scattering feature vector (θ
);associating tracks provided by another sensor based on belief functions generated from the feature vectors; and selecting one of the tracks corresponding to a track of interest.
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Specification