Raytheon Company

Hughes Aircraft Company

David M Doria

Yang Chen

Kurt Reiser

Robert T Frankot

Allen Gee

David W Webster

Charles McNary

A fusion method, implemented by one or more processors, for classifying a target having class types. The method includes: obtaining evidence from one or more classifiers, the evidence represented by scores from the one or more classifiers; representing the obtained evidence in a Bayesian context, where the Bayesian beliefs are obtained from the scores; obtaining new evidence, the new evidence represented by new scores from the classifiers; representing the obtained new evidence in an enhanced Bayesian context, where the enhanced Bayesian beliefs are obtained from the new scores; combining the scores and the new scores over multiple times; combining the evidence and the new evidence over multiple times; and using the combined scores and the combined evidence for each of the plurality of class types to classify the target.

System and method for scaled multinomial-dirichlet bayesian evidence fusion

A target correlation matrix is generated for multiple two-class combinations of target types each having a target correlation and a synthetic aperture radar observation space. A target probability density of a target radar cross-section signature and a background probability density of a background radar cross-section signature are utilized. The observation space of each of the two-class combinations is partitioned into a target partition and at least one background partition in accordance with the target correlation. A conditional log likelihood is calculated using at least one random number for each of the partitions in accordance with the target probability density and the background probability density, and summed according to the two-class combinations. A maximum log likelihood is calculated from the summed conditional log likelihoods given that one target type of the multiple two-class combinations is assumed to be true. An automatic target recognition performance prediction based on the maximum log likelihood is generated.

Performance model for synthetic aperture radar automatic target recognition and method thereof

A method and system of estimating the performance of a classifier system based on a reported confusion matrix includes, in one embodiment, parameters fit to observed confusion matrices, such that the expected performance of decision detection versus the probability of not-in-library reports can be estimated based on the forced decision confusion matrix. The approach also lends itself to a general methodology for modeling classes of confusers in a statistical manner, which can be extended to modeling clutter severity.

Empirical modeling of confusion matrices

A method of predicting a target type in a set of target types from at least one image is provided. At least one image is obtained. A first and second set of confidence values and associated azimuth angles are determined for each target type in the set of target types from the at least one image. The first and second set of confidence values are fused for each of the azimuth angles to produce a fused curve for each target type in the set of target types. When multiple images are obtained, first and second set of possible detections are compiled corresponding to regions of interest in the multiple images. The possible detections are associated by regions of interest. The fused curves are produced for every region of interest. In the embodiments, the target type is predicted from the set of target types based on criteria concerning the fused curve.

Fusion for automated target recognition

A hierarchical object recognition method for aggregation, interpretation and classification of information from multiple sensor sources on the detection feature attribute level. The system extracts information derived from each sensor source to obtain detections and their feature attributes. At least two processing streams, one for each sensor source, are provided for converting the detections and their feature attributes into hypotheses on identity and class of detected objects. The detections are shared and combined between the two processing streams using hierarchical information fusion algorithms to determine which ones of the hypotheses on identity and class of detected objects have sufficient probabilities for classifying the information.

Hierarchical information fusion object recognition system and method

Disclosed are a system and method for determining the pose (translation, rotation, and scale), or position and orientation, of a model object that best matches a target object located in image data. Through an iterative process small adjustments are made to the original position and orientation of the model object until it converges to a state that best matches the target object contained in the image data. Edge data representative of edges of the target object and edge data representative of the model object are processed for each data point in the model object relative to each point in the target object to produce a set of minimum distance vectors between the model object and the target object. A neural network estimates translation, rotation, and scaling adjustments that are to be made to the model object. Pose of the model object is adjusted relative to the target object based upon the estimated translation, rotation, and scaling adjustments provided by the neural network. Iterative calculation of the minimum distance vectors, estimation of the translation, rotation, and scaling adjustments, and adjustment of the position and orientation of the model object is adapted to reposition the model object until it substantially overlays the target object. Final position of the model object provides an estimate of the position and orientation of the target object in the digitized image.

Growing community of inventors

Lakewood, CA, United States of America

David M Doria