CLASSIFICATION OF SUBSURFACE OBJECTS USING SINGULAR VALUES DERIVED FROM SIGNAL FRAMES
First Claim
1. A method in a computing device for classifying objects, the method comprising:
- providing a classifier that identifies a class for an object, the classifier inputting a feature vector representing the object and outputting an indication of the class for the object;
receiving a signal frame for a detected object, the signal frame representing return signals acquired by receivers based on signals emitted by transmitters operating in multistatic mode, the signal frame including, for each transmitter and receiver pair, a plurality of samples acquired by the receiver at different sampling times for the signal emitted by the transmitter;
transforming the samples of the return signal for each transmitter and receiver pair to a return spectrum in the frequency domain;
generating singular values from the samples of the return spectrum of each transmitter and receiver pair;
generating a feature vector representing the detected object from the generated singular values; and
applying the classifier to the generated feature vector to identify the class of the detected object.
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Accused Products
Abstract
The classification system represents a detected object with a feature vector derived from the return signals acquired by an array of N transceivers operating in multistatic mode. The classification system generates the feature vector by transforming the real-valued return signals into complex-valued spectra, using, for example, a Fast Fourier Transform. The classification system then generates a feature vector of singular values for each user-designated spectral sub-band by applying a singular value decomposition (SVD) to the N×N square complex-valued matrix formed from sub-band samples associated with all possible transmitter-receiver pairs. The resulting feature vector of singular values may be transformed into a feature vector of singular value likelihoods and then subjected to a multi-category linear or neural network classifier for object classification.
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Citations
17 Claims
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1. A method in a computing device for classifying objects, the method comprising:
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providing a classifier that identifies a class for an object, the classifier inputting a feature vector representing the object and outputting an indication of the class for the object; receiving a signal frame for a detected object, the signal frame representing return signals acquired by receivers based on signals emitted by transmitters operating in multistatic mode, the signal frame including, for each transmitter and receiver pair, a plurality of samples acquired by the receiver at different sampling times for the signal emitted by the transmitter; transforming the samples of the return signal for each transmitter and receiver pair to a return spectrum in the frequency domain; generating singular values from the samples of the return spectrum of each transmitter and receiver pair; generating a feature vector representing the detected object from the generated singular values; and applying the classifier to the generated feature vector to identify the class of the detected object. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer-readable storage medium containing computer-executable instructions for controlling a computer to generate a classifier for classifying objects detected below a surface, by a method comprising:
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providing training data that includes, for each of a plurality of training objects, a signal frame and a class for that training object, each signal frame representing return signals acquired by receivers based on signals emitted by transmitters in multistatic mode, each signal frame including, for each transmitter and receiver pair, a plurality of samples acquired by the receiver at different sampling times for the signal emitted by the transmitter; for each training object, generating a feature vector for that training object from the signal frame for that training object by; transforming the return signal samples of each transmitter and receiver pair to return spectral samples in the frequency domain; and generating singular values for the return spectral samples of each transmitter and receiver pair; and training the classifier based on the generated feature vectors and classes of the training objects. - View Dependent Claims (9, 10, 11, 12, 13)
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14. A computing device for classifying objects, the computing device comprising:
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a component that receives a signal frame for a detected object, the signal frame representing return signals acquired by receivers based on signals emitted by transmitters in multistatic mode, the signal frame including, for each transmitter and receiver pair, a plurality of samples acquired by the receiver at different sampling times for the signal emitted by the transmitter; a component that generates a feature vector for the detected object by transforming the return signal samples of each transmitter and receiver pair to return spectral samples in the frequency domain and generates singular values for the return spectral samples of each transmitter and receiver pair; and a component that applies a classifier to the generated feature vector to identify the class of the detected object. - View Dependent Claims (15, 16, 17)
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Specification