USA As Represented by Secretary of the Navy

Other

Roy L Streit

Tod E Luginbuhl

Foster L Striffler

Marcus L Graham

Stephen G Greineder

Michael J Walsh

Tod E Luginbuhi

Michael L Rosseau

In the present invention, the histogram model used in H-PMHT is extended to treat the problem of tracking using hyper-spectral data. Completely general spectral density functions are handled via the use of non-parametric methods. The present invention is not restricted to derivations based on knowledge of the spectral character of the source being tracked. The source spectrum can be estimated in a non-parametric fashion based on an initial track, and this allows the invention to adapt to the source spectrum in situ. The resulting method has improved crossing track performance on sources that have some degree of spectral distinction and will perform no worse than regular H-PMHT on sources that have identical spectral densities.

Method for tracking targets with hyper-spectral data

A system and method for ranking features by exploiting their relationship the Fisher projection space. The system ranks n features in a feature set using a design set comprising exemplars from each of M possible event classes of an associated feature-based classification system. A training set is created by randomly selecting exemplars from each of the M classes in the design set. A 'smoothed' Fisher projection space for the training set is created by replacing the sample means and the within-class sample covariance matrix normally used in deriving a Fisher projection space with expressions for the mean vectors and covariance matrices derived from event class probability density function estimates. The angle between a given feature and the smoothed Fisher projection space is calculated for each feature in the feature set, and the features are then ordered by increasing numerical size of this angle. The system produces a reduced feature set by eliminating those features which are not important for classification based on the linear ranking of the features.

System and method for feature set reduction

A neural network for classifying input vectors to an outcome class under the assumption that the classes are characterized by mixtures of component populations having a multivariate Gaussian likelihood distribution. The neural network comprises an input layer for receiving components of an input vector, two hidden layers for generating a number of outcome class component values, and an output layer. The first hidden layer includes a number of first layer nodes each connected receive input vector components and generate a first layer output value representing the absolute value of the sum of a function of the difference between each input vector component and a threshold value. The second hidden layer includes a plurality of second layer nodes, each second layer node being connected to the first layer nodes and generating an outcome class component value representing a function related to the exponential of the negative square of a function of the sum of the first layer output values times a weighting value. The output layer includes a plurality of output nodes, each associated with an outcome class, for generating a value that represents the likelihood that the input vector belongs to that outcome class.

Neural network architecture for gaussian components of a mixture density

A neural network comprising an input layer, two hidden layers for generating an number of outcome class component values, and an output layer for classifying input vectors to an outcome class, under the assumption that the outcome classes are characterized by mixtures of component populations with each component population having a multivariate Gaussian likelihood distribution. The first hidden layer includes a number of first layer nodes each connected receive input vector components from the input layer and generates in response a first layer output value representing the absolute value of the sum of a function of the difference between each input vector component and a threshold value. The second hidden layer includes a plurality of second layer nodes each for generating an outcome class component value, each second layer node being connected to predetermined ones of the first layer nodes and generating in response to the first layer output values an outcome class component value representing a function related to the exponential of the negative square of the sum of first layer output values connected thereto. The output layer includes a plurality of output layer nodes each associated with an outcome class. Each output layer node uses the output class component values from the second layer nodes in combination with weighting values to generate the likelihood that the input vector is properly classified to the output layer node's outcome class.

Neural network for maximum likelihood classification with supervised and

A neural network for classifying input vectors to an outcome class, under the assumption that the outcome classes are characterized by mixtures of component populations, with each component population having a multivariate non-Gaussian likelihood distribution. The neural network comprising an input layer for receiving the components of the input vector, two hidden layers for generating an number of outcome class component values, and an output layer that identifies an outcome class in response to the outcome class component values. The first hidden layer includes a number of first layer nodes each connected receive input vector components from the input layer and generating in response a first layer output value representing a selected first layer power of the absolute value of the sum of the difference between a function of each input vector component and a threshold value. The second hidden layer includes a plurality of second layer nodes each being connected to predetermined ones of the first layer nodes and generating in response to the first layer output values an outcome class component value representing a function related to the exponential of the negative square of the sum of first layer output values. The output layer includes a plurality of output layer nodes each associated with an outcome class. Each output layer node receives the output class component values from all of the second layer nodes and uses them, in combination with respective weighting values, to generate a value that represents the likelihood that the input vector is properly classified to the output layer node's outcome class.

Neural network architecture for non-Gaussian components of a mixture

A method for training a speech recognizer in a speech recognition system is described. The method of the present invention comprises the steps of providing a data base containing acoustic speech units, generating a homoscedastic hidden Markov model from the acoustic speech units in the data base, and loading the homoscedastic hidden Markov model into the speech recognizer. The hidden Markov model loaded into the speech recognizer has a single covariance matrix which represents the tied covariance matrix of every Gaussian probability density function PDF for every state of every hidden Markov model structure in the homoscedastic hidden Markov model.

Training of homoscedastic hidden Markov models for automatic speech

Crosstalk contamination of signals in a multichannel data transmission sym is designated by a matrix which has unit values for the diagonal and small values for the off-diagonal terms. If this matrix satisfies the criteria for finding an inverse thereof, the matrix may be inverted and the results are used to find the values for the correction matrix electronic circuits. The correction electronic matrix circuits with their respective inputs from the telemetry receiver give their summed outputs for each channel to be the crosstalk contamination-free signals for various channels.

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Portsmouth, RI, United States of America

Roy L Streit