USA as Represented by Secretary of the Navy

David W Stein

James W Bond

James R Zeidler

Stefen Hui

A computer program product for detecting anomalies from multidimensional data comprises: a) receiving multidimensional data; b) estimating background parameters of a normal compositional model from the multidimensional data; c) estimating abundance values of the normal compositional model from the background parameters and the multidimensional data; d) determining an anomaly detection statistic from the multidimensional data, the background parameters, and abundance values; and e) classifying an observation from the multidimensional data as either a background reference or an anomaly from the anomaly detection statistic.

Method and system for detecting anomalies in multispectral and hyperspectral imagery employing the normal compositional model

A method for detecting anomalies from multidimensional data comprises: a) receiving multidimensional data; b) estimating background parameters of a normal compositional model from the multidimensional data; c) estimating abundance values of the normal compositional model from the background parameters and the multidimensional data; d) determining an anomaly detection statistic from the multidimensional data, the background parameters, and abundance values; and e) classifying an observation from the multidimensional data as either a background reference or an anomaly from the anomaly detection statistic.

A method for detecting targets comprises: a) receiving spectral data; b) using a normal compositional model for estimating background parameters from the spectral data and target components; c) estimating abundance values of classes of the normal compositional model from the background parameters and the spectral data; d) estimating target class covariance values from the spectral data, the background parameters, and the target components; e) estimating target-plus-background abundance values from the target class covariance values, the background parameters, the spectral data, and the target components; f) employing a normal compositional model for determining a likelihood ratio detection statistic from the target class covariance values, target-plus-background abundance values, spectral data, target components, background parameters, and background abundance values; and g) generating a determination output signal that represents whether an observation includes a target from the likelihood ratio detection statistic.

Method and system for detecting targets known up to a simplex from multi-spectral and hyper-spectral imagery employing the normal compositional model

A method is described for detecting weak signals in a non-Gaussian and non-stationary background using a hidden Markov parameter estimator. The method comprises the steps of: a) partitioning input data into sets of range bins, where the input data has a noise component; b) estimating hidden Markov model parameters from the input data; c) determining the average intensity I.sub.i of the range bins, where i represents an index for the ranges bins, and 1.ltoreq.i .ltoreq.R, and R represents the total number of range bins; d) estimating the average noise intensity N.sub.i of each range bin; e) determining the residual intensity estimates .rho..sub.i of each range bin as the maximum of 0 and (I.sub.i -N.sub.i) for all values of i; f) transforming the hidden Markov model parameters into estimates of hidden Markov model parameters of the noise component of the input data; g) generating modified locally optimal detection statistics representing the likelihood that the input data for the range bins contains a signal of interest using the estimates of the hidden Markov model parameters of the noise component of the input data, the residual intensity estimates, and the input data; h) transforming the modified locally optimal detection statistic into normalized values of the detection statistics applied to each range bin; and i) generating a detection output signal if any of the normalized values of the detection statistics exceeds a threshold.

Method for detecting weak signals in a non-gaussian and non-stationary

The frequency domain kernel processor of the present invention comprises a cheduler for selecting samples from blocks of Fourier transform coefficients in a predetermined sequence and for outputting the selected samples as serial terms s and as parallel terms u. A transform processor is coupled to the scheduler to receive the terms s and u. The transform processor comprises transform calculators for calculating output values p and f as functions of terms s and u to model the interference and to reveal the desired signal, a first sum calculator for calculating a sum of p values, a second summing calculator for calculating a sum of f values, and a divider for calculating, the quotient of the sum of p values divided by the sum of f values. An output buffer stores the outputs of the divider for formatting as a spectral display.

Frequency domain kernel processor

A hidden Markov detector of the present invention comprises a hidden Markov arameter estimator for coupling to an input data source, a parameter smoother coupled to the parameter estimator, and a detection statistic calculator coupled to the parameter smoother and the input data source.

Coherent hidden markov model

A frequency domain kernel phase processor comprises an input buffer to st Fourier coefficients input from a Fourier transform of a communications signal. A communications signal suitable for processing by the present invention typically comprises a desired signal masked by a stronger interference signal having varying power that spans a range of frequency bins encompassing the desired signal. A scheduler inputs Fourier transform coefficients from the input buffer and generates a series of magnitude squares and symmetric phase differences for each frequency bin. The magnitude squares and the symmetric phase differences are output to phase transform processors which transform the symmetric phase differences to reveal the desired signal. The transformed outputs from the phase transform processors are output to an output buffer to be formatted and displayed.

Frequency domain kernel phase processor

The invention provides a method for detecting signals in non-Gaussian background clutter, comprising the steps of: 1) filtering input data to partition Gaussian input data from non-Gaussian input data; 2) estimating the intensity level of the Gaussian input data; 3) determining the probability distributions of the non-Gaussian input data; 4) determining the background noise level of the input data; 5) estimating noise parameters of the input data; 6) estimating the residual intensity of the input data using the noise parameters; 7) determining a detection statistic M from the input data, estimated noise parameters, and the residual intensity of the input data; 8) determining a standard deviation N.sub.i of the detection statistic, M.sub.i ; 9) determining a normalized detection statistic Si, where S.sub.i =M.sub.i /N.sub.i,; 10) determining a maximum value, S.sub.imax of S.sub.i ; 11) determining a mean value S.sub.Mean and standard deviation S.sub.Std dev of all S.sub.i excluding S.sub.imax ; 12) determining a threshold .tau..sub..alpha. of a zero mean unit variance normal distribution corresponding to a predetermined false alarm probability, where .alpha. is a predetermined false alarm probability; and 13) generating an output signal if �S.sub.i -S.sub.Mean !/S.sub.Std dev .gtoreq..tau..sub..alpha.. The invention may also be implemented as a program storage device readable by a machine which tangibly embodies a program of instructions executable by the machine to perform the above-referenced method steps.

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Concord, MA, United States of America

David W Stein