USA As Represented by Secretary of the Navy

Marcus L Graham

Francis J O'Brien, Jr

Kai F Gong

Sherry E Hammel

Roy L Streit

Kathleen D Keay

Michael J Walsh

John F MacDonald

Vincent J Aidala

Tod E Luginbuhi

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

The present invention relates to a method for estimating the motion of a get relative to an observer station and a system for performing the method. The method includes the steps of: generating data representative of the motion of the target relative to the observer station during first, second, and subsequent measurement legs; processing the data to yield smoothed estimate of the bearing, bearing rate, and bearing acceleration of the target during each measurement leg; and processing the smoothed estimates of the bearing, bearing rate, and bearing acceleration of the target to provide an estimate of the position of the target relative to the observer station and the velocity of the target. The system for performing the method includes a data preprocessing subsystem for generating the smoothed estimate of the bearing rate, bearing and bearing acceleration, a passive localization and target motion analysis subsystem, and a trajectory modelling subsystem having a first module for creating a model of the observer station motion and a second module for creating a model of the motion of the target.

Recursive method for target motion analysis

An adaptive statistical filter system for receiving a data stream comprising a series of data values from a sensor associated with successive points in time. Each data value includes a data component representative of the motion of a target and a noise component, with the noise components of data values associated with proximate points in time being correlated. The adaptive statistical filter system includes a prewhitener module, a plurality of statistical filters of different orders and a selection module. The prewhitener module receives the data stream from the sensor and generates a corrected data stream comprising a series of corrected data values each associated with a data value of the data stream, each including a data component and a noise component with the noise components of data values associated with proximate points in time being decorrelated. The plural statistical filters are coupled to receive the corrected data stream in parallel from the prewhitener. Each statistical filter generates coefficient values to fit the corrected data stream to a polynomial of corresponding order and fit values representative of the degree of fit of corrected data stream to the polynomial. The selection module receives the fit values and coefficient values from the plural statistical filters and selects coefficient values from one of the filters in response to the fit values. The coefficient values are coupled to target motion analysis module which determines position and velocity of the target.

Enhanced adaptive statistical filter providing improved performance for

A system having a device for comparing incoming data with hypotheses prevsly formed from prior data for providing new hypotheses on target information. It has application when the data source is from either single or multiple targets. The incoming datum to the system forms new hypotheses assuming the incoming datum is invalid, forms new hypotheses assuming the new datum begins a new segment of information, and forms new hypotheses assuming the new datum is associated with segments in prior retained hypotheses. The one hypothesis of the thusly formed new hypotheses with the greatest likelihood of target information is then selected for further analyzation and the hypothesis selected and other hypotheses are retained for further processing with new incoming datum.

Automatic data segmentation module for target motion analysis

The adaptive statistical filter providing improved performance target mot analysis noise discrimination of the present invention includes a bank of parallel Kalman filters. Each filter estimates a statistic vector of specific order, which in the exemplary third order bank of filters of the preferred embodiment, respectively constitute coefficients of a constant, linear and quadratic fit. In addition, each filter provides a sum-of-squares residuals performance index. A sequential comparator is disclosed that performs a likelihood ratio test performed pairwise for a given model order and the next lowest, which indicates whether the tested model orders provide significant information above the next model order. The optimum model order is selected based on testing the highest model orders. A robust, unbiased estimate of minimal rank for information retention providing computational efficiency and improved performance noise discrimination is therewith accomplished.

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North Kingstown, RI, United States of America

Marcus L Graham