Abstract: A method for determining positions of moving targets in unbiased three-dimensional (3D) measurement spaces using data collected against the moving targets by an electro-optical or infrared (EO/IR) sensor, the method comprising receiving data collected from electrical signals reflected from the moving targets in a first focal plane of the EO/IR sensor at a first time point, receiving data collected from electrical signals reflected from the moving targets in a second focal plane of the EO/IR sensor at a second time point, generating two-dimensional (2D) measurement data for the moving targets in the first and second focal planes, calculating 3D target velocities for the moving targets using the 2D measurement data, and estimating local 3D positions within a first unbiased 3D measurement space for the moving targets at the first and second time points based on the 3D target velocity.

Abstract: A method for determining positions of moving targets in unbiased three-dimensional (3D) measurement spaces using data collected against the moving targets by an electro-optical or infrared (EO/IR) sensor, the method comprising receiving data collected from electrical signals reflected from the moving targets in a first focal plane of the EO/IR sensor at a first time point, receiving data collected from electrical signals reflected from the moving targets in a second focal plane of the EO/IR sensor at a second time point, generating two-dimensional (2D) measurement data for the moving targets in the first and second focal planes, calculating 3D target velocities for the moving targets using the 2D measurement data, and estimating local 3D positions within a first unbiased 3D measurement space for the moving targets at the first and second time points based on the 3D target velocity.

Abstract: System and method for determining a position of a target in an unbiased 3D measurement space: generating 2D measurement data in focal planes of each sensor; calculating a line of sight (LOS) from the target for each sensor; intersecting the LOSs and finding the closest intersection point in a 3D space; calculating a boresight LOS in 3D for each sensor; intersecting the boresight lines of sights for each sensor, and finding the closest intersection point in the 3D space to define an origin for forming the unbiased 3D measurement space; and forming local unbiased 3D estimates of the position of the target in the unbiased 3D measurement space as a difference between a closest point of the target LOS and a closest point of the boresight LOS.

Abstract: System and method for determining a position of a target in an unbiased 3D measurement space: generating 2D measurement data in focal planes of each sensor; calculating a line of sight (LOS) from the target for each sensor; intersecting the LOSs and finding the closest intersection point in a 3D space; calculating a boresight LOS in 3D for each sensor; intersecting the boresight lines of sights for each sensor, and finding the closest intersection point in the 3D space to define an origin for forming the unbiased 3D measurement space; and forming local unbiased 3D estimates of the position of the target in the unbiased 3D measurement space as a difference between a closest point of the target LOS and a closest point of the boresight LOS.

Abstract: System and method for determining a position of a target in an unbiased 3D measurement space: generating 2D measurement data in focal planes of each sensor; calculating a line of sight (LOS) from the target for each sensor; intersecting the LOSs and finding the closest intersection point in a 3D space; calculating a boresight LOS in 3D for each sensor; intersecting the boresight lines of sights for each sensor, and finding the closest intersection point in the 3D space to define an origin for forming the unbiased 3D measurement space; and forming local unbiased 3D estimates of the position of the target in the unbiased 3D measurement space as a difference between a closest point of the target LOS and a closest point of the boresight LOS.

Abstract: Technology for determining a position of a platform is described. A location of a horizon line can be determined using a sensor onboard the platform. One or more celestial objects in the sky can be detected using the sensor onboard the platform. Differential angular measurements between the horizon line and at least one of the celestial objects in the sky can be determined over a duration of time. The position of the platform can be determined based on the differential angular measurements between the horizon line and the celestial objects.

Abstract: System and method for determining a position of a target in an unbiased 3D measurement space: generating 2D measurement data in focal planes of each sensor; calculating a line of sight (LOS) from the target for each sensor; intersecting the LOSs and finding the closest intersection point in a 3D space; calculating a boresight LOS in 3D for each sensor; intersecting the boresight lines of sights for each sensor, and finding the closest intersection point in the 3D space to define an origin for forming the unbiased 3D measurement space; and forming local unbiased 3D estimates of the position of the target in the unbiased 3D measurement space as a difference between a closest point of the target LOS and a closest point of the boresight LOS.

Abstract: Technology for determining a position of a platform is described. A location of a horizon line can be determined using a sensor onboard the platform. One or more celestial objects in the sky can be detected using the sensor onboard the platform. Differential angular measurements between the horizon line and at least one of the celestial objects in the sky can be determined over a duration of time. The position of the platform can be determined based on the differential angular measurements between the horizon line and the celestial objects.

Abstract: A system and method for aiding landing of an aircraft receives sequential frames of image data of a landing site from an electro-optic sensor on the aircraft; identifies a plurality of features of the landing site in multiple sequential frames of the image data; calculates relative position and distance data between identified features within multiple sequential frames of image data using a local coordinate system within the frames; provides a mathematical 3D model of the landing site in response to the calculated relative position and distance data from the multiple sequential frames; updates the 3D model by repeating the steps of collecting, identifying, and calculating during approach to the landing site by the aircraft; and uses the 3D model from the step of updating for landing the aircraft on the landing site.

Abstract: A method for locating features in a field of view of an imaging sensor that includes receiving image data from the field of view of an imaging sensor, wherein the image data includes a plurality of image frames. The method also includes receiving three-dimensional position measurements, in an absolute coordinate system, for the imaging sensor at the point in time each image frame is acquired and identifying one or more features in each of the image frames. The method also includes determining position and velocity of the one or more features in the image frames based on changes to the one or more features between the image frames and determining three-dimensional positions of the one or more features in the image frames based on the received three-dimensional position measurements for the imaging sensor and position and velocity of the one or more features in the image frames.

Abstract: A method for locating features in a field of view of an imaging sensor that includes receiving image data from the field of view of an imaging sensor, wherein the image data includes a plurality of image frames. The method also includes receiving three-dimensional position measurements, in an absolute coordinate system, for the imaging sensor at the point in time each image frame is acquired and identifying one or more features in each of the image frames. The method also includes determining position and velocity of the one or more features in the image frames based on changes to the one or more features between the image frames and determining three-dimensional positions of the one or more features in the image frames based on the received three-dimensional position measurements for the imaging sensor and position and velocity of the one or more features in the image frames.

Abstract: A system and method of diagnosing diseases from biological data is disclosed. A system for automated disease diagnostics prediction can be generated using a database of clinical test data. The diagnostics prediction can also be used to develop screening tests to screen for one or more inapparent diseases. The prediction method can be implemented with Bayesian probability estimation techniques. The system and method permit clinical test data to be analyzed and mined for improved disease diagnosis.

Abstract: A system and method of diagnosing diseases from biological data is disclosed. A system for automated disease diagnostics prediction can be generated using a database of clinical test data. The diagnostics prediction can also be used to develop screening tests to screen for one or more inapparent diseases. The prediction method can be implemented with Bayesian probability estimation techniques. The system and method permit clinical test data to be analyzed and mined for improved disease diagnosis.

Abstract: A system and method of diagnosing diseases from biological data is disclosed. A system for automated disease diagnostics prediction can be generated using a database of clinical test data. The diagnostics prediction can also be used to develop screening tests to screen for one or more inapparent diseases. The prediction method can be implemented with Bayesian probability estimation techniques. The system and method permit clinical test data to be analyzed and mined for improved disease diagnosis.

Abstract: A probabilistic approximation of a data distribution is provided, wherein uncertain measurements in data are fused together to provide an indication of whether a new data item belongs to a given disease model. The probabilistic approximation is provided in accordance with a Bayesian analysis technique that examines the relationship of probability distributions for observable events x and multiple hypotheses H regarding those events.

Abstract: A system and method of diagnosing diseases from biological data is disclosed. A system for automated disease diagnostics prediction can be generated using a database of clinical test data. The diagnostics prediction can also be used to develop screening tests to screen for one or more inapparent diseases. The prediction method can be implemented with Bayesian probability estimation techniques. The system and method permit clinical test data to be analyzed and mined for improved disease diagnosis.

Abstract: A method of simulating multibody dynamics of a molecular system so as to produce output data representative of a time evolution of the molecular system includes providing a set of equations for characterizing multibody dynamics of the molecular system. The set of equations is constrained by a constraining equation. The method further includes providing an integrator for integrating the set of equations. The integrator is tailored for the set of equations so as to satisfy the constraining equation. The method further includes applying the integrator to the set of equations over a time step, so as to produce output data representative of the time evolution of the molecular system relative to the time step.

Abstract: A computer assisted method for generating a representation of a three dimensional structure of a molecule is described. The invention recursively filters an a priori vector of structural parameters, representative of the three dimensional structure of the molecule, based upon experimental observations relating to the structural parameters. The invention produces a refined vector of structural parameters and an associated covariance matrix indicating the accuracy of the vector. The invention includes a set of non-linear functions which implement a model relating the observations to the vector of structural parameters. The recursive steps of the method include a prediction step and an update step. During the prediction step, the non-linear model is analyzed by statistical methods. Only the results of the prediction step are incorporated in the update step, thus isolating the non-linear aspects of the model within the prediction step.

Abstract: A computer assisted method for generating a representation of a three dimensional structure of a molecule is described. The invention recursively filters an a priori vector of structural parameters, representative of the three dimensional structure of the molecule, based upon experimental observations relating to the structural parameters. The invention produces a refined vector of structural parameters and an associated covariance matrix indicating the accuracy of the vector. The invention includes a set of non-linear functions which implement a model relating the observations to the vector of structural parameters. The recursive steps of the method include a prediction step and an update step. During the prediction step, the non-linear model is analyzed by statistical methods. Only the results of the prediction step are incorporated in the update step, thus isolating the non-linear aspects of the model within the prediction step.