Teresa Ann Olson

US Patent:

6901152, May 31, 2005

Filed:

Apr 2, 2003

Appl. No.:

10/405691

Inventors:

Harry C. Lee - Maitland FL, US
Johnnie J. Sanders - Orlando FL, US
Teresa L. Olson - Winter Garden FL, US

Assignee:

Lockheed Martin Corporation - Bethesda MD

International Classification:

G06K009/00

US Classification:

382103, 382224

Abstract:

An automated method of, and computer software and apparatus for, classifying objects visually into one of a plurality of object types comprising receiving a still image including an object, bounding the object within the image, dividing the bound portion of the image into a plurality of profile sections, performing a transform on each of the profile sections selected from discrete cosine transforms and discrete Fourier transforms, and executing a Bayes classifier function to segregate the object into one of the object types.

US Patent:

6909997, Jun 21, 2005

Filed:

Mar 25, 2003

Appl. No.:

10/395269

Inventors:

Hai-Wen Chen - Orlando FL, US
Teresa L. Olson - Winter Garden FL, US

Assignee:

Lockheed Martin Corporation - Orlando FL

International Classification:

G06F017/40

US Classification:

702189, 702190, 702192, 702193

Abstract:

A method and system provide a multi-sensor data fusion system capable of adaptively weighting the contributions from each one of a plurality of sensors using a plurality of data fusion methods. During a predetermined tracking period, the system receives data from each individual sensor and each data fusion method is performed to determine a plurality of reliability functions for the system based on combining each sensor reliability function which are individually weighted based on the S/N (signal-to-noise) ratio for the received data from each sensor, and a comparison of predetermined sensor operation characteristics for each sensor and a best performing (most reliable) sensor. The system may dynamically select to use one or a predetermined combination of the generated reliability functions as the current (best) reliability function which provides a confidence level for the multi-sensor system relating to the correct classification (recognition) of targets and decoys.

US Patent:

6944566, Sep 13, 2005

Filed:

Mar 25, 2003

Appl. No.:

10/395215

Inventors:

Hai-Wen Chen - Orlando FL, US
Teresa L. Olson - Winter Garden FL, US

Assignee:

Lockheed Martin Corporation - Orlando FL

International Classification:

G06F019/00

US Classification:

702116, 324614

Abstract:

A multi-sensor data fusion system and method provide an additive fusion technique including a modified belief function (algorithm) to adaptively weight the contributions from a plurality of sensors in the system and to produce multiple reliability terms including reliability terms associated with noise for low SNR situations. During a predetermined tracking period, data is received from each individual sensor in the system and a predetermined algorithm is performed to generate sensor reliability functions for each sensor based on each sensor SNR using at least one additional reliability factor associated with noise. Each sensor reliability function may be individually weighted based on the SNR for each sensor and other factors. Additive calculations are performed on the reliability functions to produce at least one system reliability function which provides a confidence level for the multi-sensor system relating to the correct classification (recognition) of desired objects (e. g. , targets and decoys).

US Patent:

7065465, Jun 20, 2006

Filed:

Mar 25, 2003

Appl. No.:

10/395265

Inventors:

Hai-Wen Chen - Orlando FL, US
Teresa L. Olson - Winter Garden FL, US

Assignee:

Lockheed Martin Corporation - Orlando FL

International Classification:

G06F 19/00
G06F 17/40

US Classification:

702116, 324614, 702189, 702190, 702192, 702193

Abstract:

A multi-sensor data fusion system and method provides adaptive weighting of the contributions from a plurality of sensors in the system using an additive calculation of a sensor reliability function for each sensor. During a predetermined tracking period, data is received from each individual sensor in the system and a sensor reliability function is determined for each sensor based on the SNR (signal-to-noise ratio) for the received data from each sensor. Each sensor reliability function is individually weighted based on the SNR for each sensor and a comparison of predetermined sensor operation characteristics for each sensor and a best performing (most reliable) sensor. Additive calculations are performed on the sensor reliability functions to produce both an absolute and a relative reliability function which provide a confidence level for the multi-sensor system relating to the correct classification (recognition) of targets and decoys.

US Patent:

7391925, Jun 24, 2008

Filed:

Dec 4, 2003

Appl. No.:

10/727039

Inventors:

Hai-Wen Chen - Orlando FL, US
Felix M. Fontan - Orlando FL, US
Teresa L. Olson - Winter Garden FL, US

Assignee:

Lockheed Martin Missiles & Fire Control - Orlando FL

International Classification:

H04N 5/217

US Classification:

382275, 382221, 382223, 348241

Abstract:

A system and method for estimating noise using measurement based parametric fitting non-uniformity correction is disclosed. Fixed pattern noise (“FPN”) is estimating from an overall noise component within a detection system to enhance candidate target detection and tracking. A sensor in the detection system receives energy, such as radiant flux, that is converted to a digital image. A non-uniformity correction device generates an estimated FPN according to an applicable temperate range and integration time. A memory storing an array of coefficients is accessed to determine the estimated FPN. The valves within the array of coefficients are based on actual FPN measurements that are parametrically fitted.

US Patent:

7430303, Sep 30, 2008

Filed:

Mar 28, 2003

Appl. No.:

10/400536

Inventors:

Jason Sefcik - Orlando FL, US
Harry C. Lee - Maitland FL, US
Teresa L. Olson - Winter Garden FL, US

Assignee:

Lockheed Martin Corporation - Orlando FL

International Classification:

G06K 9/00

US Classification:

382103, 382199, 348 94

Abstract:

A method and system detects candidate targets or objects from a viewed scene by simplifying the data, converting the data to gradient magnitude and direction data which is thresholded to simplify the data. Horizontal edges within the data are softened to reduce their masking of adjacent non-horizontal features. One or more target boxes are stepped across the image data and the number of directions of gradient direction data within the box is used to determine the presence of a target. Atmospheric attenuation is compensated. The thresholding used in one embodiment compares the gradient magnitude data to a localized threshold calculated from the local variance of the image gradient magnitude data. Imagery subsets are containing the candidate targets may then be used to detect and identify features and apply a classifier function to screen candidate detections and determine a likely target.

US Patent:

7436884, Oct 14, 2008

Filed:

Mar 25, 2003

Appl. No.:

10/395255

Inventors:

Teresa L. Olson - Winter Garden FL, US

Assignee:

Lockheed Martin Corporation - Orlando FL

International Classification:

H04B 1/66

US Classification:

375240, 382252, 382253, 37524025

Abstract:

A data compression system and method provides a best base searching method to determine the best orthogonal basis function for wavelet-based, data compression. A processing device may receive data signals from a source and wavelet-based data compression may be performed on the received data before transmission (to a receiving component) and/or further digital signal processing (DSP) of the data signals. An encoder portion of the processing device performs the data compression using a predetermined algorithm that determines the best orthogonal basis function (base) of the signal transform (used for representing the data signals) by searching a set of bases including (approximately) all orthogonal bases available to provide the minimum number of coefficients for efficient data compression while maintaining a low error rate in reconstructing the original data signals.

US Patent:

7576681, Aug 18, 2009

Filed:

May 16, 2005

Appl. No.:

11/129395

Inventors:

Hai-Wen Chen - Orlando FL, US
Teresa L. Olson - Winter Garden FL, US

Assignee:

Lockheed Martin Corporation - Bethesda MD

International Classification:

G01S 7/02
G01S 7/48
G01S 13/86
G06F 17/40
G01S 13/00

US Classification:

342 52, 342 53, 342175, 342195, 702189, 702190, 702193, 702 51

Abstract:

A method and system provide a multi-sensor data fusion system capable of adaptively weighting the contributions from each one of a plurality of sensors using a plurality of data fusion methods. During a predetermined tracking period, the system receives data from each individual sensor and each data fusion method is performed to determine a plurality of reliability functions for the system based on combining each sensor reliability function which are individually weighted based on the S/N (signal-to-noise) ratio for the received data from each sensor, and a comparison of predetermined sensor operation characteristics for each sensor and a best performing (most reliable) sensor. The system may dynamically select to use one or a predetermined combination of the generated reliability functions as the current (best) reliability function which provides a confidence level for the multi-sensor system relating to the correct classification (recognition) of targets and decoys.