Dr. Teri L. Piatt Profile

Dr. Teri L. Piatt

at Jacobs Sverdrup Technology Inc

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 19 May 2005 Paper

Radar signature generation for feature-aided tracking research

Teri Piatt, John Sherwood, Stanton Musick

Proceedings Volume 5808, (2005) https://doi.org/10.1117/12.606188

KEYWORDS: Synthetic aperture radar, Radar, Sensors, Data modeling, Seaborgium, Databases, Monte Carlo methods, Electro optical modeling, Reflectors, Solid modeling

Read Abstract +

Accurately associating sensor kinematic reports to known tracks, new tracks, or clutter is one of the greatest obstacles to effective track estimation. Feature-aiding is one technology that is emerging to address this problem, and it is expected that adding target features will aid report association by enhancing track accuracy and lengthening track life. The Sensor's Directorate of the Air Force Research Laboratory is sponsoring a challenge problem called Feature-Aided Tracking of Stop-move Objects (FATSO). The long-range goal of this research is to provide a full suite of public data and software to encourage researchers from government, industry, and academia to participate in radar-based feature-aided tracking research. The FATSO program is currently releasing a vehicle database coupled to a radar signature generator. The completed FATSO system will incorporate this database/generator into a Monte Carlo simulation environment for evaluating multiplatform/multitarget tracking scenarios. The currently released data and software contains the following: eight target models, including a tank, ammo hauler, and self-propelled artillery vehicles; and a radar signature generator capable of producing SAR and HRR signatures of all eight modeled targets in almost any configuration or articulation. In addition, the signature generator creates Z-buffer data, label map data, and radar cross-section prediction and allows the user to add noise to an image while varying sensor-target geometry (roll, pitch, yaw, squint). Future capabilities of this signature generator, such as scene models and EO signatures as well as details of the complete FATSO testbed, are outlined.

Proceedings Article | 2 September 2004 Paper

A simulation system for feature-aided tracking research

Stanton Musick, John Sherwood, Teri Piatt, Neal Carlson

Proceedings Volume 5427, (2004) https://doi.org/10.1117/12.548005

KEYWORDS: Sensors, Solid modeling, Synthetic aperture radar, Radar, Monte Carlo methods, Reflectors, Computer aided design, Seaborgium, Data modeling, Detection and tracking algorithms

Read Abstract +

Many years of tracking research have shown that the greatest obstacle to effective track estimation is accurately associating sensor kinematic reports to known tracks, new tracks, or clutter. Errors in report association occur more frequently under increasingly stressful conditions, like closely-spaced targets and low measurement rates, which can lead to unstable and even divergent tracking performance. It is widely expected that adding target features will aid report association and result in enhanced track accuracy and lengthened track life. Although sensors can provide features to enhance association, progress in implementing feature aiding has been slowed by the lack of data and tools that could assist exploration and algorithm development. To encourage research in this important discipline, the Sensors Directorate of the Air Force Research Laboratory (AFRL/SN) is sponsoring a challenge problem called Feature-Aided Tracking of Stop-move Objects (FATSO). FATSO's long-range goal is to provide a full suite of public data and software to promote explorations into viable methods of feature aiding. This paper introduces the FATSO project, focusing on an upcoming release that will contain data from a diverse target set and predictor software for generating radar signatures.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years