Applying a volume dipole distribution model to next-generation sensor data for multi-object data inversion and discrimination

Fridon Shubitidze; David Karkashadze; Juan Pablo Fernández; Benjamin E. Barrowes; Kevin O'Neill; Tomasz M. Grzegorczyk; Irma Shamatava

doi:10.1117/12.850651

29 April 2010 Applying a volume dipole distribution model to next-generation sensor data for multi-object data inversion and discrimination

Fridon Shubitidze, David Karkashadze, Juan Pablo Fernández, Benjamin E. Barrowes, Kevin O'Neill, Tomasz M. Grzegorczyk, Irma Shamatava

Author Affiliations +

Proceedings Volume 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV; 766407 (2010) https://doi.org/10.1117/12.850651
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Discrimination between UXO and harmless objects is particularly difficult in highly contaminated sites where two or more objects are simultaneously present in the field of view of the sensor and produce overlapping signals. The first step in overcoming this problem is estimating the number of targets. In this work an orthonormalized volume magnetic source (ONVMS) approach is introduced for estimating the number of targets, along with their locations and orientations. The technique is based on the discrete dipole approximation, which distributes dipoles inside the computational volume. First, a set of orthogonal functions are constructed using fundamental solutions of the Helmholtz equations (i.e., Green's functions). Then, the scattered magnetic field is approximated as a series of these orthogonal functions. The magnitudes of the expansion coefficients are determined directly from the measurement data without solving an ill-posed inverse-scattering problem. The expansion coefficients are then used to determine the amplitudes of the responding volume magnetic dipoles. The algorithm's superior performance and applicability to live UXO sites are illustrated by applying it to the bi-static TEMTADS multi-target data sets collected by NRL personnel at the Aberdeen Proving Ground UXO teststand site.

Citation Download Citation

Fridon Shubitidze, David Karkashadze, Juan Pablo Fernández, Benjamin E. Barrowes, Kevin O'Neill, Tomasz M. Grzegorczyk, and Irma Shamatava "Applying a volume dipole distribution model to next-generation sensor data for multi-object data inversion and discrimination", Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 766407 (29 April 2010); https://doi.org/10.1117/12.850651

ACCESS THE FULL ARTICLE

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