Tensor Invariant Processing for Munitions/Clutter Classification

MR-2100

Objective

The procedures currently used to process electromagnetic induction (EMI) data were developed for use with single-axis sensors such as the Geonics EM61. They are not optimized for the new generation of multi-axis EMI sensors designed for munitions/clutter classification. As a result, the full potential of the new sensor technology is not being realized. The systems tend to be large and bulky, require vehicular support, and perform classification in a cued interrogation mode in which the data are collected while the sensor is parked over a suspected target of interest.

The objective of this project is to develop a new processing approach for multi-axis EMI sensor data that makes full use of the capabilities available with the new sensor technology and supports both detection and classification with survey data.

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Technical Approach

The technical approach exploits rotationally invariant properties of the polarizability tensor. Focusing the processing on the primary invariant (trace of the polarizability tensor) allows the search space that is required to invert EMI data to be significantly limited. Conventional dipole inversion requires searching over target (x, y, z) location, target (θ, φ, ψ) orientation, and (β1, β2, β3) principal axis polarizabilities to minimize the difference between the measured response and the response predicted by the dipole fit model. With tensor invariant processing, only (x, y, z) need to be searched to find the target location that minimizes the dispersion in calculated values for the rotationally invariant trace. The principal axis polarizabilities can then be calculated directly.

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Benefits

Because fewer parameters need to be estimated from the data, tensor invariant processing should be more robust than conventional dipole inversion processing. It can determine the principal axis polarizabilities used for classification from a single line of multi-axis data collected as the sensor passes over (but not necessarily directly over) the target. Because tensor invariant processing reduces the data demands for classification, it can ultimately lead to smaller, more manageable sensors that can be deployed over a wide variety of site conditions and produce survey data that supports both detection and classification. (Anticipated Project Completion - 2013)

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Project Documents

Points of Contact

Principal Investigator

Dr. Thomas Bell

SAIC

Phone: 703-312-6288

Fax: 703-414-3904

Program Manager

Munitions Response

SERDP and ESTCP

Project Documents

Document Types

  • Fact Sheet - Brief project summary with links to related documents and points of contact.
  • Final Report - Comprehensive report for every completed SERDP and ESTCP project that contains all technical results.
  • Cost & Performance Report - Overview of ESTCP demonstration activities, results, and conclusions, standardized to facilitate implementation decisions.
  • Technical Report - Additional interim reports, laboratory reports, demonstration reports, and technology survey reports.
  • Guidance - Instructional information on technical topics such as protocols and user’s guides.
  • Workshop Report - Summary of workshop discussion and findings.
  • Multimedia - On demand videos, animations, and webcasts highlighting featured initiatives or technologies.
  • Model/Software - Computer programs and applications available for download.
  • Database - Digitally organized collection of data available to search and access.