Advanced Physics & Statistics-Based Algorithms for Standoff IED Detection/Classification

Abstract

Standoff, low-frequency, wideband ground penetrating radar (GPR) systems have recentlybeen developed and offer the potential to find buried explosive hazards at safe distances.Low target-to-clutter ratios and limited spatial resolution resulting from forward-looking measurement geometries at low grazing angles limit the performance of automated detection and classification algorithms on these data. The Michigan Tech Research Institute (MTRI) proposes basic research in algorithms to overcome these measurement limitations and to generalize machine learning algorithms to fundamentally improve automatic target recognition (ATR) performance with standoff detection explosive hazard detection systems.

Document Details

Document Type
DoD Grant Award
Publication Date
Sep 23, 2016
Source ID
N000141612623

Entities

People

  • Matthew Masarik

Organizations

  • Michigan Technological University
  • Office of Naval Research
  • United States Navy

Tags

Readers

  • Neural Network Machine Learning.
  • Radar Systems Engineering.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks