Harbor Threat Detection, Classification, and Identification

Abstract

There is a critical need for reliably and rapidly detecting, identifying, and tracking submerged low observable targets in port environments, which would allow for rapid and effective neutralization of low observable threats. Without this capability, personnel, naval platforms and targets of opportunity are exposed to a cheap kill by an opportunistic threat. The long term goal of this effort is to exploit for the first time detailed active and passive signature information of harbor threats together with advanced Bayesian classifier techniques. Ultimately it is the intent of this effort to leverage the highly successful science and technology carried out in broadband mine identification [1] and EOY reports for Award Numbers: N0001406WX20052 and N0001406WX20679]. The objective is to exploit passive and active acoustic signal information associated with submerged threats in harbors and ports in order to monitor their presence in real time. There is no known capability for reliably detecting, identifying, and tracking low observable targets in such environments, particularly at ranges ~ 1km. Submerged threats include a variety of both man-made and human targets and this project emphasizes swimmer and non-swimmer threats. This project will lead to identification and demonstration through experimentation and simulation.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2006
Accession Number
ADA612415

Entities

People

  • Brian H. Houston
  • Larry Carin
  • Tim Yoder

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Acoustic Scattering
  • Acoustic Signals
  • Algorithms
  • Broadband
  • Classification
  • Computational Science
  • Data Analysis
  • Detection
  • Frequency
  • Identification
  • Information Science
  • Machine Learning
  • Measurement
  • Military Research
  • Scattering
  • Sequential Monte Carlo Methods
  • Signal Processing

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - DoD AI Strategy