Vector Sensors Integration on an Unmanned Surface Platform Feasibility Test

Abstract

Maritime autonomous surface and sub-surface platforms are capable of performing unmanned missions over long ranges, long periods and with increased payloads. These advances can pose major asymmetric threats to harbors, maritime forces and infrastructures as we have seen in recent warfare scenarios. Locally deployed manned and supervised monitoring systems might also be at risk and may have limited efficiency in detecting and tracking these threats. To address this challenge our team is proposing to leverage along different initiatives and develop a proof-of-concept to detect and track Unmanned Underwater Vehicles (UUVs) of small size and low acoustic footprints, using a fleet/swarm of low-cost solutions of commercially available unmanned surface autonomous vehicles (USV#s) equipped with a set of low-cost acoustic sensors. Through this grant we are proposing to see the feasibility of adapting Acoustic Vector Sensors (AVS) to an USV and a hybrid (with AVS and regular hydrophones) towed array to a small boat. We are also proposing to test options for the AVS edge processing and data integrations into the Command and Control (C2) system RIPPLES that will enable exploring multiple mission types along with multi-domain vehicle planning and execution control 24/7.

Document Details

Document Type
DoD Grant Award
Publication Date
Nov 09, 2024
Source ID
N000142412629

Entities

People

  • Emanuel Coelho

Organizations

  • Office of Naval Research
  • United States Navy

Tags

Readers

  • Acoustical Oceanography.
  • Neural Network Machine Learning.
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • Autonomy
  • Autonomy - UAVs
  • Fully Networked C3
  • Fully Networked C3 - Command and Control