A Cooperative Time-Frequency Approach to Detect, Recognize and Track Drones with Audio Sensor Networks (Paper with Briefing Charts)

Abstract

This work aims at proposing a detection, recognition and tracking solution for Unmanned Aircraft Systems (UAS) with a wireless audio sensor network. According to technology trends applicable to UAS (smaller, cheaper and cooperative), we propose a distributed surveillance solution with the same technology approach of the attacker one. In particular, since the drone causes a variation of the surrounding acoustic environment, we investigate the use of an audio sensor network. More precisely, a three-phase algorithm is employed to detect the presence of audio energy in the monitored environment, recognize a particular audio signature and then cooperate with a multiple node approach to track the drone. We show preliminary performance of the proposed approach by relying on experimentally acquired audio signals. We also discuss the future work to improve the actual implementation.

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Document Details

Document Type
Technical Report
Publication Date
Apr 29, 2021
Accession Number
AD1152148

Entities

People

  • Carlo Malavenda
  • Claudio Santo Malavenda
  • Marco Martalo

Organizations

  • University of Parma

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Command And Control
  • Computational Complexity
  • Computational Science
  • Cross Correlation
  • Detection
  • Detectors
  • Energy Consumption
  • Frequency
  • Information Science
  • Information Systems
  • Machine Learning
  • Neural Networks
  • Particle Swarm Optimization
  • Recognition
  • Sensor Networks
  • Signal Processing
  • Unmanned Aerial Systems
  • Unmanned Aerial Vehicles
  • Wireless Sensor Networks

Fields of Study

  • Computer science

Readers

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

Technology Areas

  • Autonomy