Open AccessDrone-based Sound Source Localisation: A Systematic Literature Review
Author(s) -
Sergio F. Chevtchenko,
Belman Jahir Rodriguez,
Rafaella F. Do Vale,
Abishek Soti,
Yeshwanth Bethi,
Naqib Ibnul,
Alexandre Marcireau,
Mostafa Rahimi Azghadi,
Andrew Wabnitz,
Saeed Afshar
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3572478
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Sound source localization (SSL) using microphones mounted on unnamed aerial vehicles (UAVs) holds significant potential for tasks ranging from search-and-rescue and gunshot detection to industrial inspection and wildlife monitoring, particularly in scenarios where camera-based sensing may be limited by poor visibility. Recent surveys take a broad view of SSL methods, with limited coverage of UAV-based approaches. This paper addresses this gap through a systematic review of UAV-based SSL, drawing on 49 studies published between 2014 and 2024. We address four research questions: (1) What is the array configuration of the SSL platform? (2) What are the intended applications of SSL on UAV platforms? (3) What are the choices for the UAV platforms? (4) What are the solutions employed for performing the SSL task? Our findings indicate that 82% of the studies are intended for search-and-rescue contexts, 60% rely on arrays of eight microphones or fewer, and only three implement real-time onboard SSL. Notably, real-time processing of larger arrays and public availability of comprehensive datasets are identified among key obstacles to the field. We conclude by identifying research gaps and proposing future directions in multi-source localisation, real-time processing, and energy efficiency.