PSO-Based Sparse Source Location in Large-Scale Environments With a UAV Swarm

Junqi Zhang, Yehao Lu, Yunzhe Wu, Cheng Wang, Di Zang, Abdullah Abusorrah, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Locating multiple sources in an unknown environment based on their signal strength is called a multi-source location problem. In recent years, there has been great interest in deploying autonomous devices to solve it. A particle swarm optimizer (PSO) is a widely employed source location method. Yet most work in this field focuses on a flat search space while ignoring height information. An unmanned aerial vehicle (UAV) has a coarser but wider view as it flies higher. Inspired by such facts, this paper focuses on improving the efficiency of locating sources by utilizing height information through UAVs. A novel source location model is designed where their sensing range gradually increases as their flying height rises, but their obtained signal strength fades away. It can be directly deployed to existing PSO-based multi-source location methods and improve their performance, especially in a large-scale environment with sparse sources. UAVs can spontaneously switch their search schemes between a rough search at a higher height and a fine one at a lower height. Experimental results of three PSO-based methods show their significant improvement after deploying our model. Given the same computation resources, its deployment leads to over 30% hike in both location accuracy and speed. This represents a great advance to the field of source location.

Original languageEnglish (US)
Pages (from-to)5249-5258
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number5
StatePublished - May 1 2023

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


  • Swarm robots
  • particle swarm optimizer (PSO)
  • source location problem


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