Online UAV path planning for joint detection and tracking of multiple radio-tagged objects

Hoa Van Nguyen, Hamid Rezatofighi, Ba-Ngu Vo, Damith C. Ranasinghe

Research output: Contribution to journalArticleResearchpeer-review

40 Citations (Scopus)

Abstract

We consider the problem of online path planning for joint detection and tracking of multiple unknown radio-tagged objects. This is a necessary task for gathering spatio-temporal information using UAVs with on-board sensors in a range of monitoring applications. In this paper, we propose an online path planning algorithm with joint detection and tracking because signal measurements from these objects are inherently noisy. We derive a partially observable Markov decision process with a random finite set track-before-detect (TBD) multi-object filter, which also maintains a safe distance between the UAV and the objects of interest using a void probability constraint. We show that, in practice, the multi-object likelihood function of raw signals received by the UAV in the time-frequency domain is separable and results in a numerically efficient multi-object TBD filter. We derive a TBD filter with a jump Markov system to accommodate maneuvering objects capable of switching between different dynamic modes. Our evaluations demonstrate the capability of the proposed approach to handle multiple radio-tagged objects subject to birth, death, and motion modes. Moreover, this online planning method with the TBD-based filter outperforms its detection-based counterparts in detection and tracking, especially in low signal-to-noise ratio environments.

Original languageEnglish
Pages (from-to)5365-5379
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume67
Issue number20
DOIs
Publication statusPublished - 15 Oct 2019
Externally publishedYes

Keywords

  • information divergence
  • POMDP
  • received signal strength
  • RFS
  • track-before-detect
  • UAV

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