Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/122727
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Type: Journal article
Title: Online UAV path planning for joint detection and tracking of multiple radio-tagged objects
Author: Nguyen, H.V.
Rezatofighi, H.
Vo, B.-N.
Ranasinghe, D.C.
Citation: IEEE Transactions on Signal Processing, 2019; 67(20):5365-5379
Publisher: IEEE
Issue Date: 2019
ISSN: 1053-587X
1941-0476
Statement of
Responsibility: 
Hoa Van Nguyen, Hamid Rezatofighi, Ba-Ngu Vo, and Damith C. Ranasinghe
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.
Keywords: POMDP; track-before-detect; received signal strength; information divergence; RFS; UAV
Rights: © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
DOI: 10.1109/TSP.2019.2939076
Grant ID: http://purl.org/au-research/grants/arc/LP160101177
http://purl.org/au-research/grants/arc/DP160104662
Published version: http://dx.doi.org/10.1109/TSP.2019.2939076
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Computer Science publications

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