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|Title:||Online UAV path planning for joint detection and tracking of multiple radio-tagged objects|
|Citation:||IEEE Transactions on Signal Processing, 2019; 67(20):5365-5379|
|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.|
|Appears in Collections:||Aurora harvest 4|
Computer Science publications
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