Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-Tagged Objects

• Published in: IEEE transactions on signal processing Vol. 67; no. 20; pp. 5365 - 5379
• Main Authors: Nguyen, Hoa Van, Rezatofighi, Hamid, Vo, Ba-Ngu, Ranasinghe, Damith C.
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.10.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; information divergence; Markov processes; Object recognition; Path planning; Planning; POMDP; Product design; Radar tracking; Radio; Radio frequency; received signal strength; RFS; Signal to noise ratio; track-before-detect; Tracking; UAV; Unmanned aerial vehicles; Wildlife
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2019.2939076

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.