Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-Tagged Objects
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 pa...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
2019
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/1808.04445 http://hdl.handle.net/20.500.11937/91028 |
| _version_ | 1848765488079831040 |
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| author | Nguyen, Hoa Rezatofighi, H. Vo, Ba-Ngu Ranasinghe, D.C. |
| author_facet | Nguyen, Hoa Rezatofighi, H. Vo, Ba-Ngu Ranasinghe, D.C. |
| author_sort | Nguyen, Hoa |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:36:02Z |
| format | Journal Article |
| id | curtin-20.500.11937-91028 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:36:02Z |
| publishDate | 2019 |
| publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-910282023-05-17T04:52:50Z Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-Tagged Objects Nguyen, Hoa Rezatofighi, H. Vo, Ba-Ngu Ranasinghe, D.C. Science & Technology Technology Engineering, Electrical & Electronic Engineering POMDP track-before-detect received signal strength information divergence RFS UAV RANDOM FINITE SETS BEFORE-DETECT MULTITARGET TRACKING SENSOR-MANAGEMENT PHD FILTERS TARGET ALGORITHM cs.SY cs.SY 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. 2019 Journal Article http://hdl.handle.net/20.500.11937/91028 10.1109/TSP.2019.2939076 English https://arxiv.org/abs/1808.04445 http://purl.org/au-research/grants/arc/DP160104662 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC restricted |
| spellingShingle | Science & Technology Technology Engineering, Electrical & Electronic Engineering POMDP track-before-detect received signal strength information divergence RFS UAV RANDOM FINITE SETS BEFORE-DETECT MULTITARGET TRACKING SENSOR-MANAGEMENT PHD FILTERS TARGET ALGORITHM cs.SY cs.SY Nguyen, Hoa Rezatofighi, H. Vo, Ba-Ngu Ranasinghe, D.C. Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-Tagged Objects |
| title | Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-Tagged Objects |
| title_full | Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-Tagged Objects |
| title_fullStr | Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-Tagged Objects |
| title_full_unstemmed | Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-Tagged Objects |
| title_short | Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-Tagged Objects |
| title_sort | online uav path planning for joint detection and tracking of multiple radio-tagged objects |
| topic | Science & Technology Technology Engineering, Electrical & Electronic Engineering POMDP track-before-detect received signal strength information divergence RFS UAV RANDOM FINITE SETS BEFORE-DETECT MULTITARGET TRACKING SENSOR-MANAGEMENT PHD FILTERS TARGET ALGORITHM cs.SY cs.SY |
| url | https://arxiv.org/abs/1808.04445 https://arxiv.org/abs/1808.04445 http://hdl.handle.net/20.500.11937/91028 |