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A track-before-detect algorithm for UWB radar sensor networks
- Source :
- Signal Processing. 189:108257
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Precise localization and tracking of moving non-collaborative persons and objects using a network of ultra-wideband (UWB) radar nodes has been shown to represent a practical and effective approach. In UWB radar sensor networks (RSNs), existence of strong clutter, weak target echoes, and closely spaced targets are obstacles to achieving a satisfactory tracking performance. Using a track-before-detect (TBD) approach, the waveform obtained by each node during a time period are jointly processed. Both spatial information and temporal relationship between measurements are exploited in generating all possible candidate trajectories and only the best trajectories are selected as the outcome. The effectiveness of the developed TBD technique for UWB RSNs is confirmed by numerical simulations and by two experimental results, both carried out with actual UWB signals. In the first experiment, a human target is tracked by a monostatic radar network with an average localization error of 41.9 cm with no false alarm trajectory in a cluttered outdoor environment. In the second experiment, two targets are detected by multistatic radar network with localization errors of 25.4 cm and 19.7 cm, and detection rate of the two targets is 88.75%, and no false alarm trajectory.<br />39 pages, 20 figures. Accepted in Signal Processing (Elsevier)
- Subjects :
- UWB radar
Signal Processing (eess.SP)
Computer science
Weak target
Track-before-detect
law.invention
law
FOS: Electrical engineering, electronic engineering, information engineering
Computer vision
Electrical Engineering and Systems Science - Signal Processing
Electrical and Electronic Engineering
Radar
business.industry
Node (networking)
Radar sensor network
Bistatic radar
Control and Systems Engineering
Signal Processing
Trajectory
Multistatic radar
Clutter
Computer Vision and Pattern Recognition
False alarm
Artificial intelligence
business
Software
Subjects
Details
- ISSN :
- 01651684
- Volume :
- 189
- Database :
- OpenAIRE
- Journal :
- Signal Processing
- Accession number :
- edsair.doi.dedup.....5b9c53bba8f62ed61d78a91bf321e528