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Classification of UAV and bird target in low-altitude airspace with surveillance radar data

Published online by Cambridge University Press:  14 March 2019

W. S. Chen Open the ORCID record for W. S. Chen [Opens in a new window] ,
J. Liu  and
J. Li
W. S. Chen*
Affiliation:
Airport Research Institute, China Academy of Civil Aviation Science and Technology, Beijing, China
J. Liu
Affiliation:
Airport Research Institute, China Academy of Civil Aviation Science and Technology, Beijing, China
J. Li
Affiliation:
Airport Research Institute, China Academy of Civil Aviation Science and Technology, Beijing, China
*
*[email protected]
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Abstract

In order to ensure low-altitude safety, a tracking and recognition method of unmanned aerial vehicle (UAV) and bird targets based on traditional surveillance radar data is proposed. First, several motion models for UAV and flying bird targets are established. Second, the target trajectories are filtered and smoothed with multiple motion models. Third, by calculating the time-domain variance of the model occurrence probability, the model conversion probability of the target is estimated, and then the target type is identified and classified. The effectiveness and robustness of the algorithm is demonstrated by several groups of Monte Carlo simulation experiments, including setting different recognition steps, different model transformation probability, filtering and smoothing algorithm comparison. The algorithm is also successfully applied on the ground-truth radar data collected by the low-altitude surveillance radar at airport and coastal environments, where the targets of UAVs and flying birds could be tracked and recognised.

Keywords

ClassificationUAVBirdTargetRadar
Type
Research Article
Information
The Aeronautical Journal , Volume 123 , Issue 1260 , February 2019 , pp. 191 - 211
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Copyright
© Royal Aeronautical Society 2019 

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