| A multi-frame track-before-detect algorithm based on root label clustering for multiple targets |
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| 作者姓名： | Jiaqi ZHANG Haihong TAO Xiushe ZHANG Chunlei HAN |
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| 作者单位： | 1. State Key laboratory of Radar Signal Processing, Xidian University;2. Xi'an Research Institute of Navigation Technology |
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| 摘 要： | In this paper, a novel multi-frame track-before-detect algorithm is proposed, which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise density increase. A criterion of track extrapolation is used to construct state transition set, root label is marked by state transition set to obtain the distribution information of multiple targets in measurement space, then measurement plots of multi-frame are divided into sever...
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| 收稿时间： | 24 February 2021 |
A multi-frame track-before-detect algorithm based on root label clustering for multiple targets |
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| Jiaqi ZHANG,Haihong TAO,Xiushe ZHANG,Chunlei HAN.A multi-frame track-before-detect algorithm based on root label clustering for multiple targets[J].Chinese Journal of Aeronautics,2022,35(5):87-96. |
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| Institution: | 1. State Key laboratory of Radar Signal Processing, Xidian University, Xi''an 710068, China;2. Xi’an Research Institute of Navigation Technology, Xi''an 710071, China |
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| Abstract: | In this paper, a novel multi-frame track-before-detect algorithm is proposed, which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise density increase. A criterion of track extrapolation is used to construct state transition set, root label is marked by state transition set to obtain the distribution information of multiple targets in measurement space, then measurement plots of multi-frame are divided into several clusters, and finally multi-frame track-before-detect algorithm is implemented in each cluster. The computational complexity can be reduced by employing the proposed algorithm. Simulation results show that the proposed algorithm can accurately detect multiple targets in close proximity and reduce the number of false tracks. |
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| Keywords: | Multi-frame track-before-detect Multiple targets detection Root label clustering State transition set Track extrapolation |
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