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| A Robust Multi-Algorithm Fusion Track Initiation Algorithm Based on Machine Learning |
| LI Chuan1，2， NIE Yiwen1，2， LIU Junwei1，2， MENG Fanqin3， SHEN Xiaojing4 |
| 1. East China Research Institute of Electronic Engineering， Hefei 230021， Anhui， China；2. Key Laboratory of
Aperture Array and Space， Hefei 230021， Anhui， China；3. Institute of space science and engineering， Sichuan
University， Chengdu 610065， Sichuan， China；4. Institute of mathematics， Sichuan University，
Chengdu 610065， Sichuan， China |
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Abstract A robust track initiation algorithm based on multi-algorithm fusion learning is proposed to resolve the correct and effective track initiation issue due to the effect of clutter and jamming in strong ECM and complex radar mission environment. This method regards the track initiation issue as classification issue, and uses the classical machine learning classification algorithm—random forest and GBDT as basis for fusion and classification. Chair-Varshney optimal decision fusion is applied to these two methods to achieve the effective and correct initiation of the target track. The simulation is used to compare the proposed method in this paper with random forest, GBDT, and Heuristic rule. The results show that the robust track initiation algorithm based on multi-algorithm fusion learning has better overall performance, much better than that based on GBDT and Heuristic rule.
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Received: 18 November 2021
Published: 25 March 2022
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2022, Vol. 5 
