Deep learning-based Doppler-spread target detection with attention mechanism

Linsheng Bu*, Wanyu Chang, Defeng Chen, Tuo Fu

*此作品的通讯作者

科研成果: 期刊稿件会议文章同行评审

摘要

In radar systems, Doppler spread issues commonly arise in space small target detections when integration is adopted during a long coherent processing interval (CPI). Even with precise compensation for translational effects, phase variations introduced by changes in target observation attitudes (TOA) can still cause the target's echo energy to spread across multiple Doppler cells. As the TOA undergoes several periods over a CPI, the echo energy disperses into equidistant Doppler cells within a range cell in the range-Doppler (RD) map, which poses challenges for traditional methods and results in degraded detection performance. Leveraging the convolutional neural network's ability to autonomously extract data features, we propose a neural network based on the Doppler auto-correlation attention mechanism (DAAM) to enhance detection accuracy and precisely estimate target positions by exploiting the sparse characteristics of the target Doppler spectrum. Training and testing are conducted using an RD map dataset, and experimental results demonstrate the network's advanced performance in both target detection and position estimation.

源语言英语
文章编号012023
期刊Journal of Physics: Conference Series
2906
1
DOI
出版状态已出版 - 2024
活动4th International Conference on Electronic Communication, Computer Science and Technology, ECCST 2024 - Shanghai, 中国
期限: 20 9月 202422 9月 2024

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引用此

Bu, L., Chang, W., Chen, D., & Fu, T. (2024). Deep learning-based Doppler-spread target detection with attention mechanism. Journal of Physics: Conference Series, 2906(1), 文章 012023. https://doi.org/10.1088/1742-6596/2906/1/012023