基于雷达RCS数据的空间目标识别算法研究

Research on space object recognition algorithm based on radar RCS data

近年来,深度学习在雷达目标识别领域取得了突破性进展,但基于雷达散射截面积数据的深度学习目标识别算法研究相对甚少。此外,空间目标雷达信号容易受噪声影响,导致目标识别准确率低。本文提出了一种端到端的时频特征融合神经网络TFF-Net用于实现基于RCS序列数据的空间目标识别。首先使用时频分析方法将RCS序列数据转化为二维时频数据来降低噪声干扰,其次使用TFF-Net提取时频数据的深层特征。TFF-Net先利用卷积神经网络捕获目标空间特征,接着采用双向长短时记忆网络来建模时序信息,再通过时间注意力网络自适应地关注时频数据中重要的序列。最后,在空间目标数据集上进行了算法对比实验。结果表明,所提出算法的空间目标识别精度达到95.8%,明显高于当前一些主流雷达目标识别算法,且在低信噪比情况下分类精度也优于其他算法,具有更好的噪声鲁棒性。

In recent years,deep learning has achieved breakthrough progress in radar target recognition.However,research on deep learning target recognition algorithms based on radar cross-section(RCS)data is relatively scarce.Additionally,space target radar signals are easily affected by noise,resulting in low target recognition accuracy.This paper proposes an end-to-end Time-Frequency Feature Fusion Neural Network(TFF-Net)for space target recognition based on RCS sequence data.First,time-frequency analysis methods are used to convert the RCS sequence data into two-dimensional time-frequency data to reduce noise interference.Then,TFF-Net is used to extract deep features from the time-frequency data.TFF-Net first uses a convolutional neural network to capture spatial features of the targets,then employs a bidirectional long short-term memory network to model temporal information,and finally applies a temporal attention network to adaptively focus on important sequences in the time-frequency data.Comparative experiments were conducted on a space target dataset.The results show that the proposed algorithm achieves a space target recognition accuracy of 95.8%,significantly higher than several current mainstream radar target recognition algorithms.Furthermore,the classification accuracy under low signal-to-noise ratio conditions is also superior to other algorithms,demonstrating better noise robustness.