Automatic target recognition method for low-resolution ground surveillance radar based on 1D-CNN

Renhong Xie, Bohao Dong, Peng Li, Yibin Rui, Xing Wang, Junfeng Wei
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Abstract

This paper proposes a low-resolution ground surveillance radar automatic target recognition(ATR) method based on onedimensional convolutional neural network (1D-CNN), which solves the problem of overfitting using complex CNN for data classification. First, the target recognition algorithm combines the time-domain waveform, power spectrum, and power transform spectrum into the three channels of the established 1D-CNN input. After that, the autoencoder is used to reduce the feature dimension and improve the classifier's ability to select parameters autonomously. Finally, the Bayesian hyperparameter optimization method is used to optimize hyperparameters, which not only simplifies the network structure, but also reduces the parameter calculation scale. We tested our method with the collected data to classify people and cars, and the results showed that the recognition accuracy rate has reached 99%. Compared with the traditional artificial feature extraction target recognition method, our model has better recognition performance and adaptability.
基于1D-CNN的低分辨率地面监视雷达目标自动识别方法
提出了一种基于一维卷积神经网络(1D-CNN)的低分辨率地面监视雷达自动目标识别(ATR)方法,解决了使用复杂CNN进行数据分类的过拟合问题。首先,目标识别算法将时域波形、功率谱和功率变换谱合并到已建立的1D-CNN输入的三个通道中。然后利用自编码器降低特征维数,提高分类器自主选择参数的能力。最后,采用贝叶斯超参数优化方法对超参数进行优化,既简化了网络结构,又减小了参数计算规模。我们用收集到的数据对我们的方法进行了测试,对人和车进行了分类,结果表明我们的识别准确率达到了99%。与传统的人工特征提取目标识别方法相比,我们的模型具有更好的识别性能和适应性。
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