Abstract In the field of radar automatic target recognition (RATR), the high-resolution range profile (HRRP) has received intensive attention. Bar a few exceptions, almost all HRRP-based ATR classification systems ignore the phase of the HRRP when the data is input to the classifier, relying instead only on the magnitude of the complex HRRP samples. This approach ignores the phase of the complex HRRPs, which reduces the information in the signal. In this paper, we develop a novel class factorized complex variational auto-encoder (CFCVAE) to utilize the phase of the high range resolution (HRR) radar echo for recognition. The CFCVAE is a complex-valued neural network (CV-NN) consisting of the encoding and decoding modules. In CFCVAE, the encoding module projects the observed data into the latent space, and then the latent features are fed to the decoding module, which further maps the latent features to data. In particular, the decoding module introduces the class labels to partition the whole observations into some parts, each of which is depicted by a specific class-decoder. Compared with the traditional variational auto-encoder (VAE) containing a single decoder, the CFCVAE can give a more accurate description to the whole dataset via multiple class-decoders, thus improving the characterization ability of features. In addition, based on the class labels, the CFCVAE employs the conditional prior on the latent variable to enhance the discrimination of features. Moreover, a complex backpropagation algorithm is derived for CFCVAE training, and a sample is classified to the class corresponding to the class-decoder with the minimum reconstruction error in the test stage. Experimental evaluations on the measured data indicate that the proposed method indeed achieves very promising target recognition performance.

中文翻译：

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用于 HRR 雷达目标识别的类因式复杂变分自动编码器

摘要 在雷达自动目标识别（RATR）领域，高分辨率距离剖面（HRRP）受到了广泛关注。除了少数例外，几乎所有基于 HRRP 的 ATR 分类系统在数据输入分类器时都忽略 HRRP 的相位，而仅依赖于复杂 HRRP 样本的幅度。这种方法忽略了复合 HRRP 的相位，从而减少了信号中的信息。在本文中，我们开发了一种新型的类别分解复变分自动编码器 (CFCVAE)，以利用高距离分辨率 (HRR) 雷达回波的相位进行识别。CFCVAE 是一个复值神经网络 (CV-NN)，由编码和解码模块组成。在 CFCVAE 中，编码模块将观察到的数据投影到潜在空间中，然后将潜在特征馈送到解码模块，解码模块进一步将潜在特征映射到数据。特别是，解码模块引入了类标签，将整个观察划分为一些部分，每个部分由特定的类解码器描述。与传统的包含单个解码器的变分自动编码器（VAE）相比，CFCVAE 可以通过多个类解码器对整个数据集进行更准确的描述，从而提高特征的表征能力。此外，基于类标签，CFCVAE 在潜在变量上采用条件先验来增强特征的辨别力。此外，还为 CFCVAE 训练导出了复杂的反向传播算法，将样本分类到测试阶段重构误差最小的类解码器对应的类。对测量数据的实验评估表明，所提出的方法确实实现了非常有希望的目标识别性能。