There are many cases in which the noise corrupts the signals in a significant manner. To better analyze these signals, the noise must be removed from the signals for further data analysis, and the process of noise removal is referred to as denoising. In this letter, we propose a novel approach to the pulse denoising problem by extracting features from time of arrival (TOA) sequences using the autoencoders. The noise-contaminated TOA sequence is first coded into a binary vector and then fed into an autoencoder for training. Then, the trained autoencoder is capable of generating the original TOA sequence without lost and spurious pulses. Moreover, the proposed method does not require a noise-free TOA sequence as a priori as with conventional autoencoders. Simulation results show that the new technique can deal with TOA sequences with complex pulse repetition interval (PRI) modes that have not been tackled before. In addition, the proposed method has a better performance in noisy environments than conventional methods and general deep neural network structures.

中文翻译：

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使用自动编码器对雷达脉冲流进行去噪

在许多情况下，噪声会以显着的方式破坏信号。为了更好地分析这些信号，必须从信号中去除噪声以进行进一步的数据分析，去除噪声的过程称为去噪。在这封信中，我们通过使用自动编码器从到达时间 (TOA) 序列中提取特征，提出了一种解决脉冲降噪问题的新方法。受噪声污染的 TOA 序列首先被编码成一个二进制向量，然后被送入一个自动编码器进行训练。然后，经过训练的自动编码器能够生成原始 TOA 序列，而不会丢失和虚假脉冲。此外，所提出的方法不需要像传统自动编码器那样先验的无噪声 TOA 序列。仿真结果表明，新技术可以处理具有复杂脉冲重复间隔（PRI）模式的TOA序列，这些模式以前没有被解决过。此外，与传统方法和一般深度神经网络结构相比，所提出的方法在嘈杂环境中具有更好的性能。