Many classic chirp signal processing algorithms may show significant performance degradation when the signal-to-noise ratio (SNR) is low. To address this problem, this paper proposes a pre-filtering method in time-domain based on deep learning. Different from traditional signal filtering methods, the proposed denoising convolutional neural network (DCNN) is trained to recover the pure signal from the noisy signal as much as possible. Following denoising, we use two classic chirp signal parameter estimation algorithms to estimate the parameters of the DCNN output. The simulation results show that, compared with no DCNN processing, the parameter estimation accuracy is significantly improved. This is mainly due to the powerful pure signal extraction ability of DCNN, which can significantly improve the SNR and the accuracy of signal parameter estimation.

当信噪比（SNR）较低时，许多经典的线性调频信号处理算法可能会表现出明显的性能下降。为了解决这个问题，本文提出了一种基于深度学习的时域预过滤方法。与传统的信号滤波方法不同，对提出的去噪卷积神经网络（DCNN）进行了训练，以尽可能多地从噪声信号中恢复纯信号。降噪之后，我们使用两种经典的线性调频信号参数估计算法来估计DCNN输出的参数。仿真结果表明，与不进行DCNN处理相比，参数估计精度得到了显着提高。这主要是由于DCNN具有强大的纯信号提取能力，