基于深度学习的弱反馈自混合干涉信号滤波方法

Weak feedback self-mixing interference signal filtering method based on deep learning

采集激光自混合干涉信号的过程中会受到环境和电路噪声的干扰,导致信号失真。为了去掉噪声,最大限度保留原信号特征,提出了基于深度学习的自混合干涉滤波方法,该方法适用于弱反馈条件。使用自编码器作为神经网络,用加入噪声的信号作为输入,未加噪声的信号作为输出来训练网络。仿真结果表明:该方法处理含噪声自混合干涉信号时不仅能提高含噪声信号的信噪比,还能很好地保留干涉条纹的波形特征,即条纹的倾斜方向。实验中,使用深度学习方法滤波,再用条纹计数法进行位移重构,结果表明该方法对弱反馈条件下的自混合干涉信号有较好的滤波效果。

In the process of collecting laser self-mixing interference signal,it is interfered by environment and circuit noise,resulting in signal distortion.In order to remove the noise and preserve the original signal features to the maximum extent,a self-mixing interference filtering method based on deep learning is proposed,which is suitable for weak feedback conditions.An autoencoder is used as a neural network,and a noisy signal is used as input and an unnoisy signal as output to train the network.The simulation results show that this method can not only improve the signal-to-noise ratio of the noisy self-mixing interference signal,but also preserve the waveform characteristics of the interference fringe,namely,the inclination direction of the fringe.In the experiment,the deep learning method is used to filter,and then the fringe counting method is used to reconstruct the displacement.The results show that this method has a good filtering effect on the self-mixing interference signal under the weak feedback condition.