Performing as an important technology, microwave interferometry helps understand and analyze plasma characteristics. However, the relatively appropriate diagnosing methods being discovered for investigating fast time-varying plasma have met with limited success so far. The crucial problem of this task lies in eliminating the interference of additive noise while compensating for phase ambiguity of the initial point and of the phase jump points. In particular, randomly varying electron density and noise make it impossible to directly detect these points. Theoretical analysis has found that the time-varying electron density of plasma causes the received signal to rotate on the complex plane. Inspired by this fact, a deep learning (DL) method is proposed to track the time-varying plasma and solve the above two problems, during which the deep neural network (DNN) is utilized to extract the curve from the data, thereby eliminating the noise and detecting the initial point and the phase jump points according to the projection of the data on the curve. Experimental results suggest that the learned curve is in good consistency with the ground-truth curve. Meanwhile, the time-varying electron density and the collision frequency can be accurately diagnosed using the learned curve.

中文翻译：

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基于深度学习的微波时变等离子体诊断方法

作为一项重要技术，微波干涉仪有助于理解和分析等离子体特征。然而，迄今为止发现的用于研究快速随时间变化的血浆的相对合适的诊断方法取得了有限的成功。该任务的关键问题在于消除附加噪声的干扰，同时补偿初始点和相位跳变点的相位模糊性。特别地，随机变化的电子密度和噪声使得不可能直接检测这些点。理论分析发现，等离子体的时变电子密度会导致接收到的信号在复平面上旋转。受这一事实的启发，提出了一种深度学习（DL）方法来跟踪时变等离子体并解决上述两个问题，在此期间，利用深度神经网络（DNN）从数据中提取曲线，从而消除噪声并根据数据在曲线上的投影来检测初始点和相位跳变点。实验结果表明，学习曲线与地面真实曲线具有很好的一致性。同时，使用所学曲线可以准确地诊断随时间变化的电子密度和碰撞频率。