Classical methods for model order selection often fail in scenarios with low SNR or few snapshots. Deep learning based methods are promising alternatives for such challenging situations as they compensate lack of information in observations with repeated training on large datasets. This manuscript proposes an approach that uses a variational autoencoder (VAE) for model order selection. The idea is to learn a parameterized conditional covariance matrix at the VAE decoder that approximates the true signal covariance matrix. The method itself is unsupervised and only requires a small representative dataset for calibration purposes after training of the VAE. Numerical simulations show that the proposed method clearly outperforms classical methods and even reaches or beats a supervised approach depending on the considered snapshots.
翻译:以深层次学习为基础的方法对于具有挑战性的情况来说是很有希望的替代方法,因为这些方法弥补了观测中缺乏信息的情况,同时对大型数据集进行了多次培训。该手稿提出一种方法,在选择命令模型时使用变式自动编码器(VAE),其想法是在VAE 解码器上学习一个参数化的有条件共变矩阵,该矩阵与真实信号共变矩阵相近。该方法本身不受监督,在对 VAE 进行培训后,只需要为校准目的建立一个有代表性的小型数据集。 数字模拟表明,拟议方法明显优于古典方法,甚至根据所考虑的截图达到或超过监督方法。