论文标题
使用基于模型的解码器的无监督参数估计
Unsupervised Parameter Estimation using Model-based Decoder
论文作者
论文摘要
在这项工作中，我们考虑使用基于模型的解码器与无监督的学习策略（DOA）估计相结合。仅依靠我们在分析中显示的未标记的培训数据，我们可以超越现有的无监督机器学习方法和经典方法。所提出的方法包括在自动编码器体系结构中引入基于模型的解码器，该解码器导致自动编码器潜在空间中统计模型的有意义表示。我们的数值模拟表明，所提出的方法的性能不受相关信号的影响，并且对不相关和相关的方案都表现良好。这是一个事实的结果，即在提出的框架中，同时估算了信号协方差矩阵和DOA。
In this work, we consider the use of a model-based decoder in combination with an unsupervised learning strategy for direction-of-arrival (DoA) estimation. Relying only on unlabeled training data we show in our analysis that we can outperform existing unsupervised machine learning methods and classical methods. The proposed approach consists of introducing a model-based decoder in an autoencoder architecture which leads to a meaningful representation of the statistical model in the latent space of the autoencoder. Our numerical simulations show that the performance of the presented approach is not affected by correlated signals and performs well for both, uncorrelated and correlated, scenarios. This is a result of the fact, that, in the proposed framework, the signal covariance matrix and the DOAs are estimated simultaneously.
