DOA Estimation Based on Convolutional Autoencoder in the Presence of Array Imperfections

Array imperfections may exist in an antenna system subject to non-ideal design and practical limitations. It is difficult to accurately model array imperfections, and thus complicated algorithms are usually inevitable for model-based methods to estimate the direction of arrival (DOA) with imperfect arrays. Deep neural network (DNN)-based methods do not need to rely on pre-modeled antenna array geometries, and have been explored to handle flawed array models because of their better flexibility than model-based methods. The DNN autoencoder (DAE) method has been proposed for the array imperfection problem, which decomposes the input into multiple components in different spatial subregions. These components have more concentrated distributions than the original input, which avoid a large number of connections and nodes used in the layers to realize DOA estimation classifiers. In this paper, we study the convolutional AE (CAE) method that substantially focuses on the learning of local features in a different manner from the previous DAE method. The advantage of the convolutional operation using a kernel in CAE is to capture features in a more efficient manner than the DAE, and thus be able to reduce the number of parameters that are required to be trained in the neural networks. From the numerical evaluation of DOA estimation accuracy, the proposed CAE method is also more resistant to the noise effect than the DAE method such that the CAE method has better accuracy at a lower signal-to-noise ratio.