Convolutional Autoencoder Based Feature Extraction in Radar Data Analysis

One of the challenging research topics is a fast, accurate, and human-like image processing method. The convolutional neural network recently shows optimistic results, but it needs a vast amount of computational resources. Utilizing feature extraction and dimensionality reduction algorithm might be a solution to increase computational efficiency. In this paper, we implemented a convolutional autoencoder for performing feature extraction and dimensionality reduction which not only can solve nonlinear problems but also easily combine the convolutional neural network. The implemented convolutional autoencoder derives remarkable reconstruction error in the experiments using the two-dimensional radar data.