A Unified Approach for Target Direction Finding based on Convolutional Neural Networks

Abstract

A convolutional neural network (CNNs) based approach for target direction finding with the thinned coprime array (TCA) as an example is proposed. The ResNeXt network is adopted as the backbone network with a multi-label classification modification to find directions of an unknown number of targets. Unlike the traditional wisdom, where an additional co-array operation is needed for underdetermined direction finding (the number of sources is larger than the number of physical sensors), in the proposed approach, it is shown that the same network with raw data as its input can deal with both the overdetermined and underdetermined cases, although using covariance matrix of the data can reduce the complexity of the whole training process at the cost of estimation performance.