Strategies for DOA-DNN Estimation Accuracy Improvement at Low and High SNRs

Abstract

Implementation of several wireless applications such as radar systems and source localization is possible with direction of arrival (DOA) estimation, an array signal processing technique. In the past, we proposed a DOA estimation method using deep neural networks (DNNs), which presented very good performance compared to the traditional root multiple signal classification (root-MUSIC) algorithm when the number of radio wave sources is two. However, once three radio wave sources are considered, the performance of that proposed DNN decays especially at low and high signal-to-noise ratios (SNRs). In this paper, mainly focusing on the case of three sources, we present two additional strategies based on our previous method and capable of dealing with each SNR region. The first, which supports DOA estimation at low SNRs, is a scheme that makes use of principal component analysis (PCA). By representing the DNN input data in a lower dimension with PCA, it is believed that the noise corrupting the data is greatly reduced, which leads to improved performance at such SNRs. The second, which supports DOA estimation at high SNRs, is a scheme where several DNNs specialized in radio waves with close DOA are accordingly selected to produce a more reliable angular spectrum grid in such circumstances. Finally, in order to merge both ideas together, we use our previously proposed SNR estimation technique, with which appropriate selection between the two schemes mentioned above is performed. We have verified the superiority of our methods over root-MUSIC and our previous technique through computer simulation when the number of sources is three. In addition, brief discussion on the performance of these proposed methods for the case of higher number of sources is also given.