Abstract
For direction of arrival (DoA) estimation, the data-driven deep-learning method has an advantage over the model-based methods since it is more robust against model imperfections. Conventionally, networks are based singly on regression or classification and may lead to unstable training and limited resolution. Alternatively, this paper proposes a two-branch neural network (TB-Net) that combines classification and regression in parallel. The grid-based classification branch is optimized by binary cross-entropy (BCE) loss and provides a mask that indicates the existence of the DoAs at predefined grids. The regression branch refines the DoA estimates by predicting the deviations from the grids. At the output layer, the outputs of the two branches are combined to obtain final DoA estimates. To achieve a lightweight model, only convolutional layers are used in the proposed TB-Net. The simulation results demonstrated that compared with the model-based and existing deep-learning methods, the proposed method can achieve higher DoA estimation accuracy in the presence of model imperfections and only has a size of 1.8 MB.
Highlights
Direction of arrival (DoA) estimation has been widely studied in the fields of acoustics, radar, sonar, and wireless communication in the past few decades [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]
The total training epoch was set to 300, and the candidate achieving the highest direction of arrival (DoA) estimation accuracy was selected as the final model
The DoA estimates were first coarsely obtained by the C-Branch and further refined by the R-Branch
Summary
Direction of arrival (DoA) estimation has been widely studied in the fields of acoustics, radar, sonar, and wireless communication in the past few decades [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]. With the rapid development of deep learning, neural-network-based algorithms have been proposed for DoA estimation. Thanks to the data-driven characteristics, these methods can be robust against model imperfections [6]. These methods can be divided into those based on regression networks or classification networks. Different structures have been proposed to estimate the DoA values. An end-to-end algorithm was proposed in [7], and a deep convolutional network was used to recover the spatial spectrum in [8]. In [12,13], bi-directional gated recurrent units (GRUs) and bidirectional long short-term memory (BiLSTM) were introduced to learn the dependencies of signals, and the DoAs were estimated by the regression layer. In [14], a DNN was proposed to map the received signals to those of a larger dimension, which can be equivalently considered as adopting an antenna array of a larger size such that the DoA resolution is improved
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