Attention based DOA estimation in the presence of unknown nonuniform noise

To solve the problem of performance degradation of the direction-of-arrival (DOA) estimate in non -uniform noise environment, we propose a novel attention mechanism using deep learning technology, namely array covariance attention (ACA). Specifically, to design the ACA, according to the structural char-acteristics of the covariance matrix, the pooling operation is removed, and one-dimensional convolution kernels are used to aggregate correlation characteristics in two spatial directions. With fully connected layers and non-linear activation layers, the characteristics are then coded into the perceptual attention matrix to improve useful information of the covariance matrix. Furthermore, to achieve a better perfor-mance, the integration position of the attention mechanism is also discussed in the network. Finally, a new deep-learning network is created for DOA estimation in the presence of non-uniform noise. The experimental results demonstrate the efficiency and superiority of the proposed network.& COPY; 2023 Elsevier Ltd. All rights reserved.