Multi-DOA estimation based on the KR image tensor and improved estimation network

Abstract

Deep neural networks have shown great performance for direction-of-arrival (DOA) estimation problem, but it is necessary to design some suitable networks to solve the multi-DOA estimation problem. In this paper, we use Khatri–Rao product to increase the degree of freedom of antenna array and obtain the image tensor of covariance matrix, then we propose an improved estimation network to process the tensor. We use the curriculum learning scheme and partial label strategy to develop a CurriculumNet training scheme. The training/validation results shows that the proposed training scheme can increase the generalization of the estimation network and improve the accuracy of network around $$10\%$$ 10 % . The estimation performance of the proposed network shows high-resolution results, which can distinguish two adjacent signals with angle difference of $$3^\circ $$ 3 ∘ . Moreover, the proposed estimation network has root mean square estimation error lower than $$1^\circ $$ 1 ∘ when signal noise ratio equals $$-\,10\,{\mathrm {dB}}$$ - 10 dB and can estimate DOAs precisely by only 8 snapshots, which performs much better than prior deep neural network based estimation methods and can estimate multi-DOA results under hostile estimation environments.

A Generalized Noise Reconstruction Approach for Robust DOA Estimation

“…Recently, data-driven deep learning-based algorithms have been developed rapidly [31], and their strong nonlinear map-ping capability and robustness have attracted a lot of attention. In terms of DOA estimation, deep neural networks (DNN) [32], convolutional neural networks (CNN) [33], and convolutional recurrent neural networks (CRNN) [34], [35] yield very accurate estimation results through multi-label classification (MLC) or regression space spectrum. However, since the data-driven algorithms are very sensitive to data, while dynamic range of data is not taken into consideration, resulting in the network learning from the experience of prejudice to any fixed signal power during the training process [33].…”

A Generalized Noise Reconstruction Framework for DOA Estimation in Received Data Domain

“…Recently, data-driven deep learning-based algorithms have been developed rapidly [31], and their strong nonlinear map-ping capability and robustness have attracted a lot of attention. In terms of DOA estimation, deep neural networks (DNN) [32], convolutional neural networks (CNN) [33], and convolutional recurrent neural networks (CRNN) [34], [35] yield very accurate estimation results through multi-label classification (MLC) or regression space spectrum. However, since the data-driven algorithms are very sensitive to data, while dynamic range of data is not taken into consideration, resulting in the network learning from the experience of prejudice to any fixed signal power during the training process [33].…”