Enhanced Coherent DOA Estimation in Low SNR Environments Through Contrastive Learning
IEEE Trans Instrum Meas(2025)
Abstract
Conventional methods for coherent direction-of-arrival (DOA) estimation often encounter considerable errors in low signal-to-noise ratio (SNR) environments. Meanwhile, deep learning approaches perform well but typically assume known signal or noise power levels for normalization—a premise not always practical in real scenarios. This study introduces a novel contrastive learning approach to enhance the performance of the deep learning method for coherent DOA estimation in a low SNR environment without the assumption of a known signal or noise power scale. The methodology includes the contrastive-learning optimization objective and the two-step training strategy for coherent DOA estimation. The proposed optimization objective is proved to significantly increase the mutual information lower bound of neural network in a self-supervised manner without the need for labels. Simulations and experiments verify that our method substantially reduces estimation errors in low SNR and Coherent environments.
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Key words
Contrastive learning,convolutional neural network,coherent signals,deep learning,direction of arrival
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