Deep Learning Training Framework for Solving Data Explosion Problem in DOA Estimation

This paper addresses the fundamental problem of data explosion encountered in the Deep learning-based Direction of Arrival (DOA) estimation, which is caused by the fact that the amount of training data grows exponentially with the number of sources. The data explosion enforces harsh requirements on the computational resources to train the deep-learning (DL) model, even making the training of the DL model mission impossible. To address this, we analyze the similarity between the received array signals in the cases of different sources, which serves as the basis for the proposed training framework. We then give the explicit formula of all possible angle combinations, demonstrating the training data explosion issue. Afterwards, we propose the training framework for progressively fine-tuning the model as the number of sources increases. By reusing a pre-trained model for k - 1 sources and fine-tuning it with a small portion of the data of k sources, the method significantly reduces the training overhead. The core of this method lies in utilizing the signal features already learned by the model trained with fewer sources and adapting it to the higher-dimensional source scenario through fine-tuning, thus avoiding the data redundancy associated with training from scratch. Numerical results show the model using the proposed training framework with only 1% of the data achieves similar performance as the fully trained model with 100% of the data.