DOA Estimation With Deep Learning: A Limited Training Data Framework

Yunye Su, Xianpeng Wang, Yuehao Guo, Feifei Gao

Abstract: Deep Learning (DL) achieves significant performance in estimating the direction of arrival (DOA) in array signal processing. However, many existing DL methods require a large amount of data to train a specialized DL network. To reduce data requirements for training, this paper presents a novel DL-based DOA estimation algorithm for limited training data(LTDDOA-net). The proposed algorithm utilizes the properties of second-order derivatives of the loss function and the ‘learn to learn’ approach to construct a framework that can achieve good performance with minimal data training. Initially, we developed a neural network designed for DOA estimation. This network was subsequently trained using proposed method and loss function on a limited dataset. Ultimately, we validated the practicality and benefits of our approach through simulations and hardware experiment. The results of simulations and hardware experiment have verified the superiority of the proposed approach.

External IDs:dblp:journals/tcom/SuWGG25