  The estimation of direction of arrival (DOA) is a crucial issue in
conventional radar, wireless communication, and integrated sensing and
communication (ISAC) systems. However, low-cost systems often suffer from
imperfect factors, such as antenna position perturbations, mutual coupling
effect, inconsistent gains/phases, and non-linear amplifier effect, which can
significantly degrade the performance of DOA estimation. This paper proposes a
DOA estimation method named super-resolution DOA network (SDOA-Net) based on
deep learning (DL) to characterize the realistic array more accurately. Unlike
existing DL-based DOA methods, SDOA-Net uses sampled received signals instead
of covariance matrices as input to extract data features. Furthermore, SDOA-Net
produces a vector that is independent of the DOA of the targets but can be used
to estimate their spatial spectrum. Consequently, the same training network can
be applied to any number of targets, reducing the complexity of implementation.
The proposed SDOA-Net with a low-dimension network structure also converges
faster than existing DL-based methods. The simulation results demonstrate that
SDOA-Net outperforms existing DOA estimation methods for imperfect arrays. The
SDOA-Net code is available online at
https://github.com/chenpengseu/SDOA-Net.git.
