SDOA-Net: An Efficient Deep-Learning-Based DOA Estimation Network for Imperfect Array

Peng Chen, Zhimin Chen, Liang Liu, Yun Chen, Xianbin Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

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 nonlinear amplifier effect, which can significantly degrade the performance of DOA estimation. This article 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.

Original languageEnglish
Article number8503512
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Instrumentation and Measurement
Volume73
DOIs
Publication statusPublished - 19 Apr 2024

Keywords

  • Convolution layer
  • deep learning (DL)
  • direction of arrival (DOA) estimation
  • imperfect array
  • super-resolution method

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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