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<p>In this paper, we propose a robust signal reconstruction and direction of arrival (DOA) estimation method based on a generative adversarial network (GAN) framework under array element failures. The proposed signal reconstruction GAN (SR-GAN) consists of two components: a generator and a discriminator. The generator integrates both upsampling and downsampling operations to capture multi-resolution information from the input data and jointly leverages adversarial loss and content loss to ensure signal generation capability and reconstruction accuracy. The discriminator is designed to perform multi-scale feature extraction, enabling it to distinguish between real and reconstructed covariance matrix effectively. Simulation results demonstrate that the proposed method achieves excellent signal recovery and parameter estimation performance under varying signal-to-noise ratios (SNRs), numbers of snapshots, and configurations of missing array elements.</p>

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Robust Signal Reconstruction and DOA Estimation Based on Generative Adversarial Network under Array Element Failures

  • 期刊论文

  • 发表日期：

    2026年4月15日

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  • Liping Teng,
  • Shuai Fan,
  • Mingyang Wang,
  • Na Xing

摘要

In this paper, we propose a robust signal reconstruction and direction of arrival (DOA) estimation method based on a generative adversarial network (GAN) framework under array element failures. The proposed signal reconstruction GAN (SR-GAN) consists of two components: a generator and a discriminator. The generator integrates both upsampling and downsampling operations to capture multi-resolution information from the input data and jointly leverages adversarial loss and content loss to ensure signal generation capability and reconstruction accuracy. The discriminator is designed to perform multi-scale feature extraction, enabling it to distinguish between real and reconstructed covariance matrix effectively. Simulation results demonstrate that the proposed method achieves excellent signal recovery and parameter estimation performance under varying signal-to-noise ratios (SNRs), numbers of snapshots, and configurations of missing array elements.