Current Direction of Arrival (DOA) estimation methods are unable to provide reliable estimates when faced with jamming attacks. To address this issue, we propose Direction of Arrival Estimation via Conditional Generative Adversarial Networks (DOA-CGAN), the first generative approach to remove the jamming component from the received signal covariance matrix. In our model, we input the received signal covariance matrix to an unsupervised generator that filters it to generate a matrix that can be deemed legitimate by a supervised discriminator. After training, we leverage the generator as a filter able to remove the jamming component from the received signal covariance matrix and feed its output to classical DOA estimation algorithms. Numerical results demonstrate that our proposed method delivers robust DOA estimation compared with other machine learning methods with a root mean squared error smaller than 0.2°.

中文翻译：

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蒸发角：一种在 DOA 估计中减轻干扰攻击的生成方法


当面对干扰攻击时，当前到达方向 （DOA） 估计方法无法提供可靠的估计。为了解决这个问题，我们提出了通过条件生成对抗网络 （DOA-CGAN） 的到达方向估计，这是第一种从接收信号协方差矩阵中去除干扰分量的生成方法。在我们的模型中，我们将接收到的信号协方差矩阵输入到无监督生成器中，该生成器对其进行过滤以生成一个可以被监督判别器认为合法的矩阵。训练后，我们利用生成器作为过滤器，能够从接收到的信号协方差矩阵中删除干扰分量，并将其输出馈送到经典的 DOA 估计算法。数值结果表明，与其他均方根误差小于 0.2° 的机器学习方法相比，我们提出的方法提供了稳健的 DOA 估计。