用可微分MUSIC算法同时估计波达方向并校准天线硬件误差
Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays
- 将MUSIC算法改造为可微形式，实现端到端学习
- 能有效学习天线位置和增益的显著偏差
- 适用于雷达通信融合场景中的鲁棒定位
波达方向（DoA）估计是雷达、声纳、音频和无线通信系统中的常见传感任务，随着感知与通信一体化的发展而愈发重要。为充分发挥此类系统的性能潜力，必须考虑可能由硬件缺陷引起的负面影响。本文提出一种基于模型的联合DoA估计与硬件损伤学习方案，推导出可微分的多重信号分类（MUSIC）算法，支持监督与无监督学习策略，实现高效参数学习。仿真结果表明，该方法能成功学习天线位置和复增益的显著不准确，且在DoA估计性能上优于经典MUSIC算法。
原文摘要 · Abstract (English)
Direction of arrival (DoA) estimation is a common sensing problem in radar, sonar, audio, and wireless communication systems. It has gained renewed importance with the advent of the integrated sensing and communication paradigm. To fully exploit the potential of such sensing systems, it is crucial to take into account potential hardware impairments that can negatively impact the obtained performance. This study introduces a joint DoA estimation and hardware impairment learning scheme following a model-based approach. Specifically, a differentiable version of the multiple signal classification (MUSIC) algorithm is derived, allowing efficient learning of the considered impairments. The proposed approach supports both supervised and unsupervised learning strategies, showcasing its practical potential. Simulation results indicate that the proposed method successfully learns significant inaccuracies in both antenna locations and complex gains. Additionally, the proposed method outperforms the classical MUSIC algorithm in the DoA estimation task.
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