State Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, Haikou 570228, China.
State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China.
Sensors (Basel). 2023 Jul 6;23(13):6196. doi: 10.3390/s23136196.
In the environment of unknown mutual coupling, many works on direction-of-arrival (DOA) estimation with sensor array are prone to performance degradation or even failure. Moreover, there are few literatures on off-grid direction finding using regularized sparse recovery technology. Therefore, the scenario of off-grid DOA estimation in sensor array with unknown mutual coupling is investigated, and then a reweighted off-grid Sparse Spectrum Fitting (Re-OGSpSF) approach is developed in this article. Inspired by the selection matrix, an undisturbed array output is formed to remove the unknown mutual coupling effect. Subsequently, a refined off-grid SpSF (OGSpSF) recovery model is structured by integrating the off-grid error term obtained from the first-order Taylor approximation of the higher-order term into the underlying on-grid sparse representation model. After that, a novel Re-OGSpSF framework is formulated to recover the sparse vectors, where a weighted matrix is developed by the MUSIC-like spectrum function to enhance the solution's sparsity. Ultimately, off-grid DOA estimation can be realized with the help of the recovered sparse vectors. Thanks to the off-grid representation and reweighted strategy, the proposed method can effectively and efficiently achieve high-precision continuous DOA estimation, making it favorable for real-time direction finding. The simulation results validate the superiority of the proposed method.
在未知互耦环境下，许多基于传感器阵列的到达角（DOA）估计工作容易出现性能下降甚至失效。此外，利用正则化稀疏恢复技术进行非网格测向的文献也很少。因此，本文研究了传感器阵列中存在未知互耦时的非网格 DOA 估计问题，并提出了一种重加权非网格稀疏谱拟合（Re-OGSpSF）方法。受选择矩阵的启发，形成一个未受干扰的阵列输出，以消除未知互耦的影响。随后，通过将高阶项的一阶泰勒近似得到的非网格误差项整合到基本的网格稀疏表示模型中，构建了一个改进的非网格 SpSF（OGSpSF）恢复模型。然后，提出了一种新的 Re-OGSpSF 框架来恢复稀疏向量，其中通过 MUSIC 类谱函数构建一个加权矩阵，以增强解的稀疏性。最终，通过恢复的稀疏向量实现非网格 DOA 估计。由于采用了非网格表示和加权策略，所提出的方法可以有效地实现高精度连续 DOA 估计，有利于实时测向。仿真结果验证了所提方法的优越性。
