多快拍与单快拍压缩波束形成
Multiple and single snapshot compressive beamforming
AI总结：
本文研究基于压缩感知的DOA估计，利用ℓ1稀疏约束和MAP估计处理单快拍与多快拍数据，在相干多径和低信噪比下实现优于传统波束形成的高分辨率定位。
摘要：
对于在传感器阵列上观测到的声场，压缩感知（CS）利用稀疏约束重建多个声源的波达方向（DOA）。通过将每个传感器处的声压表示为所有假设DOA处声源振幅的相位滞后叠加，DOA估计被表述为一个欠定问题。采用ℓ1范数约束进行正则化使该问题可通过凸优化求解，而促进稀疏性可产生高分辨率DOA图。本文针对单快拍和多快拍两种情形，利用最大后验（MAP）估计推导稀疏声源分布。CS不需要对数据协方差矩阵求逆，因此即使在单快拍情况下也能良好工作，并且比常规波束形成具有更高的分辨率。对于多快拍，即使存在相干到达波且在低信噪比条件下，CS也优于传统的高分辨率方法。利用SWellEx96实验中垂直阵列的相干多径数据，证明了CS优越的分辨能力。
英文摘要：
For a sound field observed on a sensor array, compressive sensing (CS) reconstructs the direction-of-arrival (DOA) of multiple sources using a sparsity constraint. The DOA estimation is posed as an underdetermined problem by expressing the acoustic pressure at each sensor as a phase-lagged superposition of source amplitudes at all hypothetical DOAs. Regularizing with an $\ell_1$-norm constraint renders the problem solvable with convex optimization, and promoting sparsity gives high-resolution DOA maps. Here, the sparse source distribution is derived using maximum a posteriori (MAP) estimates for both single and multiple snapshots. CS does not require inversion of the data covariance matrix and thus works well even for a single snapshot where it gives higher resolution than conventional beamforming. For multiple snapshots, CS outperforms conventional high-resolution methods, even with coherent arrivals and at low signal-to-noise ratio. The superior resolution of CS is demonstrated with vertical array data from the SWellEx96 experiment for coherent multi-paths.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
