Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida 32611, USA.
J Acoust Soc Am. 2012 Feb;131(2):1249-59. doi: 10.1121/1.3672656.
Fast implementations of the sparse iterative covariance-based estimation (SPICE) algorithm are presented for source localization with a uniform linear array (ULA). SPICE is a robust, user parameter-free, high-resolution, iterative, and globally convergent estimation algorithm for array processing. SPICE offers superior resolution and lower sidelobe levels for source localization compared to the conventional delay-and-sum beamforming method; however, a traditional SPICE implementation has a higher computational complexity (which is exacerbated in higher dimensional data). It is shown that the computational complexity of the SPICE algorithm can be mitigated by exploiting the Toeplitz structure of the array output covariance matrix using Gohberg-Semencul factorization. The SPICE algorithm is also extended to the acoustic vector-sensor ULA scenario with a specific nonuniform white noise assumption, and the fast implementation is developed based on the block Toeplitz properties of the array output covariance matrix. Finally, numerical simulations illustrate the computational gains of the proposed methods.
提出了一种快速实现基于稀疏迭代协方差估计（SPICE）算法的方法，用于使用均匀线性阵列（ULA）进行源定位。SPICE 是一种强大的、无用户参数的、高分辨率的、迭代的、全局收敛的阵列处理估计算法。与传统的延迟和求和波束形成方法相比，SPICE 为源定位提供了更高的分辨率和更低的旁瓣电平；然而，传统的 SPICE 实现具有更高的计算复杂度（在更高维数据中会加剧）。结果表明，通过利用阵列输出协方差矩阵的 Toeplitz 结构并使用 Gohberg-Semencul 分解，可以减轻 SPICE 算法的计算复杂度。还将 SPICE 算法扩展到具有特定非均匀白噪声假设的声矢量传感器 ULA 场景，并基于阵列输出协方差矩阵的块 Toeplitz 特性开发了快速实现方法。最后，数值模拟说明了所提出方法的计算优势。
