Dogan and Mendel (see IEEE Trans. on Signal Processing, 1994) have developed the virtual-ESPRIT algorithm (VESPA) for direction-finding and recovery of independent sources. VESPA can calibrate an array of unknown configuration and arbitrary response by using just one additional pair of identical sensors (instead of a copy of the entire array or storage of the entire array response for every possible scenario, which is required by existing alternatives). We present an approach that generalizes VESPA to handle the case of highly correlated or coherent sources. Unlike existing methods, our method is not restricted to linear arrays, and no search procedure is needed. Just as in VESPA, it is still possible to detect more sources than sensors, and suppress both Gaussian as well as non-Gaussian noise. A simulation experiment supporting our conclusions is provided.
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机译：Dogan和Mendel（请参阅IEEE Trans。on Signal Processing，1994年）已经开发了虚拟ESPRIT算法（VESPA），用于寻找和恢复独立信号源。 VESPA可以仅使用一对相同的传感器来校准未知配置和任意响应的阵列（而不是整个阵列的副本或每种情况下整个阵列响应的存储，这是现有替代方案所必需的）。我们提出了一种通用化VESPA的方法来处理高度相关或相干源的情况。与现有方法不同，我们的方法不限于线性数组，并且不需要搜索过程。与VESPA中一样，仍然有可能检测到比传感器更多的源，并同时抑制高斯和非高斯噪声。提供了支持我们结论的仿真实验。
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