| 摘要 |
Parallel architectures preprocesses large matrices from digital phased array systems, receiving signals from distant sources, to produce lower order matrices, called pseudo coherent apertures, which are computationally less burdensome. The large matrices are processed by frequency shifting, low pass filtering with an FIR filter, and executing front-end decimation to create the pseudo coherent apertures, each corresponding to different sectors of the spatial frequency spectrum. The pseudo coherent apertures are processed using matrix based superresolution spectral estimation algorithms such as the Tufts-Kumaresan (T-K) reduced rank modified covariance algorithm and the Linear Minimum Free Energy algorithms produce an image of the sources.
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