PARALLEL ARCHITECTURES FOR MULTIRATE SUPERRESOLUTION SPECTRUM ANALYZERS

This work develops parallel architectures for implementing matrix based superresolution spectral estimation algorithms for situations that require high levels of resolution commensurate with large coherent apertures and large sample orders. The featured architectures couple matrix based superresolution algorithms together with front-end multirate decimation preprocessor. This procedure creates parallel pseudo-apertures corresponding to different sub-bands of the temporal (or spatial) frequency spectrum. The overall superresolution of the large aperture is maintained. Simulations applying the large array architectures to the Tufts-Kumaresan reduced rank modified covariance algorithm and the linear minimum free energy/regularized form of the modified covariance algorithm are given for 1024-element coherent apertures.