Abstract-We introduce and discuss a parallel SAR backprojection algorithm using a Non-Uniform FFT (NUFFT) routine implemented on a GPU in CUDA language.The details of a convenient GPU implementation of the NUFFT-based SAR backprojection algorithm, amenable to further generalizations to a multi-GPU architecture, are also given.The performance of the approach is analyzed in terms of accuracy and computational speed by comparisons to a "standard", parallel version of the backprojection algorithm exploiting FFT + interpolation instead of the NUFFT. Different interpolators have been considered for the latter processing scheme. The NUFFT-based backprojection has proven significantly more accurate than all the compared approach, with a computing time of the same order. An analysis of the computational burden of all the different steps involved in both the considered approaches (i.e., standard and NUFFT backprojections) has been also reported.Experimental results against the Air Force Research Laboratory (AFRL) airborne data delivered under the "challenge problem for SAR-based Ground Moving Target Identification (GMTI) in urban environments" and collected over circular flight paths are also shown.

Abstract-We present an ω-k approach based on the use of a 1D NonUniform FFT (NUFFT) routine, of NER (Non-Equispaced Results) type, programmed on a GPU in CUDA language, amenable to realtime applications. A Matlab main program links, via mex files, a compiled parallel (CUDA) routine implementing the NUFFT. The approach is shown to be an extension of an already developed parallel algorithm based on standard backprojection processing to account also for near-field data. The implementation of the GPU-based, parallel NUFFT routine is detailed and the computational advantages of the developed approach are highlighted against other confronted sequential or parallel (on multi-core CPU) procedures. Furthermore, the benefits of the ω-k, NUFFT-based processing are pointed out by both comparing its accuracy and computational convenience against other interpolators, and by providing numerical results. By comparing the computational performance of the algorithm against a multi-core, Matlab implementation, the speedup has been about 20 for a medium size image. The performance of the approach has been pointed out in the applicative case of vegetation imaging against experimental data of a boxtree (Buxus tree), also under a source of temporal decorrelation (wind).

Abstract-The paper introduces the use of Non-Uniform Fast Fourier Transform (NUFFT) routines in "complex" (i.e., amplitude and phase) and phaseless Near-Field/Far-Field transformations. The use of those routines results computationally very convenient when non-regular field sampling prevents the use of standard FFTs. The attention is focused on a plane-polar acquisition geometry. Numerical and experimental results show the effectiveness of the developed algorithms.

Abstract-The problem of characterizing random sources from near-field measurements and of devising the random field sampling procedure is tackled by a stochastic approach. The presented technique is an extension of that introduced in [22] and successfully adopted to experimentally characterize deterministic (CW and multi-frequency) radiators and fields. Under the assumption that the source is wide sense stationary, quasi-monochromatic and incoherent, its intensity is reconstructed by time-domain field measurements aimed at extracting information from the mutual coherence of the acquired near-field. The linear relation between the field coherence and the source intensity is inverted by using the Singular Value Decomposition (SVD) approach, properly representing the source intensity distribution by exploiting the a priori information (e.g., its size and shape) on the radiator. The sampling of the radiated random field is devised by a singular value optimization procedure of the relevant finite dimensional linear operator. Experimental results using a slotted reverberation chamber as incoherent source assess the performance of the approach.

We present the development of a fully parallel Magnetic Resonance Imaging algorithm written in CUDA language capable to very quickly process data collected under non-conventional (e.g., spiral) acquisition schemes