Gpu-Based ω-K Tomographic Processing by 1d Non-Uniform FFTS

Abstract

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).

Novel Pre-Processing Techniques for Coherence Improving in Along-Track Dual-Channel Low Frequency Sar

“…With the imaging parameters of the static target and some approximations to H a (f a , f r ), there may be a curve to the distribution of RFI in the frequency-time domain image. Fortunately, the curvature of the curve is so small that it will not influence the application of our approach, especially when the low frequency SAR uses high precise imaging algorithms, like non-linear chirp scaling algorithm (NCSA) and omegak algorithm [28][29][30]. Figure 2(b) illustrates an example of an airborne low frequency SAR data which is transformed into the frequency-time domain.…”

“…Different imaging algorithms may bring some signal processing errors. The same high precise imaging algorithm for both channels will reduce these errors [28][29][30]. The obtained image pair after imaging is x i (τ, t m ).…”

Fast, Phase-Only Synthesis of Aperiodic Reflectarrays Using Nuffts and Cuda

“…Parallelism is the future of computing [9] and the interest of the Antennas and Propagation community in topics of high performance computing and, in particular, of parallel programming on GPUs to face computationally burdened problems has been remarkable, as witnessed by [2][3][4][5][6][7] and by other electromagnetic numerical methods which have benefitted from GPU computing [10][11][12][13][14][15][16][17]. From this starting point, it is clear that the electromagnetic community can take advantage of this technological evolution to employ ever-more sophisticated numerical methods.…”

Nufft-Accelerated Plane-Polar (Also Phaseless) Near-Field/Far-Field Transformation

“…Such algorithms enable to accurately performing the interpolation stage in a computationally convenient way, so that their burden is proportional to that of a standard FFT. Furthermore, NUFFTs can be made available as library routines to make more friendly their usage and, accordingly, their exploitation in NFFF transformations, as well as other application fields [19].…”