This paper describes a parallel implementation of the Inverse Fast Multipole Method (IFMM) for multi-bistatic imaging configurations. NVIDIAs Compute Unified Device Architecture (CUDA) is used to parallelize and accelerate the imaging algorithm in a Graphics Processing Unit (GPU). The algorithm is validated with synthetic data generated by the Modified Equivalent Current Approximation (MECA) method and experimental data collected by a Frequency-Modulated Continuous Wave (FMCW) radar system operating in the 70-77 GHz frequency band. The presented results show that the IFMM implementation using the CUDA platform is effective at significantly reducing the algorithm computational time, providing a 300X speedup when compared to the single core OpenMP version of the algorithm.
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