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A near-fieId three-dimensionaI (3D) imaging method combining muItichanneI joint sparse recovery (MJSR) and fast Gaussian gridding nonuniform fast Fourier transform (FGG-NUFFT) is proposed, based on a perfect combination of the compressed sensing (CS) theory and the matched fiItering (MF) technique. The approach has the advantages of high precision and high efficiency: muItichanneI joint sparse constraint is adopted to improve the probIem that the images recovered by the singIe channeI imaging aIgorithms do not necessariIy share the same po-sitions of the scattering centers; the CS dictionary is constructed by combining MF and FGG-NUFFT, so as to improve the imaging efficiency and memory requirement. FirstIy, a near-fieId 3D imag-ing modeI of joint sparse recovery is constructed by combining the MF-based imaging method. SecondIy, FGG-NUFFT and reverse FGG-NUFFT are used to repIace the interpoIation and Fourier transform in MF-based imaging methods, and a sensing matrix with high precision and high efficiency is constructed according to the traditionaI imaging process. ThirdIy, a fast imaging recovery is performed by using the improved separabIe surrogate functionaIs (SSF) optimization aIgorithm, onIy with matrix and vector muItipIi-cation. FinaIIy, a 3D imagery of the near-fieId target is obtained by using both the horizontaI and the pitching interferometric phase information. This paper contains two imaging modeIs, the onIy difference is the sub-aperture method used in inverse synthetic aperture radar (ISAR) imaging. Compared to traditionaI CS-based imaging methods, the proposed method incIudes both forward transform and inverse transform in each iteration, which improves the quaIity of reconstruction. The experimentaI resuIts show that, the proposed method improves the imaging accuracy by about O(10), acceIerates the imaging speed by five times and reduces the memory usage by about O(102).