Conventional sparsity-driven synthetic aperture radar (SAR) imagery often encounters the sensitivity of nonsystematic errors and highly computational load. In this article, a cooperative multitask learning algorithm is proposed based on an autocalibrated alternating direction method of multipliers (AutoCal-ADMM) framework, by which the sparse feature of the scenes/targets-of-interests can be enhanced, and simultaneously the nonmodeled motion errors of either airborne platform or moving target can be autocalibrated in a synergistic manner. By leveraging the entropy and sparsity regularizers in the AutoCal-ADMM framework, the proposed algorithm is particularly tailored to obtain focused SAR images with enhanced sparsity. A reasonable surrogate function is designed for a convex objective function, so that an analytical proximal mapping of the entropy regularizer can be derived. Both nonsystematic range cell migration (NsRCM) and azimuthal phase errors (APEs) are concerned and coherently compensated. A linear and complex soft-thresholding operator is introduced for the sparse solution. The proposed algorithm is capable of greatly alleviating ``error propagation'' between multiple tasks, where an optima balance between the sparse and focusing features can be achieved. Superior performance in terms of convergence and efficiency can be guaranteed. Both raw SAR and canonical ground moving target imaging (GMTIm) data sets are processed and comparisons with conventions are performed, where the effectiveness and superiority of the proposed AutoCal-ADMM algorithm are validated.

Fast factorized back-projection (FFBP) has significant advantages for bistatic forward-looking synthetic aperture radar (BFSAR) imaging with arbitrary geometry and complex configuration. Conventional FFBP is generally based on the polar coordinate system (PCS) for recursive processing; however, it involves huge interpolations and causes computational inefficiency. In this article, a novel FFBP is developed for BFSAR focusing based on the Cartesian coordinate system (CCS), which is referred to as Cartesian fast factorized back-projection (CFFBP). In the new algorithm, a two-step spectrum correction is designed to avoid spectrum aliasing, and the Nyquist sampling requirement (NSR) for the BFSAR image spectrum can be decreased significantly. With low NSR in CCS, subimage merging can be implemented with noninterpolation processing, so that the proposed algorithm can achieve high performance in both accuracy and efficiency. Moreover, the practical problem of motion error is particularly considered in algorithm development, and well-adapted data-driven motion compensation (DDMC) is integrated with CFFBP based on which a new Cartesian fast time-domain (CFTD) processing framework is developed for BFSAR application. Promising results from both simulation and raw data experiments are provided and analyzed to validate the high performance of the proposed algorithm.

Due to the flexible bistatic configuration and complicated moving trajectory of radar platform, time-domain algorithms have significant focusing performance advantages for bistatic forward-looking synthetic aperture radar (BFSAR) applications. In this paper, a new fast factorized back projection (FFBP) based on orthogonal elliptical polar (OEP) coordinate is proposed for BFSAR imaging. Owing to the orthogonality of OEP system, the spectrum of BFSAR subimages can be compacted into the narrowest range and very low Nyquist sampling rate can be utilized in FFBP recursion. Comparing with the conventional FFBP based on original elliptical polar coordinate, the proposed OEP-based algorithm has prominently reduced burden in computation, especially for the BFSAR cases with large baseline geometry. Moreover, a new wavenumber decomposition-based strategy is presented to reveal two-dimensional Nyquist sample requirement without complicated calculation and mathematical derivation, which makes the FFBP process much more easier to design. Promising results from simulations and raw data experiments are presented to validate the advantages of the proposed algorithm.

Fast factorized back-projection (FFBP) is a classic fast time-domain algorithm (FTDA), which is not limited by the assumption of azimuth-invariant of echo signal and is suitable for the bistatic synthetic aperture radar (BiSAR) process of arbitrary geometric configuration. However, when the conventional FFBP processing is employed for continuous imaging of multiple full-apertures, the processing efficiency will be decreased significantly, and difficulty will be introduced in motion compensation (MOCO) development. The main contributions in this article include the following two aspects: 1) a new FTDA framework based on FFBP implementation is developed for continuous imaging where echo data are divided into several full-aperture data blocks and then processed separately by FFBP implementation to reduce redundant BP operations for achieving high efficiency and 2) an efficient and effective data-driven MOCO methodology is developed based on the new FTDA framework for high focusing quality. In MOCO, because the phase error functions of subimages are estimated in the phase history domain from different local polar coordinate systems, these phase error functions are actually discontinuous in the spatial domain, which will bring significant discontinuity and defocusing into the final image. To address this problem, the correspondence of error functions between the spatial domain and the wavenumber domain is revealed based on which the phase error functions are reconstructed to remove the discontinuity for high focusing quality. Promising results from both simulation and raw data experiments are provided and analyzed to validate the high performance of the proposed algorithm.

Due to the independence of azimuth-invariance and high implementing efficiency, fast time-domain algorithm has significant advantages for airborne bistatic synthetic aperture radar (BiSAR) data process with general geometric configuration. In this paper, the practical problem of unexpected motion errors of the airborne platform is carefully analyzed under fast factorized back-projection (FFBP) framework for general BiSAR process and a coherent data-driven motion compensation (MOCO) algorithm integrated with FFBP is proposed. By utilizing wavenumber decomposition, the analytical spectrum of polar grid image is obtained where the motion error can be conveniently investigated in image spectrum domain and the coherence between azimuthal phase error (APE) and motion induced nonsystematic range cell migration (NsRCM) can be perfectly revealed. Then, a new data-driven MOCO method for both APE and NsRCM correction is developed with FFBP process. Different from the data-driven MOCO in most frequency-domain algorithms, the residual NsRCM introduced by FFBP process is particularly analyzed and addressed in the MOCO which significantly improves the image quality in focusing. Promising results from both simulation and raw data experiments are presented and analyzed to validate the advantages of the proposed algorithm for airborne BiSAR process.

By equipping the synthetic aperture radar (SAR) sensors on multiple unmanned aerial vehicles (UAVs) to form a UAV swarm (UAVS) and operate collaboratively, UAVS-SAR presents the significant advantages of rapid echoes acquisition, high imaging frame rate as well as high system survivability for advanced SAR applications. However, due to the flexible trajectories as well as the distributed configuration, the problem of the spectrum blurring in the UAVS-SAR is more complicated than that of the conventional monostatic/bistatic SAR configurations, which makes the current fast time domain algorithms (FTDAs) difficult to achieve high imaging performance. In this paper, a novel fast time domain algorithm (FTDA) is developed for UAVS-SAR imaging with both high efficiency and promising accuracy. By developing the hierarchical framework based on the designed spectrum alignment function, the imaging procedures can be realized recursively where back projection (BP) operations are reduced dramatically, and then, the total computational burden are decreased consequently. Moreover, the trajectory difference of the UAVS formation is particularly considered for practical applications, which will inevitably degrade the coherence among the sub-images from different UAV platforms. To address this problem, a correction procedure is designed according to the distributed geometrical configuration. As the coherence between the sub-images is adequately maintained, the data-driven motion compensation (MOCO) is readily developed to remove the residual phase errors to achieve desirable imaging performance. Simulations and raw data experiments are presented to validate the advantages of the proposed algorithm.