Single-channel spaceborne-maneuvering bistatic forward-looking synthetic aperture radar (SS-BFSAR) enables the maneuvering platform to achieve high-resolution forward-looking imaging without deploying additional antennas or transmitting the radar signal. Nevertheless, achieving the suppression of spatial variant clutter with only the single-channel configuration remains a critical challenge for the ground moving target imaging (GMTIm) mission of SS-BFSAR. This study proposes an enhanced clutter suppression and GMTIm algorithm for SS-BFSAR. First, the proposed modified deramp-keystone processing (DKP) completely decouples the echo signal in range and azimuth while avoiding the signal-to-clutter ratio (SCR) degradation caused by the azimuth spectrum aliasing. Subsequently, two pre-focusing approaches are developed with nonlinear chirp scaling (NCS), i.e., global NCS and block NCS, to achieve deep focusing of spatial variant clutter while introducing differences of Doppler frequency position (DFP) between the clutter and GMT. These approaches preserve the clutter consistency of the pre-focus results, thereby ensuring that the GMT signal exhibits high SCR following the image domain cancellation. Finally, a matched filter and the proposed monostatic-equivalent model are used to refocus the GMT and estimate its velocity. The proposed algorithm can simultaneously obtain the well-focused ground scene image and the GMTIm result without DFP drift caused by the target’s motion. Comparative experiments using simulation and real data demonstrate the effectiveness and superiority of the proposed algorithm.

Hyperspectral remote sensing images (HSIs) capture detailed spectral characteristics of features, while multispectral remote sensing images (MSIs) provide clear spatial distribution. Fusing these two types of images can enhance feature identification and classification accuracy. Current deep learning algorithms achieve high fusion quality but struggle with balancing global effective perception and lightweight computation. Moreover, these algorithms typically discretely handle data mapping, which contrasts with the continuous nature of the world. Recently, the Mamba has shown significant potential for complex long-range modeling, addressing the computational complexity of global perception. Concurrently, implicit neural representation (INR) offers high-quality solutions for continuous domain modeling. To this end, this study introduces a novel network architecture that combines Mamba and INR, termed the Mamba cooperative INR fusion network (MCIFNet). MCIFNet effectively captures global image information and generates fused images in a continuous domain through point-to-point processing. The network comprises two main units: potential space projection and semantic extraction and fusion. The potential space projection unit performs shallow encoding of hyperspectral and MSIs, mapping them to a latent feature space. The semantic extraction and fusion unit (SEFU) uses scale adaptive residual state spatial and implicit spatial-spectral fusion (ISSF) modules to extract deep features from the bimodal images, generating fused images point-by-point. A series of fusion experiments with$4\times $,$8\times $, and$16\times $scale factors demonstrate that MCIFNet surpasses popular algorithms in both spatial detail and spectral information reconstruction, while also providing more lightweight performance. The code for MCIFNet will be shared onhttps://github.com/chunyuzhu/MCIFNet.

Appropriate modeling and processing of echoes are the foundations for high-precision frequency-domain synthetic aperture radar (SAR) imaging. The complex geometry makes it challenging to accurately characterize and cope with the 2-D spatial variation of Doppler modulation in maneuvering-platform translational-variant bistatic forward-looking SAR (MTV-BFSAR), resulting in that the azimuth processing method by means of setting reference points on the Cartesian coordinate axis significantly impairs the performance in terms of robustness, accuracy, and efficiency. This article proposes a comprehensive MTV-BFSAR imaging algorithm based on universal frequency-domain azimuth signal modeling (UFDASM). The presented methodology utilizes the bistatic bisector to form the azimuth reference line (ARL) and develops two expeditious ARL and range isoline (RIL) coordinates’ solving techniques, which substantially augments the reliability and efficiency of the space-variant Doppler modulation coefficient (DMC) representation, reduces the order, and improves the robustness of the entire algorithm. Subsequently, thanks to UFDASM, a modified nonlinear chirp scaling (NLCS) method with orthogonal impulse response function (IRF) is derived to eliminate the spatial variation of the quadratic DMC in the range-Doppler domain while omitting that of the cubic one. Furthermore, one may find that the equalization of the second-order spatial variation introduces a cubic phase error (CPE) term. However, boundary analyses manifest that this error is small enough not to affect the imaging performance in MTV-BFSAR. Finally, the accuracy, robustness, and efficiency of the approach are validated through numerical simulation and raw data processing.

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.

In high-maneuverability bistatic forward-looking synthetic aperture radar (HMBF-SAR) imaging, the acceleration leads to an increased residual range curve and a deepened two-dimensional spatial variance of Doppler parameters, which cannot be processed by the traditional algorithms. To address these problems, this paper establishes a more accurate digital representation for HMBF-SAR model and investigates an extended azimuth nonlinear Chirp Scaling (EANLCS) imaging method. In the flowchart of this paper, we first propose a more precise slant range model with improved expansion coefficients, and defines the range and azimuth direction of HMBF-SAR imaging. Then, a novel fast reference point (i.e., azimuth and range reference point) selection method is proposed to analyze two-dimensional spatial variance of signal characteristics, which is used to construct a high order model of residual range cell migration and Doppler parameters. Based on above analysis, we put forward an advanced imaging algorithm of combining the Second-order keystone and extended azimuth nonlinear chirp scaling (EANLCS) to compensate the increased residual range curve and two-dimensional spatial variance of Doppler parameters. Finally, the effectiveness of the proposed HBMF-SAR method is verified by several numerical simulations and comparative studies based on both the simulated and raw data.