An L-band geosynchronous synthetic aperture radar (GEO SAR) will be inevitably affected by ionosphere scintillation because of its low carrier frequency. Meanwhile, compared with the low Earth orbit (LEO) SAR, a higher orbit of GEO SAR makes it have a longer integration time and a longer operation time within the susceptible regions of ionospheric scintillation. Thus, its imaging is more sensitive to ionospheric scintillation, and the corresponding degradation will have a different pattern. However, few works are focused on the quantitative analysis of the ionospheric scintillation impacts on L-band SAR. Moreover, the parameters of ionospheric irregularities utilized in the analyses are hard to be determined. In this paper, we first deduced the azimuth point-spread function with the consideration of both the amplitude and phase scintillation. Then, based on the measurable statistical parameters of ionospheric scintillation, performance specifications, including azimuth resolution, azimuth peak-to-sidelobe ratio (PSLR), and azimuth integrated sidelobe ratio (ISLR) are obtained to fully evaluate the impacts. The analysis suggests that in GEO SAR imaging, the azimuth ISLR severely deteriorates, whereas degradations of the azimuth resolution and PSLR are negligible. Finally, the simulations and a real ionospheric scintillation monitoring experiment by employing Global Positioning System satellites receivers were conducted, verifying the conclusions that the serious degraded contrast and focus quality of the images are brought by the raised azimuth ISLR.

Existing ship inverse synthetic aperture radar (ISAR) imaging algorithms are not applicable, when the signal-to-noise ratio (SNR) is low, for the translational motion that cannot be well compensated by existing algorithms. To achieve ship ISAR imaging with low SNR, a ship ISAR imaging algorithm based on the generalized radon-Fourier transform (GRFT) is proposed in this paper. Considering not only the rotational motion but also the translational motion between the radar and the ship, the proposed algorithm uses the GRFT to simultaneously compensate the time-variant range envelopes and the Doppler phase. Thus, the signal coherence is fully utilized, and the coherent integration of the ship's multicomponent echo signal is realized. Subsequently, to overcome the problem of the heavy computational load and improve the efficiency of the proposed algorithm, the scheme of cascaded GRFTs that consists of the coarse GRFT and the subsequent fine GRFT is adopted. The coarse GRFT with large search ranges and intervals is aimed at obtaining the real ranges of ship scatter points' motion parameters. Based on the coarse GRFT result, the fine GRFT with small search ranges and intervals is performed to efficiently obtain the coherent integration result. Then, based on the coherent integration result, the constant false alarm rate (CFAR) detection is performed to obtain the desired scatter points and their amplitudes and motion parameters, and the multicomponent signal is reconstructed. Finally, based on the reconstructed multicomponent signal, the high-quality instantaneous ship ISAR image can be obtained. Computer simulations and experiment results validate the effectiveness of the proposed algorithm.

Recent advances in deep learning have significantly improved the quality of precipitation nowcasting. Current approaches are either based on deterministic or generative models. Deterministic models perceive nowcasting as a spatiotemporal prediction task, relying on distance functions like L2-norm loss for training. While improving meteorological evaluation metrics, they inevitably produce blurry predictions with no reference value. In contrast, generative models aim to capture realistic precipitation distributions and generate nowcasting products by sampling within these distributions. However, designing a generative model that produces realistic samples satisfying meteorological evaluation indexes in real-time remains challenging, given the triple dilemma of generative learning: achieving high sample quality, mode coverage, and fast sampling simultaneously. Recently, diffusion models exhibit impressive sample quality but suffer from time-consuming sampling, severely hindering their application in nowcasting. Moreover, samples generated by the U-Net denoiser of current denoising diffusion model are prone to yield poor meteorological evaluation metrics such as CSI. To this end, we propose a spatiotemporal Transformer-based conditional diffusion model with rapid diffusion strategy. Concretely, we incorporate an adversarial mapping-based rapid diffusion strategy to overcome the time-consuming sampling process for standard diffusion models, enabling timely nowcasting. Additionally, a meticulously designed spatiotemporal Transformer-based denoiser is incorporated into diffusion models, remedying the defects in U-Net denoisers by estimating diffusion scores and improving nowcasting skill scores. Case studies of typical weather events such as thunderstorms, as well as quantitative indicators, demonstrate the effectiveness of the proposed method in generating sharper and more precise precipitation forecasts while maintaining satisfied meteorological evaluation metrics.

Recently, frequency-modulated continuous-wave (FMCW) radar-based hand gesture recognition (HGR) using deep learning has achieved favorable performance. However, many existing methods use extracted features separately, i.e., using one of the range, Doppler, azimuth, or elevation angle information, or a combination of any two, to train convolutional neural networks (CNNs), which ignore the interrelation among the 5-D time-varying-range-Doppler-azimuth-elevation feature space. Although there have been methods using the 5-D information, their mining of the interrelation among the 5-D feature space is not sufficient, and there is still room for improvements. This article proposes a new processing scheme of HGR based on 5-D feature cubes that are jointly encoded by a 3-D fast Fourier transform (3-D-FFT)-based method. Then, a CNN is proposed by building two novel blocks, i.e., the spatiotemporal deformable convolution (STDC) block and the adaptive spatiotemporal context-aware convolution (ASTCAC) block. Concretely, STDC is designed to cope with hand gestures’ large spatiotemporal geometric transformations in the 5-D feature space. Moreover, ASTCAC is designed for modeling long-distance global relationships, e.g., relationships between pixels of the feature at the upper left corner and lower right corner, and exploring the global spatiotemporal context, in order to enhance the target feature representation and suppress interference. Finally, our presented method is verified on a large radar dataset, including 19 760 sets of 16 common hand gestures, collected by 19 subjects. Our method obtains a recognition rate of 99.53% on the validation dataset and that of 97.22% on the test dataset, which is significantly better than state-of-the-art methods.

Multistatic geosynchronous synthetic aperture radar (GEO SAR) utilizes multiple satellites' transmitted and received signals simultaneously to generate multiple phase centers (PCs) and to reduce the synthetic aperture time and the power budget. Two key problems of this SAR system are the design of the PC array configuration to satisfy the resolution requirements and the suppression of the grating lobes that are inherently introduced by the PC array. The resolution of the SAR can be analyzed using generalized ambiguity functions (GAFs). However, the current research on the multistatic SAR resolution and grating lobes is mostly based on specified configurations and numerical simulation methods, which cannot establish the relationship between the system parameters and the resolution. This article proposes an analytical analysis method for the multistatic GEO SAR GAF-based on the array spatial ambiguity function (ASAF). First, gradient analysis is used to obtain analytical expressions for ASAF and the multistatic SAR GAF. On this basis, an analytical resolution expression is obtained, and an orbital element design method that considers the Earth's rotation is proposed. In addition, the lobe positions are analytically expressed based on the geometry, and the grating lobes are suppressed by designing the optimal integration time such that the null depth of the velocity ambiguity function (VAF) coincides with the grating lobe positions. Finally, simulation results at various positions for various orbit types demonstrate the accuracy of the GAF approximation formula and the satisfactory performance of the optimal time expression in suppressing the grating lobes.

Geosynchronous spaceborne-airborne bistatic multichannel synthetic aperture radar (GEO-SABM SAR) is a form of bistatic SAR constructed by a GEO transmitter and an airborne multichannel receiver. The frequent coverage of earth observations in GEO-SABM SAR provides a great advantage in SAR-ground moving target indication (SAR-GMTI). However, the bandwidth of the azimuth spectrum in this configuration, which is larger than the pulse repetition frequency (PRF), makes the whole spectrum fold into several subsegments, i.e., azimuth spectrum aliasing (ASA) occurs. In addition, azimuth Doppler ambiguity (ADA) caused by the larger radial velocity of the moving target may occur simultaneously. To address these problems, an improved velocity SAR (VSAR)-based azimuth spectrum reconstruction method for moving targets is proposed. The influence of ASA and ADA on the signal of the moving targets is analyzed, and a signal model after coarse imaging with static target parameters is deduced. From the signal model, each subsegment has different Doppler frequencies and can be extracted based on improved VSAR. Meanwhile, the Doppler ambiguous integer has a definite relationship with the slope of the range migration of the subsegments after a range cell migration correction (RCMC) based on the static target parameters and can be estimated by this relationship. Furthermore, based on the extracted spectrum subsegments and estimated Doppler ambiguous integer, the azimuth spectrum can be reconstructed. Finally, the effectiveness of the proposed method is verified by the numerical experiments.