The missile-borne SAR works in the forward-squint state, and a higher flight speed will cause a very large relative radial velocity between the target and the antenna phase center (APC). The classic polar format algorithm (PFA) and its improved version do not effectively compensate for the intra-pulse Doppler history resulting from large radial velocity, which leads to defocused images. Therefore this paper proposes a modified version of the PFA, which can solve such image defocus. Compared with the classical PFA, the modified PFA has the same advantages of simplicity and efficiency while implementing higher image quality by compensation of the residual phases caused by large radial velocity. The modified PFA is suitable for hardware implementation, since only fast Fourier transform (FFT) and complex vector multiplications are required in the range dimension processing. In this paper, simulation examples are employed to verify the validity and advantages of the modified PFA.

Ground armored weapons have a high detection value in military operations. Satellite synthetic aperture radar (SAR) cannot accurately detect military targets with meter-level sizes limited by resolution of sensors. Airborne SAR have strict experimental conditions and cannot be applied in actual battlefield environments. MiniSAR sensors, which combine the advantages of submeter-level ultrahigh resolutions and flexible flight, play a crucial role in recognizing military targets. In this paper, various small military targets in real complex ground scenarios are detected with the MiniSAR of NUAA. However, there are still two difficulties. First, because of a limitation in the number of flight circles, the number of obtainable military target samples is not sufficient to adapt to the traditional deep learning methods that rely on a large number of image samples. Second, due to the imaging systems and different depression angle of MiniSAR, the SAR images of MiniSAR suffer from the same deformation challenge as the moving and stationary target acquisition and recognition (Mstar) with high depression angle. To address these two challenges, we propose a FASAR-Net framework based on few-shot learning with meta learning and adversarial domain learning, combined with the inherent scattering features of the SAR targets. Furthermore, we validate the reliability and accuracy of this algorithm on Mstar and our datasets, and the result of recognizing small SAR targets is compared with our algorithm and other classical algorithms. We conclude that the proposed algorithm has high accuracy in the recognition of the deformation small targets under the few sample condition.

Barrage jamming technique with controllable jamming coverage against synthetic aperture radar (SAR) systems is of great importance in electronic countermeasures. However, it is still a difficulty for the jammer to accurately impose controllable two-dimensional (2-D) local jamming on the regions of interest (ROIs). In this respect, a new parameterized and large-dynamic-range precise controllable (PLDR-PC) jamming method has been proposed in this paper to assist in solving such problems. Based on the SAR imaging properties of linear frequency modulation (LFM) case, the range and azimuth modulation factors have been well designed to generate large dynamic controllable coverage of jamming signals with high 2-D processing gain. In such a context, the PLDR-PC technique can provide the optimal power allocation and considerably reduce the jamming power while still ensuring the satisfactory performance. The proposed PLDR-PC technique can improve the jamming efficiency and considerably reduce the exposure probability of the jammer. Moreover, to improve the barrage jamming performance, the parameter estimation error model is also established to determine the simple yet valid jamming strategy in practical implementations. Finally, extensive numerical simulations in comparison with the current jamming methods have been carried out to demonstrate the effectiveness and prospect of the PLDR-PC technique against airborne/spaceborne SAR systems.

The wavefront curvature error (WCE), which is caused by the planar wavefront assumption in the polar format algorithm (PFA), induces serious degradation to the formed image, especially for the bistatic synthetic aperture radar (BSAR). The existence of a double hyperbola in the bistatic range equation makes it difficult to derive the exact analytical expression for the WCE in the wavenumber domain. Actually, the distortion derives from the first-order WCE and the defocus mainly comes from the quadratic WCE. Moreover, the residual odd-order WCE also affect the main-lobe of the target and the residual even-order WCE lead to the problem of sidelobe asymmetry. To overcome the issues, in this paper, the first-order one-dimensional (1D) Taylor expansion is adopted to effortlessly separate the distortion error and the residual error. The proposed separation strategy can compensate the residual high-order error and maintain the image continuity. In addition, to avoid the interpolation error, which is induced by the wavenumber nonuniformity, the nonuniform fast Fourier transform (NUFFT) is adopted to construct the phase filter. The effectiveness of the proposed algorithm is demonstrated by simulated experiments.

The multirotor drone based miniature synthetic aperture radar (MiniSAR) has attracted attentions due to the low costs and flexible capacities. The circular trajectory enables it with the capability of 360° observation of the region of interest (ROI). However, the focusing ability of time-domain based imaging algorithms mainly depend on high precision inertial navigation system (INS), which is not available for the finite load of MiniSAR. In addition, in dealing with the problems of motion error compensation and sub-images registration, the present time-domain based noncoherent imaging algorithms rely on preset calibrators or isolated strong point-like scatterers in the observed scene, which restrict its practical applications. From the efficiency and practicability points of view, this paper presents a polar format algorithm (PFA) based noncoherent imaging strategy for multirotors borne circular MiniSAR. The effectiveness of the proposed method is verified by the real data experiments.

Wavefront curvature effects grow worse in spotlight Bistatic synthetic aperture radar (BSAR) imagery when reconstructed via polar format algorithm (PFA) under the large-scale scene. The wavefront curvature error, which causes geometric distortion and defocuses to the image, is induced by the faulty hypothesis of the planar wavefront. Due to the approximated or unsegmented wavefront curvature error, conventional compensation algorithms experience different degrees of performance restriction. In this letter, the geometric distortion error and the intact defocus error are separated and analyzed for the first time. Based on the separated phase error model, a modified space-variant post-filtering (MSVPF) algorithm is proposed to correct the wavefront curvature effects of the PFA image. Two major contributions of this algorithm are as follows. First, as no high-order term is ignored in the constructed space-variant filter, the proposed algorithm can compensate for the defocus error with any order. Second, MSVPF employs a separate strategy to correct the space-variant defocus and geometric distortion, which avoids high overlap rate in subimage processing and maintains the computational efficiency of the original SVPF. The effectiveness of the proposed algorithm is demonstrated by numerical simulations.

Based on the principle of planar wavefront assumption, a novel point of view in processing the spotlight synthetic aperture radar (SAR) data is proposed in this letter. After match filtering and motion compensation to the scene center, from the perspective of range cell migration correction (RCMC), two scaling transforms (a range-frequency scaling with a subsequent azimuth-time scaling) are proposed to correct the range cell migration caused by the coupling between the range-frequency and azimuth-time. Moreover, to reduce the spectrum loss (the loss of image resolution), two constant scaling factors are introduced to optimize the range-frequency and azimuth-time scaling transforms, which enable the presented algorithm to realize the flexible and efficient imaging ability. Simulation results are conducted to validate the efficacy of this algorithm.