Polar Format Algorithm (PFA) is effective for single-channel high-resolution synthetic aperture radar (SAR) imaging. However, to image for high-resolution and wide swath (HRWS) SAR, the traditional methods for multichannel (MC) SAR require an extra signal reconstruction process. When the signal reconstruction is combined with the PFA, some advantages of PFA, such as simple implementation and high efficiency, cannot be retained. In this paper, a PFA for MC SAR (MC-PFA) is proposed which avoids the extra signal reconstruction by a frequency-band reweighting interpolation (FBRI) proposed in this paper, thus retaining the simplicity and high efficiency of the traditional PFA. In the MC-PFA, the FBRI is combined with the azimuth interpolation in the traditional PFA. Compared to the azimuth interpolation of the traditional PFA, the combined processing can fulfill the range cell migration correction and the signal reconstruction simultaneously without additional interpolation. Furthermore, when the MC-PFA is combined with the Generalized PFA (GPFA), it can be further extended to MC sliding spotlight SAR and MC Terrain Observation by Progressive Scans (TOPS) SAR. Simulation experiments verify the effectiveness of the algorithm proposed in this paper.

Spaceborne multichannel (MC) synthetic aperture radar (SAR) can achieve high-resolution and wide-swath (HRWS) imaging. However, for an MC SAR system that does not satisfy the displaced phase center antenna (DPCA) condition, ghosts will appear in the image when the traditional back projection (BP) algorithm based on direct coherent integration is applied. To obtain the image without ghosts, an MC-BP algorithm based on time-domain channel weighting is proposed in this article. Compared with the traditional BP algorithm for single-channel signal, this algorithm just adds a channel-weighting factor in coherent integration. The channel-weighting factor is determined based on the channel index and ambiguity index, which is selected based on the instantaneous space angle of the pixel. Different from the existing MC imaging methods including two steps: signal reconstruction and imaging, the proposed method fulfills the image formation in one step and thus is simpler. Moreover, it can adapt the process of MC and multimode [stripmap, spotlight, sliding spotlight, and terrain observation by progressive scans (TOPSs)] data without any extra operation. To improve the efficiency of the MC-BP algorithm and overcome the defocusing issue caused by the Earth’s curved surface in the spaceborne geometry, a fast MC-BP algorithm based on a local spherical coordinate system, i.e., MC spherical Cartesian fast BP (MC-SCFBP) algorithm, is further developed. The spaceborne SAR simulation results with 0.1-m resolution are given to verify the effectiveness of this algorithm.

Due to the unsteady motion of the platform, airborne synthetic aperture radar (SAR) images are easily smeared by motion errors. In order to obtain a well-focused image, motion error compensation is essential and autofocus methods are used widely. Different from the conventional "indirect estimation" autofocus methods, a "direct estimation" autofocus method based on multi-subaperture interference is proposed in this paper. The concept of image interference is introduced into the autofocus method for the first time. The constant term of phase error can be obtained directly through multi-subaperture interference combined with the least squares method. This method avoids error accumulation caused by subaperture phase error combination and integration operations in conventional methods. Experimental results indicate the accuracy and effectiveness of the proposed method.

The wavenumber domain algorithm is an ideal solution for high-resolution and highly squinted (HRHS) synthetic aperture radar (SAR) imaging in the case of an ideal straight trajectory. However, for airborne HRHS SAR imaging, the Stolt mapping leads to nonsystematic range cell migration (NsRCM) and secondary range compression (SRC) for the HRHS SAR, which causes the HRHS SAR image to defocus severely. To obtain a well-focused HRHS SAR image, a new autofocusing algorithm for HRHS SAR imagery based on an equivalent broadside model is proposed in this article. After coarse motion compensation, linear range walk correction and azimuth resampling are employed to transform the HRHS SAR data into the equivalent broadside SAR data, which has been proved to greatly reduce NsRCM and SRC. After that, the motion error prior structure in the 2-D wavenumber domain is revealed. According to the structure, a novel 2-D wavenumber domain autofocusing algorithm is proposed by the relationship between the 1-D azimuth phase error and the 2-D wavenumber domain phase error correction. Finally, the well-focused HRHS SAR imagery is obtained. Experiments based on simulated and acquired data are carried out to verify the necessity and effectiveness of the proposed algorithm for HRHS SAR imaging.

For airborne synthetic aperture radar (SAR) imaging with high resolution and wide swath, the atmospheric turbulence may produce serious range-dependent (RD) motion error. To estimate the RD motion error, traditional methods usually first divide the range full-aperture data into multiple range blocks, and then use phase gradient autofocus (PGA) to estimate the phase error of all range blocks one by one, which is inefficient. In addition, the robustness of PGA is also affected by the number of strong scattering points. To solve these two problems, a new motion compensation (MoCo) algorithm is proposed to improve the efficiency and robustness of airborne SAR MoCo. The real data-processing results are given to verify the effectiveness of the algorithm.

Airborne synthetic aperture radar (SAR) with an imaging resolution of up to 0.03 m is developed. However, the imaging process suffers from motion errors with 2-D spatial-variant characteristics that invalidate approximations suitable for motion compensation (MOCO) in a submeter resolution SAR system. To estimate and compensate for 2-D spatial-variant motion error (2-D SVME), we propose a novel two-stage processing framework for the ultrahigh-resolution microwave photonic (UHR MWP) airborne SAR imaging. In the first stage, the two-step MOCO compensates for the spatial-invariant and range-variant motion errors. Range downsampling and azimuth windowing are adopted to increase the robustness of the method. Afterward, the coupling of the 2-D SVME is greatly decreased, and a coarse-focused image is obtained. In stage two, an extended autofocusing method in the 2-D wavenumber domain based on the extended range migration algorithm (ERMA) compensates for the azimuth-variant motion errors and nonsystematic range cell migration (NsRCM) for 2-D wide-swath stripmap SAR data. After the ERMA and obtaining the coarse-focused image, the analytical structure of the residual 2-D phase error in the wavenumber domain is revealed. A nonlinear scaling equation is developed, thus relating the 1-D azimuth phase error to the 2-D phase error correction. The Ku-band stripmap UHR MWP (0.03 m) airborne SAR data are analyzed to verify the necessity and effectiveness of the proposed framework. A well-focused stripmap SAR image is obtained.