With continuous improvement of the synthetic aperture radar (SAR) resolution, the 2-D defocus effect in SAR image resulting from uncompensated motion error has made autofocus become a new challenging problem. Conventional 2-D autofocus approaches assume that the 2-D phase error is absolutely unknown and estimate them directly. Due to high dimensionality of the unknown parameters, these approaches often suffer from high computational burden and low estimate accuracy. In this paper, we analyze the effect of range migration algorithm (RMA) processing on the 2-D echo phase, and reveal the analytical structure of the residual 2-D phase error in RMA imagery. Then, by exploiting the derived prior knowledge on the phase error structure, an accurate and efficient 2-D autofocus approach is proposed. In the new method, only 1-D error, e.g., azimuth phase error, or residual range cell migration, is required to be estimated directly, while the 2-D phase error is computed directly from the estimated 1-D error by exploiting the analytical structure of the 2-D phase error. The experimental results clearly demonstrate the effectiveness and robustness of the proposed method.

Conventional two-dimensional (2-D) autofocus algorithms blindly estimate the phase error in the sense that they do not exploit any a priori information on the structure of the 2-D phase error. As such, they often suffer from the low computational efficiency and the lack of data redundancy to accurately estimate the 2-D phase error. In this paper, an efficient semi-blind 2-D autofocus algorithm which is based on exploiting the a priori knowledge about the 2-D phase error structure, is presented. First, as a prerequisite of the proposed method, the analytical structure of the residual 2-D phase error in SAR imagery is investigated in the polar format algorithm framework. Then, by incorporating this a priori information, a novel 2-D autofocus approach is proposed. The new method only requires an estimate of the azimuth phase error and/or residual range cell migration, while the 2-D phase error can then be computed directly from the estimated azimuth phase error or residual range cell migration. Experimental results clearly demonstrate the effectiveness and the robustness of the proposed method.

A new formulation of the polar format algorithm (PFA) for spotlight synthetic aperture radar (SAR) imaging is presented, which is useful when investigating its response to the moving target. Our work starts with examining the commonly adopted implementation of the PFA, i.e., the separable 1-D range and azimuth resampling procedures for their roles in range cell migration (RCM) correction, respectively. It is shown that the former performs RCM predeformation, while the latter is, substantially, the combination of RCM linearization and the keystone transform (KT), i.e., a well-known technique for SAR imaging of moving targets. The new formulation approach is applied to analyze the moving target. The far-field limit of the PFA for moving target focusing is derived, which can be used to predict the target's impulse response (IPR) function. The work presented might be helpful when considering a SAR system with the capability of ground moving target indication and imaging (GMTI&Im).

An alternate transmitting mode (ATM) is an important synthetic aperture radar (SAR) imaging mode as it can provide rich waveform design degrees of freedom to improve the system performance, especially to mitigate range ambiguities. However, the azimuth ambiguity issue caused by the differences between the autocorrelation functions of transmitted waveforms is ignored in existing studies. In this article, a deep understanding of the ambiguities in the ATM allows a correct evaluation of the ambiguity-to-signal ratio and the design of quasi-orthogonal nonlinear frequency modulation (NLFM) waveforms optimized for ambiguity suppression. Moreover, a novel azimuth compensation method is developed to remove the azimuth ambiguities caused by waveform diversity. Finally, detailed simulation experiments are carried out to verify the theoretical analysis.

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.

With the development of smart driving technology, Millimeter Wave (MMW) radar has become increasingly crucial in the automotive environmental sensing system. However, limited by the vehicle size, the MMW radar antenna length cannot meet the requirements of high-resolution imaging. Synthetic Aperture Radar (SAR) can solve this problem to some extent. But unlike airborne and spaceborne SAR, the near-field and curved trajectories in automotive MMW radar imaging are unavoidable. In this paper, we propose an imaging method based on sub-aperture Polar Format Algorithm (PFA) for curved trajectory imaging. Firstly, the beam width is narrowed by filtering. This technique can lower the system Pulse Repetition Frequency (PRF) requirement. Subsequently, the whole aperture is divided into several sub-apertures where PFA achieves high-resolution imaging in each sub-aperture. Finally, by rotating the sub-images and mosaicking them together, the whole scene imaging is completed. Besides, the paper also analyzes the factors affecting sub-image mosaicking. Based on the imaging results, we can conclude that the proposed method is effective in curved trajectory imaging.