Synthetic aperture radar (SAR) is an advanced remote sensing technique, capable of observing Earth’s surface independent of weather conditions and sunlight illumination. Restricted by the minimum antenna area, however, conventional spaceborne SAR systems cannot achieve high azimuth resolution in a wide swath. In addition, blind ranges are present as the constant pulse repetition interval (PRI) is used. To solve these problems, a PRI-staggered elevation multichannel SAR (EMC-SAR) system is employed in this article. By transmitting the continuously PRI-varied sequence, the blind ranges are located at different regions in different receive instants, effectively avoiding the loss of coverage in elevation. In this system, three issues are required to be addressed: 1) recovering the missed data located at blind ranges; 2) suppressing range ambiguous components; and 3) restoring the PRI-varied signal into a regular grid. To deal with these problems, we propose a novel SAR imaging method for a PRI-staggered EMC-SAR system. To be applied on-ground, assume downlinking of the individual elevation channels. First, the modified$\varepsilon $-insensitive loss tube regression with the L2 regularization method is applied to recover the missed data. Then, the range ambiguous components are suppressed by performing digital beamforming (DBF) based on the elevation multichannel technique, where the covariance matrix is constructed by using an iterative adaptive algorithm. After that, a generalized scaling transform is employed to restore the PRI-varied signal into a uniform sampled grid. Finally, a well-focused SAR image can be obtained by performing the conventional SAR imaging techniques. The effectiveness of the proposed method is validated by both simulated and real SAR data processing results.

By transmitting the constant pulse repetition interval (PRI) sequence, the traditional synthetic aperture radar (SAR) system will suffer from the loss of coverage in elevation. Thus a staggered-SAR system is developed in recent years, which employs the continuously PRI-varied sequence, making the blind ranges locate at different regions in different receive instant. However, the nonuniformly sampled signal will cause ambiguities in the SAR imaging result. To deal with this issue, in this paper, a novel signal restoration method based on kernel regression is proposed to resample the nonuniform signal. Real-measured spaceborne SAR data is used to validate the proposed method.

Next-generation SAR imaging system demands wide-swath and high resolution to observe Earth's surface, promoting the development of the high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system working in terrain observation by progressive scans (TOPS). However, Doppler ambiguities will occur in this system, causing the imaging performance severely degrading. To address this issue, in this paper, a novel Doppler ambiguous suppression method for an HRWS-TOPS SAR system based on the orthogonal projection subspace is developed. The effectiveness of the proposed method is verified by both the simulated and real SAR data.

The spaceborne high-resolution wide-swath synthetic aperture radar (HRWS-SAR) system generally does not meet the optimal SAR imaging configuration, and thus, it is necessary to apply digital beam-forming filtering technology to restore the nonuniformly sampled signal into the uniform grids. However, in practice, because of the influences of temperature, receiving machine, and other error factors, the channel errors may possibly exist, causing the SAR image to be smeared. To address this issue, this letter proposes a novel algorithm based on the hybrid artificial fish school algorithm–glowworm swarm optimization–genetic algorithm (AFSA-GSO-GA) to address the channel imbalance issue. First, coarse HRWS-SAR imaging processing is performed to obtain the positions of the Doppler ambiguity components. Then, according to the designed cost function, the hybrid AFSA-GSO-GA algorithm is used to realize the channel phase error estimation. Finally, a well-focused SAR image can be obtained after channel balance. The effectiveness of the proposed method is validated by both simulated and real SAR data.

For a spaceborne high-resolution and wide-swath synthetic aperture radar (HRWS-SAR) system, it usually uses the digital beamforming technology. However, in practice, because of the influences of temperature, antenna pattern, receiving antenna, and other error factors, there may exist the range synchronization time errors, amplitude errors, and phase errors among different spatial channels. These nonideal factors will significantly degrade the multichannel data reconstruction performance, resulting in a smeared SAR image. To address this issue, in this article we propose a novel channel error correction algorithm based on the orthogonal projection theory. First, the optimal weight of each Doppler ambiguity component is calculated by the orthogonal projection. Then, the cost function is constructed based on the power maximization criterion, from which the channel phase errors can be obtained. Finally, the HRWS-SAR imaging can be finely realized after performing the channel balancing. Compared with the conventional phase error estimation method, the proposed algorithm does not require to perform the matrix eigenvalue decomposition, avoiding the signal leakage phenomenon under low SNR case. The effectiveness of the proposed algorithm is validated by both airborne and space-borne real SAR data.

The azimuth multichannel spaceborne SAR system can achieve the high-resolution and wide-swath simultaneously. In a high-resolution and wide swath (HRWS) SAR system, the Doppler centroid (DC) frequency estimation is an important step for Doppler ambiguity signal reconstruction, which should be precisely estimated. In this paper, we propose a novel Doppler centroid frequency estimation method for HRWS-SAR system based on modified shuffled frog leafing algorithm (MSFLA). Firstly, the cost function is designed based on the average power difference between ambiguities and useful signal. Then, the MLSFA algorithm is proposed to accomplish the DC estimation. Finally, a good HRWS-SAR image is obtained based on the estimated DC. Compared with the traditional time-domain based DC estimation methods, the proposed method is not affected by the troubled cross-terms induced by high-order moments. The effectiveness of the proposed method is verified by the simulation experiments and real-measured SAR data.