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.

Multi-channel synthetic aperture radar (SAR) in azimuth can overcome the minimum-antenna-area constraint of the conventional SAR in high-resolution and wide-swath (HRWS) imaging. However, the SAR system suffers from amplitude and phase mismatch among channels and nonideal antenna pattern, which will result in azimuth ambiguity and ghost targets in the final image. Therefore, taking the nonbandlimited signal and channel errors into account, a practical azimuth ambiguity-to-signal ratio (AASR) model of multi-channel SAR system is established. Meanwhile, the baseband Doppler centroid (DC) frequency related to channel errors also has an influence on image quality. Then, an effective method is proposed to calculate the baseband DC frequency according to the jumping points of the channel phase errors estimate. Subsequently, considering the effect of azimuth antenna pattern (AAP), a corresponding relationship between the ideal steering vectors and the signal subspace from the decomposing covariance matrix is established. After that, based on the uniqueness of the signal subspace and the correct corresponding relationship, an accurate method is proposed to estimate the channel phase errors by minimizing the minimum mean square error (MMSE) of the signal subspace. Finally, an accurate multi-channel SAR imaging diagram is shown to effectively mitigate the azimuth ambiguous energy caused by channel errors. Simulation and real data experiments, including four channel airborne SAR data with a bandwidth of 210 MHz and the Chinese Gaofen-3 dual receiving channel (DRC) spaceborne SAR data, validate the effectiveness of the proposed calibration method, particularly in low signal-to-noise ratio (SNR).

Synthetic aperture radar (SAR), an active remote sensing equipment, shares the spectrum with devices in the same frequency band and is therefore easy to affect by pulse radio frequency interference (PRFI). The wideband version of PRFI, i.e., linear-frequency-modulation PRFI (LFM-PRFI), derived from the ground- and space-based radar sensors is a challenging issue for SAR because it usually has a large bandwidth and pulse width compared with the traditional PRFI. The well-known notch filtering methods, including time- and frequency-domain versions, are robust approaches against PRFI, which has been integrated into some SAR ground processing systems. However, the down-range frequency domain notch filtering will increase the sidelobes of the targets when mitigating wideband LFM-PRFI, whereas the time-domain version will introduce ghosts when notching large pulse-width ones. This paper proposed a filtering method that consists of three steps to tackle the above problems. The first step is focusing the energy of LFM-PRFI down-range and cross-range simultaneously, which can be done by match filtering and Fourier transform. The second step is mitigating the LFM-PRFI, which can be done by a designed filter. The last step is data restoration, which can be done by multiplying the conjugate of the phase function used before. Numerical experiments based on simulated SAR data and measured spaceborne SAR data acquired by European Sentinel-1 are performed to test the mitigation performance, which verified the effectiveness and superiority of the proposed approach.

A novel imaging algorithm is presented in this article for focusing the very high resolution spaceborne synthetic aperture radar (SAR) data of spotlight mode. The long synthetic aperture time in spotlight mode brings new problems, such as Doppler spectrum aliasing and curved orbit. The imaging results will be filled with ambiguity and would suffer from resolution reduction if Doppler spectrum aliasing is not handled. The error introduced by curved orbit will degrade resolution of the target and introduces asymmetric sidelobes in azimuth direction. The start–stop approximation is no longer applicable, since it introduces two effect, e.g., “fast-time” effect and “slow-time” effect, which will cause defocusing and range-dependent azimuth shift, respectively if not handled. The proposed algorithm combines the two-steps processing approach (TSPA) and the backprojection algorithm (BPA). First, the initial step of TSPA is used to get a high azimuth sampling rate which is higher than pulse repetition frequency to avoid the Doppler spectrum aliasing. Then, the “fast-time” effect of start–stop approximation is corrected in the 2-D frequency domain. Finally, the BPA is used to correct the error introduced by the curved orbit and the “slow-time” effect of start–stop approximation. The experimental results of spaceborne SAR data acquired by Gaofen-3 (GF-3) SAR system demonstrate the feasibility of the proposed algorithm.

The 3-D structure of forests is an important indicator for evaluating forest health and can provide data support for ecological monitoring and protection. Synthetic aperture radar (SAR) tomography (TomoSAR) is an important technology for forest structure estimation using the multibaseline (MB) SAR data stacks. The spectral estimators, such as the Capon method, are usually used to estimate the 3-D reflectivity along elevation direction. In this letter, a modified Capon (M-Capon) method is proposed to improve the estimated profiles of ground and canopy scatterers in the elevation direction, including the estimation accuracy and resolution. This method combines the ideas of the CLEAN algorithm with the Capon method and uses iteration to improve the resolution and accuracy of the estimation. The effectiveness of the M-Capon method is demonstrated using the simulated data and the MB SAR data acquired by the P-band airborne SAR system over the Saihanba Forest Farm in Hebei, China.

In the multichannel synthetic aperture radar (SAR) systems, unavoidable channel errors will significantly degrade the performance of the ambiguity suppression. To address this problem, a channel phase error estimation method is proposed in this letter. First, the multichannel echo model and the weighted backprojection algorithm are introduced. The channel phase errors are then estimated by maximizing the image intensity using the gradient descent method. The simulation results confirm that the proposed method has higher accuracy and robustness than the orthogonal subspace method. The airborne SAR data acquired by a four-channel strip map C-band airborne SAR system demonstrate the feasibility of the proposed method.