Azimuth multichannel (AMC) technology is one of the mainstream technical approaches to realize high-resolution wide-swath (HRWS) imaging. It has been successfully applied to several synthetic aperture radar (SAR) satellites in orbit. However, the inevitable imbalance between channels can seriously affect the azimuth reconstruction spectrum, introducing ghost targets into the final imaging results and degrading the SAR image quality. In order to address this issue, this paper proposes a channel imbalance estimation method based on minimizing the sum of the sub-band norm (MSSBN) for the reconstructed azimuth spectrum. First, the amplitude imbalance is calibrated in the range-Doppler domain. Then, the echo in each channel with phase imbalances is reconstructed by filters separately and converted to the range-Doppler domain. Finally, the global optimization algorithm is used to find the phase error of each channel so that the reconstructed postcompensation spectrum has the smallest sub-band spectrum norm sum. By two-dimensional blocking, this method can also estimate the space-varying phase imbalance in the range dimension and the time-varying phase imbalance in the azimuth dimension. Experimental results using simulated and actual AMC SAR data from the GF-3 system validate the proposed algorithm’s high estimation accuracy and excellent computational efficiency.

The LuTan-1(LT-1), known as the L-band differential interferometric synthetic aperture radar (SAR) satellite system, is an essential piece of civil infrastructure in China, providing extensive applications such as surface deformation monitoring and topographic mapping. To achieve high-resolution and wide-swath (HRWS) observation abilities, the LT-1 takes the dual receiving antenna (DRA) imaging mode as its working mode. However, amplitude and phase errors between channels lead to a mismatch between the reconstruction filter and the multichannel echo signal, worsen the reconstructed azimuth spectrum, and introduce ambiguity targets in the final imaging results, seriously affecting the final imaging quality. In order to better evaluate the channel error and azimuth ambiguity performance of the LT-1 system, this paper proposed an advanced channel consistency correction method and conducted many measured data experiments. The experimental results show that the proposed method is effective, and the LT-1 system has excellent channel error control and azimuth ambiguity performance.

Synthetic aperture radar (SAR) plays a significant role in electronic reconnaissance due to its high-resolution imaging capabilities. Recognizing the operating modes of spaceborne SAR systems is crucial for implementing targeted jamming measures to safeguard information security. However, existing methods do not fully exploit the operating mode information contained in received signals, and the range of SAR operating modes they can recognize remains limited. To address these issues, this paper reformulates the spaceborne SAR operating mode recognition problem as a video recognition problem and proposes an axial attention and motion extraction network (A²-MENet) based on azimuth time-frequency amplitude sequences (ATFASs). The distinctions between different operating modes are summarized as variations in the power intensity and Doppler histories of the received signals. The ATFASs, which can simultaneously reflect these two characteristics, are selected as the input for A²-MENet. A simple multi-receiver deployment scheme is proposed to ensure the acquisition of sequential ATFASs. An axial attention module (AAM) is introduced to model the variation trends in received power. Meanwhile, a motion extraction module (MEM) is designed to capture multi-scale Doppler motion information through differential extraction. Finally, to facilitate the recognition of spaceborne SAR operating modes, a new dataset, SAR-ATFSet, is simulated. Extensive experimental results on both our dataset and real measurement data show that A²-MENet outperforms existing methods in accuracy and robustness.