Impulse ultrawideband (UWB) synthetic aperture radar (SAR) combines high-azimuth-range resolution with robust penetration capabilities, making it ideal for applications such as through-wall detection and subsurface imaging. In such systems, the performance of UWB antennas is critical for transmitting high-power, large-bandwidth impulse signals. However, two primary factors degrade radar imaging quality: (1) inherent limitations in antenna radiation efficiency, which lead to low-frequency signal loss and subsequent time-domain ringing artifacts; (2) impedance mismatch at the antenna terminals, causing standing wave reflections that exacerbate the ringing phenomenon. This study systematically analyzes the mechanisms of ringing generation, including its physical origins and mathematical modeling in SAR systems. Building on this analysis, we propose a Bayesian ringing suppression algorithm based on sparse optimization. The method effectively enhances imaging quality while balancing the trade-off between ringing suppression and image fidelity. Validation through numerical simulations and experimental measurements demonstrates significant suppression of time-domain ringing and improved target clarity. The proposed approach holds critical importance for advancing impulse UWB SAR systems, particularly in scenarios requiring high-resolution imaging.

With the growing demand for large-scale urban observation, multi-channel technology has become a cornerstone of high-resolution wide-swath SAR systems. The challenge of storing and transmitting the large data volumes generated by multi-channel systems has driven the development of advanced data compression techniques. However, in onboard implementations with non-power-of-two channel numbers and serial data formats, the existing multi-channel compression algorithms reveal significant conflicts involving channel counts, FFT cores, and the Krieger method. To address these issues, this paper introduces the Channel-Adaptive Range-Doppler domain filtering Serial Block Adaptive Quantization algorithm (CARDS-BAQ). By incorporating a point-frequency RD domain filtering approach and leveraging serial data matrix splicing and rollback combined with point-frequency ABAQ, CARDS-BAQ enables efficient data compression for arbitrary channel counts. The performance of CARDS-BAQ is validated using GF-3 measured data through comparative analysis with BAQ, ABAQ, MCBAQ, and 3MBAQ algorithms under power-of-two channel conditions. Additionally, its applicability and reliability for non-power-of-two channel numbers are demonstrated through payload flight experiments conducted in 2024 in Yingkou, Liaoning Province, China. CARDS-BAQ effectively supports data storage and transmission for large-scale urban observation, marking a significant advancement in remote sensing technology.

The measurement of the target azimuth angle using forward-looking radar (FLR) is widely applied in unmanned systems, such as obstacle avoidance and tracking applications. This paper proposes a semi-supervised support vector regression (SVR) method to solve the problem of small sample learning of the target angle with FLR. This method utilizes function approximation to solve the problem of estimating the target angle. First, SVR is used to construct the function mapping relationship between the echo and the target angle in beamspace. Next, by adding manifold constraints to the loss function, supervised learning is extended to semi-supervised learning, aiming to improve the small sample adaptation ability. This framework supports updating the angle estimating function with continuously increasing unlabeled samples during the FLR scanning process. The numerical simulation results show that the new technology has better performance than model-based methods and fully supervised methods, especially under limited conditions such as signal-to-noise ratio and number of training samples.

In ultra-wideband (UWB) synthetic aperture radar (SAR) imaging, in-band antenna standing waves (SW) can generate range ghosts, degrading image quality. To address this issue, an image-domain suppression method is proposed, leveraging the phase symmetry property (PSP) between the SW signal and its mirror SW (MSW) signal. Based on PSP, the MSW signal is rapidly constructed from the SW signal, ensuring that both share the same target region but exhibit different ghost regions. PSP is further extended to the image domain. Specifically, the SW-induced phase is extracted in the wavenumber domain. Based on the PSP, this phase is then used to construct the MSW signal, which exhibits a phase spectrum that is symmetric to that of the SW signal with respect to the origin. The MSW image is subsequently fused with the original SAR image, thereby effectively suppressing SW-induced ghosts. The experimental results demonstrate that the proposed method significantly mitigates ghosting while preserving the amplitude and structural integrity of the main signal, thereby enhancing overall imaging quality.

This study addresses the challenge of suppressing radio frequency interference (RFI) in ultra-wideband (UWB) synthetic aperture radar (SAR) operating within complex electromagnetic environments, and proposes an innovative time–frequency signal extraction method. The proposed approach integrates a low-rank and sparse representation (LRSR) model in the time–frequency domain with a time total variation (TTV) constraint. The core contributions are twofold: (1) constructing a time–frequency LRSR model of frequency modulation continuous wave (FMCW) signal, and (2) incorporating spectral continuity as a prior via TTV regularization into a joint low-rank sparse optimization framework. This effectively reduces the aliasing of RFI components into the target components caused by improper hyperparameters, which is particularly pronounced under low signal-to-interference-plus-noise ratio (SINR) conditions. To enhance robustness, the incoherence of interference across frequency bands is exploited, and a sub-band coherence factor (CF) weighting technique is introduced to further suppress RFI residues in the image domain. Experimental results demonstrate that the proposed method significantly outperforms existing robust principal component analysis (RPCA)-based techniques, offering a more adaptive and robust solution for RFI mitigation in UWB SAR systems.