Synthetic aperture radar (SAR) raw data simulators (RDSs) play a critical role in system design, mission planning, and algorithm evaluation, particularly for emerging imaging modes such as frequency-scanning (F-SCAN) SAR. However, existing simulators are often either computationally inefficient or unsuitable for this mode. To address these limitations, a dedicated RDS for F-SCAN SAR is first proposed, featuring detailed mathematical derivation, explicit modeling of antenna pattern effects, and a computational complexity analysis. Simulation experiments validate the proposed approach, demonstrating an efficiency improvement of 97.4% compared with the conventional time-domain method, while maintaining root-mean-square amplitude and phase errors on the order of 10-3. These results confirm that the proposed simulator is both efficient and accurate, providing a practical tool for F-SCAN SAR research.

Time synchronization and phase synchronization are indispensable for spaceborne bi-/multi-static SAR systems. The LuTan-1 (LT-1) bistatic SAR system employs a time synchronization scheme combining pulse-per-second (PPS) signals with GNSS-disciplined oscillators (GDOs), along with a novel non-interrupted alternate pulse (NIAP) scheme for phase synchronization. Furthermore, the time synchronization accuracy can be further enhanced by applying the two-way time transfer (TWTT) technique based on synchronization data from the NIAP scheme. To accurately evaluate the synchronization performance of the LT-1 system during in-orbit operation, an assessment method based on the two-dimensional (2D) positional offset of calibration targets is proposed. The method compares the theoretical offsets derived from imaging geometry with the measured offsets extracted from monostatic and bistatic SAR images to determine the system’s time and frequency deviations. Using 16 repeat-pass acquisitions over the calibration field in Hami, Xinjiang, during LT-1’s bistatic operation phase, the in-orbit synchronization performance of LT-1 is demonstrated. The PPS+GDO scheme achieves time synchronization accuracy under 100 ns, while the TWTT algorithm enhances this metric by three orders of magnitude, which is better than 0.10 ns. After NIAP phase synchronization, the LT-1 system exhibits a frequency deviation of approximately −2.17 × 10−3Hz, corresponding to frequency stability of 2 × 10−12.

LuTan-1 is the first spaceborne L-band bistatic Synthetic Aperture Radar(BiSAR) constellation in China aimed at highly accurate digital elevation model(DEM) and surface deformation monitoring. During the in-orbit commissioning phase, the constellation performed periodic observations over the Hami region in Xinjiang, China, while flying in a controlled helix formation. This paper preliminarily elucidates the capability and the stability of Raw DEM generation utilizing LuTan-1 BiSAR interferometric images. Interferometric processing procedures, including initial baseline estimation and absolute phase estimation, were implemented without the use of external ground control points(GCPs). In comparison with GCPs, the results showed that the mean error and the root mean square error (RMSE) of the eight Raw DEMs exhibit close alignment, accompanied by a small standard deviation, validating the excellent stability and capability of LuTan-1 Bistatic SAR.

Azimuth ambiguity in synthetic aperture radar (SAR) images results from the limited azimuth sampling rate and seriously affects image quality and normal applications. Multiple algorithms for azimuth ambiguity elimination have been proposed. However, most algorithms adopt a single-channel model and ignore the effect of additional reconstruction operations on ambiguity in azimuth multichannel systems (MCSs). In this letter, the particularity of azimuth ambiguity for MCSs is analyzed theoretically. A refined ambiguity suppression scheme based on multichannel cancellation (MCC) and ambiguity refocusing is proposed. The ambiguity region is first located through the comparison of multi-look image pairs. Then the interference image for ambiguity extraction is cancellated through MCC. Consequently, the ambiguous image is refocused and extracted. Finally, the ambiguity is reversed to the echo domain to handle the special replicated ambiguity problem for MCSs. With the proposed scheme, ambiguity suppression performance has improved for MCSs. The validity of the proposed algorithm is further verified through LuTan-1 real data.

The azimuth multichannel synthetic aperture radar (SAR), as one of the mainstream technologies for achieving high-resolution and wide-swath (HRWS) imaging, has been successfully employed in several on-orbit SAR missions. However, the unavoidable phase errors among channels result in azimuth ambiguity, deteriorating the recognizability of targets in SAR images. Additionally, the radio frequency interference (RFI) exacerbates the difficulty of phase error estimation. To address this issue, a phase error calibration method based on least spectrum difference (LSD) is proposed. Firstly, the multichannel signals are reconstructed using the linear mapping form of the reconstruction algorithm. Secondly, the objective function is established based on the continuity of the azimuth spectrum, by which only the signals near the discontinuity points are proposed. Finally, the optimal estimation of the phase differences can be obtained after iteration. In LSD method, the RFI to the objective function is mitigated due to the operation in the range-Doppler domain, and the iteration is proposed only using a few data near the discontinuity points thus saving much computational cost. Experimental results based on the simulated data and real bistatic echoes of the LuTan-1 (LT-1) mission validate the superiority of the proposed LSD method.

Range ambiguity is a technical challenge due to the deterioration of the image quality in the multistatic synthetic aperture radar (SAR) constellations. This article proposes an effective range ambiguity suppression scheme based on multiechoes coherent processing. First, a general signal model impacted by range ambiguity is built up based on the geometry of the multistatic SAR constellation, showing the different phase characteristics of the desired and ambiguous signals between the satellites. Then, an effective processing scheme for range ambiguity suppression is proposed based on the different phase characteristics, and the corresponding ambiguity suppression performance (ASP) is analyzed accordingly. This scheme can achieve a coherent summation of the desired signals by compensating for the corresponding phase difference, while the ambiguous signals are summed incoherently. Therefore, the range ambiguities can be suppressed without increasing the SAR instrument complexity. Finally, the system and imaging simulation results are provided to validate the theoretical analysis of the ASP and demonstrate the effectiveness of the proposed scheme, respectively.

With the growing scarcity of spectrum resources, frequency sharing among different systems is becoming quite common, which causes radio frequency interference (RFI) to normal SAR applications. Recently, in the RFI suppression field, algorithms based on the low-rank property (LRP) assumption have become a popular research topic. However, this assumption is not valid in complicated cases, especially when there is a wide variety of RFIs. To address this problem, in this paper, factors that affect the LRP are studied through theoretical analysis and simulation verification, a general RFI model is established to fit complicated cases, and an advanced RFI suppression scheme based on low-rank property restoration (LRPR) is proposed. The LRPR scheme has three steps. The first step is RFI localization, which captures the rough profile of the RFI in the time-frequency domain for RFI spectrum separation and extraction. The second step is RFI clustering, in which a novel similarity evaluation metric and corresponding two-step clustering algorithm are designed. The final step is RFI suppression. Operations for LRP recovery in each RFI group are proposed, consequently, classical low-rank approximation methods are applied for RFI suppression. The simulation results for various types of RFI show the robustness and performance improvement of the proposed scheme. In addition, in LuTan-1 data processing, the proposed scheme outperforms classical algorithms in both intensity image recovery and phase preservation.

Benefiting from the wide coverage and high resolution of synthetic aperture radar (SAR) data, interferometric SAR (InSAR) has significant advantages in wide-area deformation detection and monitoring. In order to improve computational efficiency and save computational resources, it is expected to perform the deformation area identification first as accurately as possible, and then perform local time series inversion for the specific deformation area. However, with the interference of tropospheric delay, especially its systematic component, the accurate identification of the deformation area becomes challenging. To address this issue, we propose a two-step tropospheric delay removal method including time domain correction and spatial domain correction. The time domain correction is used to avoid the effect of the systematic component of tropospheric delay so as to derive an accurate deformation rate map. The purpose of the spatial domain correction is to finely remove the effect of tropospheric delay in local area to recover the correct deformation time series. Applying our method, external data based method and existing classical SAR data based method to the reservoir area of the Lianghekou hydropower station with Sentinel-1A ascending data for comparison, the results demonstrate the advantages of our method in deformation area identification and deformation monitoring.

Phase synchronization plays a crucial role in the LuTan-1 (LT-1) bistatic synthetic aperture radar (BiSAR) system, as it aims to eliminate additional azimuthal phase modulation caused by oscillator differences. However, for the pulse alternating transmission system operating in the L-band, the presence of radio frequency interference (RFI) poses an inevitable challenge. Serious RFIs introduce phase jitters, compromising the accuracy of synchronization. In this letter, an effective method for detecting and removing phase jitters is proposed to enhance synchronization accuracy. In the proposed method, the jitter features are separated by the iteratively reweighted least squares (IRLS) in the instantaneous frequency domain based on the established synchronization phase model. Then the jitter positions are detected by correlated peaks between the designed convolutional kernels and jitters. Finally, a polynomial model is utilized to remove jitters and assist in phase unwrapping. The synchronization phases acquired by the LT-1 mission are used to verify the feasibility of the proposed algorithm. The improved imaging quality demonstrates the effectiveness of the proposed method and confirms its ability to ensure the high-precision generation of the LT-1 BiSAR images.

The innovative bistatic synthetic aperture radar (BiSAR) mission LuTan-1 (LT-1) uses a noninterrupted synchronization scheme to achieve high-precision phase synchronization. While radio frequency interference (RFI) is a major factor in deteriorating phase synchronization performance. To present the effect of RFI on the synchronization phase clearly, the characteristics of RFI in the synchronization link are described in detail, and a precise analytic expression between the amplitude, frequency, and phase of RFI and synchronization phase error is established. Furthermore, a novel pulse-compression-based notch (PCN) method is proposed to eliminate the phase error introduced by RFI. In the proposed method, the saturated distortion signals resulting from strong interferences are detected and discarded by the distribution features of their modes. Then, inspired by contrary thinking, the synchronization signal after pulse compression is notched instead of RFI. A fast missing data iterative adaptive approach (Fast MIAA) is performed to recover the gaped RFI signal and remove it from the compressed signal. Finally, the correct synchronization phases can be extracted from the peak positions of the remaining signals. Experimental results derived from using simulated and real synchronization data of the LT-1 system validate the performance of the proposed RFI mitigation method.

This paper proposes an advanced radio frequency interference (RFI) mitigation scheme for the phase synchronization of the bistatic SAR (BiSAR) system based on the blind source separation (BSS) technique, by which narrowband and wideband RFI can be suppressed significantly. First, the RFI signal model is built up and the impact of the RFI on the phase synchronization is analyzed accordingly. Second, a phase synchronization system with multiple receiving channels and the corresponding blind identification diversity (BID) method are proposed to separate the synchronization signal from the contaminated data with less signal loss. In BID, a novel peak detection algorithm is presented to solve the inherent ambiguity of the BSS. Finally, the simulation and experimental results based on the BiSAR system LuTan-1 are presented to validate the advantages of the proposed scheme.

An advanced sparse multi-channel system is proposed for spaceborne digital beamforming synthetic aperture radar (DBF-SAR), which can suppress pulse extension loss (PEL) and frequency dispersion loss (FDL) without increasing the computational load and system complexity. First, conventional scan-on-receive (SCORE) technique is reviewed and a matching ratio (MR) is proposed to evaluate the mismatch between the formed beam pattern and the pulse signal amplitude. To mitigate the PEL and FDL, the novel sparse SCORE (S-SCORE) based on the optimization of the sparse channel distribution is proposed. The impact of sparse channel distribution is analyzed and the method to optimize the distribution based on the maximized MR is proposed accordingly. Finally, the results of simulations and experiments are provided to demonstrate the superiority of the proposed S-SCORE technique. The work in this paper can be seen as an important candidate for future spaceborne DBF-SAR.

In an azimuth multichannel synthetic aperture radar (SAR) system, the unavoidable channel error will result in virtual targets or azimuth ambiguity, which significantly degrades the quality of high-resolution and wide-swath (HRWS) SAR image. Calibration of the imbalances between channels has been an important topic. First, to present the channel error effect on the final image clearly, a precise relation between the amplitudes of virtual targets and amplitude–phase error is established by matrix trace, which applies to the case of a certain amplitude–phase error. In addition, the total amplitude of all targets represented by the${L^{1}}$-norm reaches the minimum when there is no channel error. Based on this principle, an image-domain least${L^{1}}$-norm method is proposed to estimate the phase error between channels. By utilizing the focused high signal-to-noise ratio (SNR) images, the proposed algorithm achieves high estimation accuracy. Moreover, no redundant channel is required in the proposed algorithm compared with the subspace-based method. Finally, the effectiveness of the proposed channel error effect analysis and calibration method is validated by both the simulated and real SAR data.

Interferometric synthetic aperture radar (InSAR) is an efficient tool for global large-scale digital elevation model (DEM) generation. However, for steep terrain, the current approaches cannot stably reconstruct valid DEM products from a single-baseline InSAR image pair without an external reference DEM due to the influence of shadow/layover geometries, phase noise, and the Itoh condition limitation in the phase unwrapping (PU) process. In this article, a novel stereo-radargrammetry-assisted PU (SAPU) approach with no need for external auxiliary information is proposed to eliminate the constraint of the Itoh condition by exploiting the internal stereo-radargrammetric shifts. The method reduces the phase gradient in the interferogram and guides the PU process with automatically selected tie points. Notably, the current stereo-radargrammetry approaches will deteriorate to an incoherent state in steep terrain, hampering the reliability and accuracy of SAPU. Accordingly, we also propose an adaptive weighted subwindow-coherent stereo-radargrammetric shift estimation (AWS-CSE) method to improve the accuracy of subpixel shifts by introducing local topographic phase consistency in coherence estimation. We quantitatively validate the performance of the proposed methods based on the L-SAR 01 simulation data and three pairs of repeat-pass single-baseline Advanced Land Observing Satellite (ALOS) phased array type L-band synthetic aperture radar (PALSAR) images from different areas, comparing the results with those of various traditional and deep-learning-based PU methods. The findings suggest that the proposed methods can generate accurate DEMs from single-baseline measurements in steep terrain while avoiding additional data acquisitions.