Range ambiguity is a critical factor degrading the high-resolution and wide-swath (HRWS) imaging performance of spaceborne synthetic aperture radar (SAR), arising primarily from the antenna sidelobe characteristics. Recently, blind source separation (BSS) methods have shown promise in mitigating range ambiguity. However, existing studies have mainly focused on the determined scenario. In contrast, underdetermined cases are often more prevalent in practical settings. To address this gap, this article proposes a novel range ambiguity suppression scheme specifically designed for the underdetermined BSS (UBSS) scenario. Point and distributed targets simulation based on Sentinel-1 system is conducted to verify its effectiveness. The results indicate that for the point target imaging performance of two channels, peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR) are improved by an average of 6.62 and 9.47 dB, respectively. In the distributed target case, the separation and recovery of the echo signals in the target region achieve an average similarity (pixel, structure, and cosine metrics) exceeding 94.27%, and demonstrate robustness at signal-to-noise ratios above 25 dB. These findings provide insight into the feasibility of UBSS-based strategies for range ambiguity suppression and offer valuable reference points for future investigations involving single-channel implementations.

LuTan-1 (LT-1) is the first civil L-band synthetic aperture radar (SAR) satellite constellation and comprises two identical satellites. LT-1 is designed to fulfill two main requirements, one of which the main tasks is to provide digital surface model (DSM) products covering the areas that are not available using optical satellites. The second task is to provide deformation products to support the geohazard monitoring task. For the first task, LT-1 provides a novel noninterrupted imaging technology to facilitate phase synchronization. For the second task, the orbit maintenance is implemented using a newly proposed in-plane offset semimajor axis and out-of-plane control triggered strategy. Besides, we provide the system performance analysis for the two main tasks. Finally, the first results are produced with root-mean-square-error (RMSE) of the DSM less than 0.7 m in the flat region and 6.7 m in the mountainous region. The RMSE of the first deformation products is less than 2.7 mm in the test region. The results indicate a favorable potential for terrain mapping and deformation monitoring applications.

Synthetic Aperture Radar (SAR) demonstrates advantages in continuous, all-weather, high-resolution Earth observation. However, barrage jamming in both the time and frequency domains can overwhelm SAR signals, leading to a significant overlap between interference and target signals, thus making separation challenging. In particular, barrage interference from multiple jammers can render SAR images almost indecipherable, substantially diminishing the quality of SAR image products. This paper introduces a barrage jamming suppression method for multiple jammers utilizing elevation multi-channel SAR. By first performing blind source separation on the mixed echoes, signals from different imaging bands are extracted, followed by imaging and other processes. Simulation experiments have confirmed that this approach offers benefits such as low system complexity and effective jamming suppression.

Interferometric Synthetic Aperture Radar (InSAR) shift measurement plays a key role in image coregistration and absolute phase measurement and has significant applications in the InSAR processing workflow. However, the current shift measurement algorithms are limited by the relative bandwidth of the SAR system, resulting in low resolution and accuracy. SSENet is a recent InSAR shift measurement approach that utilizes deep learning to address these issues to some extent. This paper proposes a calibration method for SSENet, which introduces a lightweight neural network designed to refine the marginally biased output shifts. Furthermore, we demonstrate the performance of the refined SSENet algorithm and its potential in assisting phase unwrapping using LSAR-01 bistatic Synthetic Aperture Radar (SAR) data.

Benefiting from the rapid development of deep learning, the field of synthetic aperture radar (SAR) ship detection has been promoted with renewed vigor. However, due to the unique characteristics of SAR ship detection, several challenges have been encountered in the process of fusing SAR ship detection. On the one hand, some special properties of SAR images, including low resolution, small targets, dense ship inshore arrangements, and background clutter noise, increase the difficulty of detection; on the other hand, satellite detection requires a high degree of real-time and lightweight, and most traditional models fail to satisfy the requirements in two areas. After considering the above issues, we combine the advantages of space-to-depth convolution (SPDConv) and inception depthwise convolution (InceptionDWConv) to design SPD-InceptionDWConv (SIDConv), which resolves the above problems with fewer parameters and operations. Moreover, based on SIDConv, we also design light SIDConv (LSIDConv) and efficient SIDConv (ESIDConv) to further reduce the model complexity and to accelerate the detection process. With the above module as the core and YOLOv5 as the base, we propose a lighter and faster detector named LFer-Net, which has only 0.8M parameters and 1.9G FLOPs. Extensive experimental results on the SSDD and HRSID datasets confirm that our model is not only superior to other models with 98.2 and 90.6 accuracy but also achieves detection speeds of 144 and 96 frames per second (FPS), respectively.

The dual-frequency (DF) synthetic aperture radar (SAR) in lunar orbit, using L and S bands, is the only full-polarization (FP) SAR. It explores different layers of the moon, relying on the unusual rotational symmetry for polarimetric calibration. Addressing prior issues in polarimetric evaluation, this study tackles the challenges of an expanding dataset (now exceeding 900 scenes) and the necessity to evaluate polarimetric distortion (PD) values in the range direction. A novel polarimetric evaluation framework is introduced, enhancing existing methods. First, a scheme is proposed to eliminate low-quality areas in the range direction based on antenna isolation specifications, validated theoretically with simulated and real data. Second, more than 900 DFSAR scenes, downloaded prior to April 25, 2023, are utilized for evaluation, surpassing previous limitations and enhancing accessibility for users. By evaluating the L-band data, the image calibration results acquired by DFSAR between September 19, 2019, and December 4, 2020, have high accuracy and can be prioritized for lunar applications. In addition, we recommend that researchers pay attention to the issue of changing data quality for releases around March 2021.The amount of data in the S-band with the L-S-joint mode is small, and we provide the availability results directly. Third, the study applies the evaluated results to lunar polarization research, contributing to lunar exploration through polarimetric parameters. This comprehensive framework ensures accurate data utilization, addressing the evolving needs of lunar exploration with improved methodologies and expanded datasets.

Due to the large equivalent transmit crosstalk in hybrid quadrature polarimetric SAR system, traditional calibration methods using azimuthally symmetric distributed targets (AS-Targets) cannot work. In this manuscript, a preprocessing method of AS-Targets' covariance matrix is proposed, which makes equivalent crosstalk after processed become small to achieve system calibration through the AS-Targets. Simulation and airborne SAR data testing verify the reliability of the proposed method. The results show that the method presented in this manuscript can well calibrate the hybrid quadrature polarimetric SAR system using AS-Targets.

In previous publications, it is generally believed that the rotated buildings in built-up areas can induce polarization orientation angles, which cause overestimation of the volume scattering contribution. However, we found that the rotation effect of the double-bounce scattering wall-ground structures cannot be accurately expressed by an orientation angle. In this letter, dihedrals are used to simulate the wall-ground structures, and a novel technique for inversion of rotation angles in built-up areas is proposed. This letter is committed to directly inverting rotation angles rather than orientation angles. Then the rotation angles are compensated into the measured scattering matrix instead of deorienting the coherence matrix. C-band GF-3 and L-band ALOS-2 polarimetric data of San Francisco are used to verify the effectiveness of the proposed inversion technique.

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.

Range ambiguities severely degrade the performance of the synthetic aperture radar (SAR) system. To make an improvement without increasing the system complexity, antenna pattern synthesis is a feasible method. To this end, a novel algorithm that can shape the mainlobe and suppress the range ambiguities simultaneously by relaxing the problem as a semidefinite relaxation (SDR) programming problem is proposed here for the first time. Compared to the algorithm with global optimization, the presented algorithm could find the desired pattern without the large occupation of computing resources and has great potential in raising the performance of SAR system. In addition, some simulations are carried out, and the results prove that the proposed algorithm has the superior ability to suppress the range ambiguities while controlling the beamwidth and dynamic range ratio (DRR).

Hybrid-polarimetric architecture may become a new option for spaceborne quadrature-polarimetric (quad-pol) synthetic aperture radar (SAR) mainly due to the range ambiguity improvement without loss of polarization information. To inherit the analytical methods developed for the conventional quad-pol SAR data, it is necessary to transform the hybrid quad-pol SAR data into a linear polarimetric basis. However, this operation will result in deterioration of azimuth ambiguity performance. The theoretical analysis on range and azimuth ambiguities in quad-pol SAR has been relatively complete, which is also summarized in this article. Yet, there is no real data to verify the equivalence of polarization information and to compare the ambiguity levels. Based on P-band airborne experiment, real hybrid and conventional quad-pol SAR data are used for comparative verification. In addition, considering that polarimetric target decomposition is often used in the practical applications, we suggest a new perspective from target decomposition for the ambiguities in quad-pol SAR. Identical polarimetric processing results for two sets of comparative data verify that hybrid quad-pol SAR data are indistinguishable from conventional quad-pol SAR data. The demonstrated intensity images and decomposed RGB images with azimuth ambiguities, acquired by the two quad-pol modes, completely conform to the theory.

The innovative spaceborne Earth observation mission LuTan-1 (LT-1) deploys advanced full polarimetric L-band synthetic aperture radar (SAR) to obtain high-precision, multidimensional ground feature information. As the quad-pol SAR system, LT-1 suffers from strong ambiguities that degrade the quality of the observation products. Antenna pattern synthesis algorithm can suppress the range ambiguities without the increase of the azimuth ambiguities and the system complexity and therefore has great potential to improve the overall ambiguity performance of the quad-pol SAR system. However, the previous research on this kind of method is generally limited to specific situations and lacks of the analysis of actual measurement results. Based on the application requirements of LT-1, this article proposes a novel pattern synthesis algorithm that suppresses the range ambiguities via sequential convex optimizations. In simulation and comparison, the proposed algorithm effectively suppresses the strong co-polarized range ambiguities and shows the flexibility, efficiency, and stability that are significantly better than the previous algorithms. What is more, the analysis of practical performance loss is performed based on the measurement result of the LT-1 antenna, and the practicality and validity of the algorithm are strongly verified.

The bistatic synthetic aperture radar (BiSAR) plays an important role in remote sensing. However, the deviation between the two oscillators in BiSAR systems will cause a residual modulation of the echo signal. Therefore, the phase synchronization is an important issue that must be addressed in the BiSAR system. An advanced noninterrupted phase synchronization scheme is used for LuTan-1. The synchronization pulses are exchanged immediately after the ending time of the radar echo receiving window and before the starting time of the next pulse repetition interval, which will not interrupt the normal SAR operation. In order to evaluate the accuracy of the phase synchronization scheme, the model of phase synchronization is introduced at first. The hardware design and processing flow of LuTan-1 are introduced in detail. An innovative internal calibration strategy is also described. Then, the test data acquired by the ground validation system are analyzed to verify the effectiveness of the phase synchronization scheme. The signal-to-noise ratio (SNR) and the synchronization rate are the two most important factors to influence the accuracy in phase synchronization. The conclusions have guiding significance for the synchronization module design of LuTan-1 and the future BiSAR system.

An airborne P-band SAR experiment was conducted to detect targets in a forest in October 2019 and different results of target detection in the forest under snow-covered and snow-free states were obtained. When there is no snow on the branches, the targets in the forest are not easy to be seen in the image, but when the branches are covered with snow, the targets in the forest are clearly visible in the image. Three quad-pol data sets were used for this paper to compare and analyze the differences of target detection in the forest that Yamaguchi Four Component Decomposition and H/α unsupervised classification methods were used to explain. The results reveal that snow affects the scattering characteristics of the forest, and indirectly affects the penetration depth of the P-band electromagnetic waves into the forest, resulting in a large difference between the two detection results.

Phase synchronization is one of the key issues that must be addressed for the bistatic synthetic aperture radar (BiSAR) system. LuTan-1 (LT-1) is an innovative spaceborne BiSAR mission based on the use of two radar satellites operating in the L-band to generate the global digital terrain models in the bistatic interferometry mode. An advanced synchronization scheme is used for the LT-1 system. The synchronization pulses are exchanged immediately after the ending time of the radar echo-receiving window and before the starting time of the next pulse-repetition interval. Therefore, it cannot interrupt the normal SAR data acquisition, further improving the synchronization accuracy and avoiding the data missing effect. In this letter, a robust phase-error estimation and compensation method is proposed to improve the accuracy of the synchronization by using the Kalman filter. The test data acquired from the ground validation system of the LT-1 synchronization module are used to demonstrate the feasibility of the proposed scheme. The results validate the effectiveness of the proposed scheme and prove the promise for its future application in LT-1.

LuTan-1 (LT-1), i.e., TwinSAR-L, mission is an innovative spaceborne bistatic synthetic aperture radar (SAR) mission that is based on two satellites operating at L-band with flexible formation flying, which is planned to launch in 2020. The primary objective of LT-1 is to generate a highly accurate global digital elevation model (DEM). Beyond that, LT-1 will serve for the demonstration of some state-of-the-art technologies in radar field and some applications, such as biomass inversion, disaster forecasting, and climate and environmental monitoring, and phase synchronization is a technical challenge in realizing the highly accurate topography and deformation measurements. The pulsed alternate synchronization scheme, which is proposed to solve the synchronization problem of TerraSAR-X add-on for Digital Elevation Measurements (TanDEM)-X, is an efficient and accurate method. However, it interrupts the normal work of the TanDEM-X, accordingly flowing a series of problems. For LT-1, a novel synchronization scheme, in which the phase synchronization signal is exchanged by virtue of a time slot between radar signals, is applied. Thus, the working efficiency and synchronization accuracy can further be improved. Furthermore, the performance prediction and phase synchronization experiment for this synchronization scheme is presented, which verifies the feasibility of the proposed scheme. Finally, in order to guarantee the transmission quality of the synchronization signal, the illumination combinations and gain variation of the synchronization antenna in an orbital period are detailed.

LuTan-1 [(LT-1), i.e., TwinSAR-L] mission is an innovative spaceborne bistatic synthetic-aperture radar (BiSAR) mission focusing mainly on differential interferometry, which will be launched in 2020. This article introduces some important aspects of the LT-1 mission for the first time, including the formation configuration, imaging mode, application scenarios, and the efficient baselines between the master satellite and the slave satellite. To realize the high accurate topography and deformation measurements, a wide swath BiSAR focusing algorithm with phase reserving ability should be developed. This article proposes a modified bistatic range-Doppler algorithm based on 2-D principle of stationary phase spectrum, which reduces the phase error introduced by the root term expansion and has an excellent focus performance and a good phase reserving ability. Finally, the spaceborne bistatic simulation experiments using orbital parameters and imaging mode of the LT-1 mission, including point targets and scene targets, are utilized to illustrate the validity and accuracy of the proposed algorithm.

TwinSAR-L (Terrain Wide-swath INterferometric L-band SAR) is an innovative spaceborne bistatic SAR mission, whose primary objectives are achieving a global DTM dataset with high accuracy and observing the land deformation using differential InSAR technology within short revisit time.This paper presents a general methodology for the end-to-end raw data simulation for the spaceborne interferometry SAR systems, which requires correct terrain-mapped phase preserving, including the monostatic and the distributed systems. This paper describes the raw data generation method and its application in TwinSAR-L development phase.

Generally, synthetic aperture radar (SAR) system transmits linear frequency modulation (LFM) signal to obtain the high-resolution image and weighted windowing is usually employed to suppress sidelobes. However, it will cause a 1-2-dB signal-to-noise ratio (SNR) loss. Nonlinear frequency modulation (NLFM) signal, which can construct the signal's power spectral density (PSD) to reduce sidelobes without loss of SNR, is a promising candidate. However, the real-time generation of precise NLFM signal is still a technical challenge. In this letter, a high-precision NLFM signal generator with the ability of predistortion compensation is developed, and this signal generator will be employed in LuTan-1 (LT-1, i.e., TwinSAR-L) mission which is an innovative spaceborne bistatic SAR mission and planned to launch in 2020. In addition, a two-step error compensation method is developed to compensate the system error. Finally, the ground experiment is performed to validate the designed signal generator.

The intensive care unit (ICU) delivers care to critically ill patients with high resource intensity. Stochastic patient arrival and uncertain length of stay in ICUs present tremendous challenge to establish the admission and discharge policy to maximize the number of surviving patients and improve operational efficiency. In practice, ICU schedulers reserve some beds for potential patients with most critical conditions. Moreover, they prematurely discharge current ICU patients who are in stable status to accommodate for new and more urgent arrivals. We develop an analytical framework to quantify the impact of the number of reserved beds and suggest when to prematurely discharge current patients. A Markov decision process model is established to strike a balance between the rejection of incoming patient and the premature discharge in the near future. Monotonicity, concavity, and structural properties of the optimal control policy are presented. Using the inpatient record at a tertiary-level hospital in China, we conduct a case study and propose an effective threshold policy. Extensive numerical experiments are performed to analyze the effect of each parameter on the total survival benefits and compare different policies.