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As a crucial geological, climatic, and ecological boundary in the southeastern margin of the Tibetan Plateau (SEMTP), the topographic evolution of the Diancang Shan (DCS) remains unclear due to the lack of direct constraints on its paleoelevation. Here, we quantitatively reconstructed changes in annual mean temperature (ANNT) based on palynological data from the terrestrial Dasongping section (~7.6–1.8 Ma) in the Dali Basin, located at the northeastern margin of the DCS in Yunnan Province, China. Integrating the thermochronological data from the eastern and southern margins of DCS, we have clarified the paleotopographic evolution of DCS during this period: the paleoelevation of DCS likely exceeded 2000 meters above sea level (m a.s.l.) due to initial normal faulting at ~7.6 Ma, possibly comparable to the current average elevation (~2200 m a.s.l.) of surrounding Dali Basin region. Significant growth occurred between ~5.0 Ma and ~3.5 Ma, with at least ~1000 meters uplift gain in the northern segment and up to ~2000 meters in the southern segment of DCS, caused by the intensification of normal faulting activities. Finally, the northern segment of DCS reached the elevation of ~3500 m a.s.l. after ~1.8 Ma. Our findings suggest that the quantitively ANNT reconstruction, combined with thermochronological and sedimentary data, can significantly improve constraint on the paleotopographic evolution of DCS. [ABSTRACT FROM AUTHOR]

In recent years, jamming strategies for Synthetic Aperture Radar (SAR) pertaining to target detection and identification, such as the creation of false targets, electromagnetic (EM) deception, and signal spoofing, have been increasingly emphasized. Distinct from traditional SAR jamming approaches, the introduction of an innovative artificial material cloak in SAR target jamming presents augmented capabilities. These methods demonstrate a proficient redirection of incident EM waves in specific or arbitrary directions, effectively masking the vital information linked to critical targets. This study introduces a broadband SAR target jamming system employing an information metasurface that incorporates intelligent information processing algorithms in conjunction with a space-time-coding digital metasurface, endowing it with the capacity to adeptly modulate incident EM waves. This integration facilitates a versatile approach to jamming, enabling the deployment of multi-mode protective measures against critical targets. The conducted simulation and experiment results validate the system's ability to adjustably produce EM deception and generate multiple false targets independently of the SAR system. The outcomes of this research significantly advance the practicality of SAR protection strategies, pushing the boundaries toward more dynamic, broadband, and controllable scenarios, thereby substantially improving the concealment of critical targets in highly sensitive conflict areas. [ABSTRACT FROM AUTHOR]

High-resolution and high-squint spaceborne spotlight synthetic aperture radar (SAR) has significant potential for extensive application in remote sensing, but its swath width effectiveness is constrained by a critical factor: severe range cell migration (RCM). To address this, pulse repetition interval (PRI) variation offers a practical scheme for raw data reception. However, the current designs for continuously varying PRI (CV-PRI) exhibit high complexity in engineering. In response to the issue, this paper proposes a novel strategy of stepwise varying PRI (SV-PRI), which demonstrates higher reconstruction accuracy compared with CV-PRI. Furthermore, confronting the azimuth non-uniform sampling characteristics induced by the PRI variation, this paper introduces a complete uniform reconstruction processing based on the azimuth partitioning methodology, which effectively alleviates the inherent contradiction between resolution and swath width. The processing flow, utilizing the temporal point remapping (TPR) concept, ensures the uniformity and coherence of dataset partitioning and reassembly in the context of the interpolation on non-uniform grids. Finally, according to the simulation results, the point target data, processed through the processing flow proposed in this study, have demonstrated effective focusing results. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) and tomographic SAR measurement techniques are commonly used for the three-dimensional (3D) reconstruction of complex areas, while the effectiveness of these methods relies on the interferometric coherence among SAR images with minimal angular disparities. Radargrammetry exploits stereo image matching to determine the spatial coordinates of corresponding points in two SAR images and acquire their 3D properties. The performance of the image matching process directly impacts the quality of the resulting digital surface model (DSM). However, the presence of speckle noise, along with dissimilar geometric and radiometric distortions, poses considerable challenges in achieving accurate stereo SAR image matching. To address these aforementioned challenges, this paper proposes a radargrammetric method based on the composite registration of multi-aspect SAR images. The proposed method combines coarse registration using scale invariant feature transform (SIFT) with precise registration using normalized cross-correlation (NCC) to achieve accurate registration between multi-aspect SAR images with large disparities. Furthermore, the multi-aspect 3D point clouds are merged using the proposed radargrammetric 3D imaging method, resulting in the 3D imaging of target scenes based on multi-aspect SAR images. For validation purposes, this paper presents a comprehensive 3D reconstruction of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) using Ka-band airborne SAR images. It does not necessitate prior knowledge of the target and is applicable to the detailed 3D imaging of large-scale areas with complex structures. In comparison to other SAR 3D imaging techniques, it reduces the requirements for orbit control and radar system parameters. To sum up, the proposed 3D imaging method with composite registration guarantees imaging efficiency, while enhancing the imaging accuracy of crucial areas with limited data. [ABSTRACT FROM AUTHOR]

Spaceborne Synthetic Aperture Radar (SAR), which can be mounted on space vehicles to collect information of the entire planet with all-day and all-weather imaging capacity, has been an indispensable device for earth observation. Currently, the technology of our spaceborne SAR has achieved a considerable technological improvement, including the resolution change from meter to submeter, the imaging mode from stripmap to azimuth beam steering like the sliding spotlight, the practical application of the multichannel approach and the conversion of single polarization into full polarization. With the development of SAR techniques, forthcoming SAR will make breakthroughs in SAR architectures, concepts, technologies and modes, for example, high-resolution wide-swath imaging, multistatic SAR, payload miniaturization and intelligence. All of these will extend the observation dimensions and obtain multidimensional data. This study focuses on the forthcoming development of spaceborne SAR.

When the Permanent Scatterer (PS) technique is utilized to compensate the Atmospheric Phase (AP) for Ground-Based Interferometric Synthetic Aperture Radar (GB-InSAR) images, a proper parametric model should be built to describe the AP. However, for some interferograms, the AP may nonlinearly vary with the PS range, and this cannot be effectively compensated via the conventional method. This paper proposes an improved method to compensate the nonlinear AP. Here, the conventional method is first used to compensate all the phase interferograms. By calculating the standard deviation of the phase sequence of every PS and setting a proper threshold, a large number of stable PSs are selected. Then these stable PSs are divided into a certain number of sub-regions, and some control points are determined. With the inverse distance weighting interpolation, the APs of all the PSs are estimated and compensated. To verify the effectiveness of the proposed method, 460 radar images are processed, and the results are made compared with those of the conventional method. The nonlinear AP could be better compensated with the proposed method to avoid misunderstanding of the motional area. Several reference PSs are selected to make quantitative comparisons, and measurement error up to 1 rad could be reduced.

Ground-Based Differential Interferometric Radars (GB-DInRads) have been widely applied to measure deformations. In this paper, the main types of GB-DInRads are summarized, including ground-based real aperture radar and Ground-Based Synthetic Aperture Radar (GB-SAR). The working principles and important parameters of some representative systems are introduced. Then, taking the GB-SAR as an example, the current key processing techniques are introduced, which mainly include differential interferometry, permanent scatterer selection, and atmospheric phase compensation. Lastly, three examples are presented to show the applications of GB-DInRad in deformation measurements. A Multiple-Input Multiple-Output (MIMO) radar was utilized to monitor an open-pit mine for about 11 days, and two regions with obvious deformation were found. In addition, a linear-scanning GB-SAR was utilized to monitor a mountain slope after severe landslide. The analysis result proved that rainfall could accelerate deformation. The feasibility of vibration measurement with the MIMO radar was also discussed in this paper.

Ground-based interferometry synthetic aperture radar (GB-InSAR) technology is becoming an important terrain mapping technology as it has the characteristics of short observation intervals, flexible operation environment and high data precision. Compared with air/space borne InSAR system, it is much easier for GB-InSAR to acquire repeat-pass/short-baseline data set, which can bring huge convenience on permanent scatterer (PS) technology. By using PS technology, on the one hand, the error caused by low coherence can be avoided, and on the other hand, the speed of the phase unwrapping part can be increased. In order to overcome the disadvantages of the non-uniform PS points distribution and low coherence cavities, the Kriging interpolation method is used to get an uniform digital elevation model (DEM) eventually. This study proposed a GB-InSAR interferogram processing method. First, it proposes a PS point selection method considering both accuracy and efficiency. Second, the study shows a network programming phase unwrapping method to unwrap the PS points. Then, Kriging interpolation method is analysed and used to fill the low coherence cavities. Finally, a field test is carried out in an open pit in Hebei, China. The DEM result of the observation area is shown to verify the correctness of this study's method.

Synchronization is a key problem in distributed Synthetic Aperture Radar (SAR) systems. In this paper, we perform a complex mathematical deduction and then analyze the influences of time synchronization on the SAR imaging and interferometric process. We discuss the relationship between time and phase synchronization, considering that different oscillators in separated transmitters and receivers lead to both time and phase synchronization errors. With respect to beam synchronization, we present the effects of the accuracies of beam pointing and satellite attitude on the antenna gain, based on the attitude-steering strategy, which involves azimuth weighting of the Doppler spectra for independent zero-Doppler beam steering. We also analyze the influences of beam synchronization on Doppler decorrelation, Signal-to-Noise Ratio (SNR), and overlapping swath error. We conduct simulations to validate the analysis results. Our findings provide guidance for system design.

The multi-baseline InSAR can effectively reduce the adverse effect caused by the abrupt change of the target and the large noise disturbance and can obtain the Digital Elevation Model (DEM) that is more accurate than the single baseline InSAR. Traditional multi-baseline height reconstruction algorithm based on Maximum Likelihood (ML) estimation is poorly reconstructed in the case of fewer channels, and the height reconstruction algorithm based on Maximum A Posteriori estimation (MAP) has a long runtime defect; to solve this problem, this study proposes the cluster analysis based on maximum a posteriori algorithm. This algorithm uses the ML estimation to obtain a rough DEM. Based on this result, the noise pixels in the neighborhood in each iteration process are determined by cluster analysis. Finally, through the calculation of posterior probability to complete the reconstruction, an optimized method is adopted to improve the accuracy. Experiments reveal that the algorithm retains the speed of the ML method as well as the high precision of the MAP estimation, thus maintaining accuracy and high operating efficiency.

High-resolution synthetic aperture radar presents a significant challenge to imaging algorithms and computing power. Slide spotlight is an important mode that has both high resolution and wide azimuth swath. Generally, in the slide spotlight mode, the performance of conventional frequency domain imaging algorithms degrades because of orbit curvature, the time-variant azimuth chirp rate, and other factors. We adopt the Back-Projection (BP) algorithm in this study to counteract this limitation. We also propose a CPU/GPU heterogeneous BP algorithm to deal with the high computing complexity O(N3) of the BP algorithm. This heterogeneous BP algorithm makes full use of computing resources and accelerates imaging progress, and the design of a scheduling thread improves the flexibility of the algorithm.

An effective way to achieve High Resolution and Wide Swath (HRWS) imaging capability is the multi-channel technique in azimuth. Improved resolution and swath can dramatically increase the volume of echo data in the SAR system. However, the onboard data storage and data-transmission bandwidth are limited, so data compression technique is typically used to reduce the volume of echo data. To study the effect of raw data compression on the azimuth multi-channel SAR system, in this paper, we establish a multi-channel SAR signal model based on data compression. We then derive and analyze the effects of data compression on the Signal-to-Noise Ratio (SNR) scaling factor of the multi-channel SAR system and quantization noise. Finally, we verify the validity of the proposed model and analysis results using simulation and real data and discuss the effect of data compression on the Peek-to-Ghost Ratio (PGR). The results of this paper provide an important theoretical basis for the choice of compression method in the multi-channel SAR system.

In this study, we present a modified full-aperture imaging algorithm that includes scalloping correction and spike suppression for sliding-Mosaic-mode Synthetic Aperture Radar (SAR). It is innovational to correct the azimuth beam-pattern weighting altered by radar antenna rotation in the azimuth during the deramping preprocessing operation. The main idea of spike suppression is to substitute zeros between bursts with linear-predicted data extrapolated from adjacent bursts to suppress spikes caused by multiburst processing. We also integrate scalloping correction for the sliding mode into this algorithm. Finally, experiments are performed using the C-band airborne SAR system with a maximum bandwidth of 200 MHz to validate the effectiveness of this approach.

To improve the accuracy and stability of built-up area extraction from Synthetic Aperture Radar (SAR) image time series, in this paper, we propose a multilayer neural-network-based built-up area extraction method that combines the characters of time-series images. The proposed method coarsely tags single images and obtains a large number of samples from time-series images that have been processed by a histogram specification procedure. To generate a training sample dataset, we use samples generated from one image to determine network depth and select samples with higher accuracy from the sample set taken from the timeseries images. The final model is trained by the selected large and high quality training dataset. We perform two comparison experiments with 38 25-m resolution ENVISAT ASAR images. Using the proposed method, we achieved 90.2% minima accuracy and a 0.725 minima Kappa coefficient, which are much higher than those of the three conventional methods. Thus, the accuracy and stability of built-up area extraction are significantly improved. In addition, the method proposed in this paper has the advantages of requiring minimal manual operation, well generalization, and training efficiency.

In this letter, a high-resolution synthetic aperture radar (SAR) imaging algorithm based on a modified omega-K algorithm is proposed. The proposed algorithm first multiplies the reference signal. Then, the azimuth nonstationary phase error (ANSPE) is considered after performing the interpolation process based on the generalized frequency scale transformation. Finally, a well-focused SAR image can be obtained after compensating the ANSPE and correcting the residual spatial variant range migration by utilizing the range segmentation technique. The simulation results of point targets and the real-measured airborne high-resolution SAR data simultaneously verify the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Airborne VHR SAR image registration is a challenging task. The number of CPs is a key factor for complex CP-based image registration. This paper presents a two-step matching approach to obtain more CPs for VHR SAR image registration. In the past decade, SIFT and other modifications have been widely used for remote sensing image registration. By incorporating feature point location affine transformation, a two-step matching scheme, which includes global and local matching, is proposed to allow for the determination of a much larger number of CPs. The proposed approach was validated by 0.5 m resolution C-band airborne SAR data acquired in Sichuan after the 2008 Wenchuan earthquake via a SAR system designed by the IECAS. With the proposed matching scheme, even the original SIFT, which is widely known to be unsuitable for SAR images, can achieve a much larger number of high-quality CPs than the one-step SIFT–OCT, which is tailored for SAR images. Compared with the classic one-step matching approach using both the SIFT and SITF–OCT algorithms, the proposed approach can obtain a larger number of CPs with improved precision. [ABSTRACT FROM AUTHOR]

The ambiguity suppression is a technical challenge for the present generation of spaceborne synthetic aperture radar (SAR) systems since this kind of suppression does not take the high spatial resolution and wide coverage into account simultaneously. The transmitting scheme based on the waveform diversity technique is a promising candidate for the conventional (one transmit, one receive channel) SAR systems and has been widely discussed, because it has almost no extra system costs and the ambiguity suppression performance is not closely related to pulse repetition frequency (PRF). However, the accurate method to evaluate the ratio of the intensities of the ambiguities to that of the signal is still a gap. To this end, starting from the precise signal model formulated in this paper, the ambiguity evaluation for spaceborne SAR with waveform diversity has been analyzed in detail. Particularly, the modified azimuth ambiguity-to-signal ratio (AASR) and range ambiguity-to-signal ratio (RASR) formulas are given for the single polarization SARs and quadrature-polarimetric (quad-pol) SARs, which contributes a lot, for the system designer, to precisely evaluating the ambiguity performance. Finally, detailed simulation experiments exploiting the system parameters of the LuTan (LT-1) system are carried out to corroborate the theoretical developments. [ABSTRACT FROM AUTHOR]

As polarimetric antennas can be isolated through excellent electronic frameworks in circular quad-polarization (CQP) synthetic aperture radar (SAR) systems, cross-polarization (x-pol) and co-polarization (co-pol) channel imbalances are more challenging and essential to calibrate than crosstalk in polarimetric calibration (PolCAL). In uncalibrated CQP SAR images without corner reflectors (CRs), the reciprocity and reflection symmetry assumptions of the distributed targets are commonly used to estimate the x-pol and co-pol channel imbalances, respectively. To suppress the influence of additive noise on determining channel imbalances through distributed targets, high signal-to-noise ratio (SNR) distributed targets should be obtained from the x-pol and co-pol channels of the CQP SAR images: namely, surface-dominated and volume-dominated targets. However, some reflection symmetry assumptions used in the existing calibration literature have poor applicability with volume-dominated targets, resulting in unsatisfactory estimation results for the co-pol channel imbalance phase. In this paper, we assess the priority of the reflection symmetry properties of volume-dominated targets used to calibrate the co-pol channel imbalance phase in CQP SAR data synthesized from linear quad-polarization data of ALOS, GF-3, and RADARSAT-2. In the theoretical part, high-priority reflection symmetry (termed semireflection symmetry) assumptions are confirmed as the most suitable for estimating the co-pol channel imbalance phase, and were selected to develop an algorithm for estimating the co-pol channel imbalance phase. Furthermore, based on the novel method for estimating the co-pol channel imbalance phase, a channel imbalance calibration scheme is proposed for CQP SAR systems with reciprocal crosstalk, including extracting surface-dominated and volume-dominated targets, and estimating and filtering channel imbalances. We demonstrate the effectiveness of our proposed scheme with CRs in simulated CQP SAR images. The experimental results show that the calibration scheme is an effective workflow for estimating channel imbalances in CQP SAR systems with reciprocal crosstalk. [ABSTRACT FROM AUTHOR]

In this paper, according to the tightly coupled antenna theory, the dual-polarized tightly coupled antenna of 15–40 GHz is designed. The antenna size is 3.75 mm × 3.75 mm × 2.38 mm, and the spacing between the elements is 3.75 mm. The media used from bottom to top are Rogers5880, PP adhesive, RogersTMM10, and feed line using coaxial line directly, with a coaxial line inner core radius of 0.2 mm. Based on the simulations, we find that the antenna can easily cover ±45° and maintain good active standing wave performance. The antenna can support ±60° maximum Angle scanning and maintain good active standing wave performance. [ABSTRACT FROM AUTHOR]

Video Synthetic Aperture Radar (ViSAR) operating in spotlight mode has received widespread attention in recent years because of its ability to form a sequence of SAR images for a region of interest (ROI). However, due to the heavy computational burden of data processing, the application of ViSAR is limited in practice. Although back projection (BP) can avoid unnecessary repetitive processing of overlapping parts between consecutive video frames, it is still time-consuming for high-resolution video-SAR data processing. In this article, in order to achieve the same or a similar effect to BP and reduce the computational burden as much as possible, a novel time-frequency sub-aperture technology (TFST) is proposed. Firstly, based on azimuth resampling and full aperture azimuth scaling, a time domain sub-aperture (TDS) processing algorithm is proposed to process ViSAR data with large coherent integration angles to ensure the continuity of ViSAR monitoring. Furthermore, through frequency domain sub-aperture (FDS) processing, multiple high-resolution video frames can be generated efficiently without sub-aperture reconstruction. In addition, TFST is based on the range migration algorithm (RMA), which can take into account the accuracy while ensuring efficiency. The results of simulation and X-band airborne SAR experimental data verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Space-time waveform-encoding (STWE)-synthetic aperture radar (SAR) is an effective way to accomplish high-resolution and wide-swath (HRWS) imaging. By designing the specific signal transmit mode, the echoes from several subswaths are received within a single receiving window and overlap each other in STWE-SAR. In order to separate the overlapped echoes, the linear-constrained minimum variance (LCMV) beamformer, a single-null beamformer, is typically used. However, the LCMV beamformer has a very narrow and unstable notch depth, which is not sufficient to accurately separate the overlapped echoes with large signal energy differences between subswaths. The issue of signal energy differences in STWE-SAR is first raised in this paper. Moreover, a novel echo separation scheme based on a second-order cone programming (SOCP) beamformer is proposed. The beam pattern generated by the SOCP beamformer allows flexible adjustment of the notch width and depth, which effectively improves the quality of separation results compared to the LCMV beamformer. The simulation results illustrate that the scheme can greatly enhance the performance of echo separation. Furthermore, the experimental results based on the X-band STWE-SAR airborne system not only demonstrate the scheme's effectiveness but also indicate that it holds great promise for future STWE-SAR missions. [ABSTRACT FROM AUTHOR]

In this paper, a method of Synthetic Aperture Radar (SAR) image Automatic Target Recognition (ATR) based on Convolution Auto-encode (CAE) and Support Vector Machine (SVM) is proposed. Using SVM replaces the traditional softmax as the classifier of the CAE model to classify the feature vectors extracted by the CAE model, which solves the problem that the softmax classifier is less effective in the nonlinear case. Since the SVM can only solve the binary classification problem, and in order to realize the classification of the class objectives, the SVM were designed to achieve the classification of the input samples. After unsupervised training for CAE, the coding layer is connected with SVM to form a classification network. CAE can extract the features of the data by an unsupervised method, and the nonlinear classification advantage of SVM can classify the features extracted by CAE and improve the accuracy of the object recognition. At the same time, the high-accuracy identification of key targets is required in some special cases. A new initialization method is proposed, which initializes the network parameters by pretraining the key targets and changes the weights of different targets in the loss function to obtain better feature extraction, so it can ensure good multitarget recognition ability while realizing the high recognition accuracy of the key targets. [ABSTRACT FROM AUTHOR]

The Wishart mixture model is an effective tool for characterizing the statistical distribution of polarimetric synthetic aperture radar (PolSAR) data. However, due to the difficulty in determining the equivalent number of looks, the Wishart mixture model has some problems in terms of practicality. In addition, the flexibility of the Wishart mixture model needs to be improved for complicated scenes. To improve the practicality and flexibility, a new mixture model named the relaxed Wishart mixture model (RWMM) is proposed. In RWMM, the equivalent number of looks is no longer considered a constant for the whole PolSAR image but a variable that varies between different clusters. Next, an innovative algorithm named RWMM-Markov random field (RWMM-MRFt) for supervised classification is proposed. A new selection criterion for adaptive neighborhood systems is proposed in the algorithm to improve the classification performance. The new criterion makes effective use of PolSAR scattering information to select the most suitable neighborhood for each center pixel in PolSAR images. Three datasets, including one simulated image and two real PolSAR images, are utilized in the experiment. The maximum likelihood classification results demonstrate the flexibility of the proposed RWMM for modeling PolSAR data. The proposed selection criterion shows superior performance than the span-based selection criterion. Among the mixture model-based MRF classification algorithms, the proposed RWMM-MRFt algorithm has the highest classification accuracy, and the corresponding classification maps have better anti-noise performance. [ABSTRACT FROM AUTHOR]

The integration of sensing, computing, storage, and communications is one of the main research directions of current network development. To achieve the integration, an innovative design in the antenna array is essential. This paper presents a novel design of an irregularly distributed antenna array for smart 6G networks. Firstly, to solve the problem of fast amplitude and phase distribution of conformal arrays, the fast electromagnetic code of the multilevel fast multipole (MLFMA) based on volume surface integral equations (VSIE) is used to simulate the radiation characteristics of the irregularly distributed antenna arrays. Secondly, another stochastic global optimization algorithm, Simulated Annealing (SA), has been widely used to solve multiscale nonlinear problems. Finally, the performance of the proposed antenna array is given by simulation results and tests, to prove the effectiveness and correctness. [ABSTRACT FROM AUTHOR]

Multiple-input and multiple-output synthetic aperture radar (MIMO SAR) is a very potential technique for high-resolution and wide-swath (HRWS) imaging due to the fact that it can provide more degrees of freedom. In this article, an innovative MIMO-HRWS SAR imaging system is introduced with segmented phase coding (SPC) waveforms. However, it is well-known that the echo separation issue is the most technical challenge for MIMO SAR. It has shown that the separation of orthogonal echoes from cross correlation interference can be achieved by scan-on-receive (SCORE). However, this method often requires large computing resources on board, especially for null-steering process. For this, we propose a two-stage spaceborne–ground echo separation scheme. This scheme is mainly divided into two steps: the first step is a real-time SCORE beamforming process on board to reduce the downlink data volume, and the second step is a bandpass–null-steering process to suppress the interfering echoes on the ground. Thus, this scheme allows for the low computing resources on board and sufficient suppression of the interference simultaneously. Following this scheme, the MIMO-HRWS SAR system enables its number of equivalent phase centers nearly double that of the azimuth multichannel SAR system, leading to higher resolution and wider swath imaging. Finally, detailed simulation experiments for the MIMO-HRWS SAR imaging system are performed to verify the practicability and feasibility of the proposed scheme. [ABSTRACT FROM AUTHOR]

Dynamic deformation measurement is an important approach to monitor structure stability. Due to its rapid imaging capabilities, ground-based multi-input–multi-output (GB-MIMO) radar has shown great application potential in bridge structure monitoring. This article proposes a dynamic deformation estimation method based on radar image series. First, an improved clutter suppression method is used to overcome the estimation error in complex working status. Second, a two-step pixel extraction method is adopted to ensure both the quantity and quality of sample pixels. Finally, frequency- and time-domain environmental stimulating methods are jointly considered for stable mode shape estimation. This article systematically measured the dynamic deformations of bridge structures, including two suspension bridges and a cable-stayed bridge. The time-series deformation, deflection, vibration frequency, and amplitude on the bridge structure are obtained by image-domain measurement. For the first time, the mode shapes of bridges are obtained through GB-MIMO radar. The correctness of structure parameter measurement is verified with the finite element model (FEM). Experimental results prove that with the proposed method, stable results could be acquired against weak and complex stimulation conditions. [ABSTRACT FROM AUTHOR]

To achieve high-resolution and wide-swath (HRWS) imaging, a space-time waveform-encoding (STWE) spaceborne synthetic aperture radar (SAR) system is adopted. In rugged terrain, the beam-pointing mismatch problem will appear when the traditional digital beamforming (DBF) technique is used to separate the received echoes. This problem leads to decreasing the received echo's gain, deteriorating the quality of the image product and making the interpretation of SAR image difficult. To address this problem, an advanced echo separation scheme for STWE spaceborne SAR based on the DBF and blind source separation (BSS) is put forward in this paper. In the scheme, the echoes are transmitted within the adjacent pulse repetition intervals to simulate multiple beams, and the scattered echoes are received by the sixteen-channel antennas in elevation simultaneously. In post-processing, a detailed flow is adopted. In the method, the DBF is firstly performed on received echoes. Due to the error caused by terrain undulation, the degree of echo separation is not enough. Then, the BSS is performed on the multiple echoes obtained after the DBF processing. Finally, the highly separated echo signal can be obtained. In this scheme, there is no need to perform the direction of arrival (DOA) estimation before the DBF processing, which saves valuable computing resources. In addition, to verify the proposed scheme, point target and distributed target simulations based on the 16-channel data of an elevation X-band DBF-SAR system are carried out. The results of point targets indicate that the residual echo caused by rough terrain can be reduced by more than 14 dB using the proposed scheme. The proposed scheme can be directly implemented into existing SAR systems; thus, it does not increase the complexity of the system design. The scheme has the potential to be applied to future spaceborne SAR missions. [ABSTRACT FROM AUTHOR]

Due to the minimum antenna area constraints of synthetic aperture radar (SAR), high-resolution and wide-swath (HRWS) imaging is difficult to be achieved using classical modes, such as scan, spotlight, and so on. Range ambiguity is one of the main technical challenges limiting HRWS imaging in spaceborne SAR. Inspired by the cocktail party effect, an advanced scheme based on blind source separation (BSS) is put forward to suppress the range ambiguity of spaceborne SAR in this article. In the scheme, multiple subantennas are used to collect multiple echo datasets, which are given different patterns. In this way, the echo signal of the desired region and that of the ambiguous region are weighted using different coefficients. During postprocessing, a detailed flow with the BSS algorithm is proposed to separate the desired echoes from those datasets. To verify the scheme, point target and scene simulations based on GF-3 are carried out. The results indicate that the range ambiguity can be reduced by more than 15 dB using the proposed scheme, the processing flow is effective, and the scheme has a good performance without increasing the complexity of the system design under the current SAR system conditions. The scheme has the potential to be adopted in future spaceborne SAR missions. [ABSTRACT FROM AUTHOR]

Ground-based interferometric synthetic aperture radar (GB-InSAR) technology can be applied to generate a digital elevation model (DEM) with high spatial resolution and high accuracy. Phase unwrapping is a critical procedure, and unwrapping errors cannot be effectively avoided in the interferometric measurements of terrains with discontinuous heights. In this paper, an improved multi-baseline phase unwrapping (MB PU) method for GB-InSAR is proposed. This method combines the advantages of the cluster-analysis-based MB PU algorithm and the minimum cost flow (MCF) method. A cluster-analysis-based MB PU algorithm (CA-based MB PU) is firstly utilized to unwrap the clustered pixels with high phase quality. Under the topological constraints of a triangulation network, the connectivity graph of any non-clustered pixel is established with its adjacent unwrapped cluster pixels. Then, the absolute phase of these non-clustered pixels can be identified using the MCF method. Additionally, a spatial-distribution-based denoising algorithm is utilized to denoise the data in order to further improve the accuracy of the phase unwrapping. The DEM generated by one GB-InSAR is compared with that generated by light detection and ranging (LiDAR). Both simulated and experimental datasets are utilized to verify the effectiveness and robustness of this improved method. [ABSTRACT FROM AUTHOR]