Global navigation satellite system-based bistatic synthetic aperture radar interferometry (GNSS-based InBSAR) can improve the monitoring interval to one day due to the using of navigation satellites. Meanwhile, the low signal-to-noise ratio (SNR), poor image resolution, and the random focus position offsets cause large interferometric phase noise. In this letter, an interferometric phase noise reduction algorithm is proposed for GNSS-based InBSAR based on adaptive edge detection and temporal area filtering. An improved edge detection algorithm is adopted to solve the overlapping of resolution cells and phase interference caused by poor resolution. Then, to compensate the random focus position, an area filtering algorithm is proposed to find the temporal supporting area of persistent scatterers (PSs). Finally, the principal phase is extracted to reduce the interferometric phase error. The raw data are used to indicate the effectiveness of the proposed algorithm, and the best monitoring accuracy can reach millimeter level.

The most prominent advantage of the global navigation satellite system-based bistatic synthetic aperture radar interferometry (GNSS-InBSAR) system is its capability to achieve high-frequency 3-D deformation monitoring by combining measurements from different satellites. However, multisatellite, multiband, and short-interval imaging also introduces challenges such as large data amount and high computational requirements. In this article, an adaptive-segmentation-oriented multiband synthesis fast imaging algorithm is proposed for GNSS-InBSAR system. First, multiband synthetic signal model is established for the Beidou navigation signals. Then, optimization methods for segment parameters are proposed along range and azimuth directions, respectively. The limitation of the optimization methods are next discussed based on the GNSS-InBSAR system characteristics. The raw data of multiband navigation signals are used to indicate the effectiveness of the proposed algorithm. The computation time for single-band signals has been reduced by 77%, while for multiband signals, the computation time has been reduced by 71%.

The Global Navigation Satellite System (GNSS)-based Interferometric Bistatic Synthetic Aperture Radar (InBSAR) systems can retrieve 3-D deformation fields. However, the terrain phase error significantly deteriorates their accuracy. In this article, an adaptive high-accuracy terrain phase error compensation algorithm is proposed. First, based on the inherent simultaneous availability of multiple satellite signals, a multiangle SAR image association algorithm is proposed to obtain the observations of the same point from different angles, and image shift under different bistatic configurations is also eliminated here. Second, incorporating a nonrobust data extraction and discarding mechanism, an improved estimator is proposed, along with modified loss and weight functions, to robustly estimate terrain errors of persistent scatterers. Third, since position dilution of precision (PDOP) can quantify the impact of multistatic configurations on 3-D deformation retrieval accuracy, a filter that considers both the PDOP and distances between the persistent scatterers named PDOP-Distance-weighted spatial filter is proposed to maximize the utilization of multisatellite collaborative observations and obtain the terrain errors of whole scene. The experimental data of five BeiDou satellites verify the proposed algorithm. The deformation measurements and terrain error estimations from GNSS-based InBSAR are compared with classical differential GNSS and autonomous aerial vehicles (AAV)-mounted light detection and ranging (LiDAR), both showing good consistency. These results prove the reliability of GNSS-InBSAR systems and indicate their great potential for deformation monitoring.

Global Navigation Satellite System based bistatic Synthetic Aperture Radar Interferometry (GNSS-based InBSAR) is an effective approach for achieving high-precision 3D surface deformation measurement. However, existing studies on the vegetation effects on deformation measurement using GNSS-based InBSAR have rarely been conducted. In this paper, a preliminary assessment of deformation results in various vegetated areas using GNSS based InBSAR with Beidou Navigation Satellite System (BDS) data is implemented. The imaging results and deformation measurement accuracy are analyzed. Current results prove the feasibility of GNSS-based InBSAR in analyzing vegetation decoherence.

Global Navigation Satellite System-based Bistatic Synthetic Aperture Radar Interferometry (GNSS-InBSAR) system adopts a novel and integrated GNSS receiver, where the direct signal could be collected by the backlobe of the reflected antenna and would result in the fake permanent scatterers (PSs) in the single channel data. Additionally, limited by the data amount, only one channel of data can be transmitted and signal processed. In this letter, an adaptive fake PSs removal algorithm is proposed based on the multi-parameters-estimation direct signal reconstruction. First, the direct signal model considering the transmission link filter, the image offsets, the amplitude, and the phase of the image is established. Then, all these parameters are estimated based on the adaptive image information extraction. Finally, the direct signal is reconstructed to remove the fake PSs in the raw image results. The raw data of the Beidou navigation satellite is used and the final experimental results indicated that the fake PSs in the SAR images can be completely removed, and the deformation retrieval accuracy of the interference area has improved by 63%.

Global Navigation Satellite System based Bistatic Synthetic Aperture Radar Interferometry (GNSS-based InBSAR) is a radar remote sensing technique for measuring surface deformation with mm-level accuracy. However, it suffers from low Signal to Noise Ratio (SNR) and low image resolution, resulting in few Persistent Scatterers (PS) extracted. Thus, deformation measurements are more sparsely compared with other InSAR and an accurate interpolator is needed to obtain the deformation field. In this letter, a deformation field formation algorithm based on modified Kriging interpolator is proposed to obtain the deformation field over the whole scene in GNSS-based InBSAR. First, the deformation measurements on the PS are divided in two sets by an adaptive threshold, and then the linear optimal matching model is constructed to overcome the invalidity of fitting parameters. Second, the spherical model re-fitting is used to obtain the final Kriging weights. The raw data of Beidou navigation satellites are used to validate the effectiveness of the proposed algorithm.

The 3-D deformation monitoring can be achieved by associating different monitoring results from different navigation satellites in Global Navigation Satellite System (GNSS)-based interferometric bistatic synthetic aperture radar (InBSAR) system. However, the whole scene monitoring accuracy is limited by the low signal-to-noise ratio (SNR) and the differences among synthetic aperture radar (SAR) images from different GNSS satellites. In this article, a modified 3-D deformation fields construction algorithm based on the spatial and temporal continuity is proposed for GNSS-based InBSAR system. First, based on the spatial continuity, the persistent scatterers (PSs) that can be observed by more satellites are used to filter the initial scene deformation monitoring results. Second, the model is established according to the temporal continuity of deformation to refine the optimal regularization parameters. Third, with the intersystem fusion concept, 1-D deformation with sub-millimeter accuracy of ground-based SAR (GB-SAR) is introduced to further improve the 3-D deformation monitoring accuracy of GNSS-based InBSAR. Finally, the experimental data verify the proposed algorithm for the GNSS-based InBSAR system, achieving millimeter-level deformation monitoring accuracy. The proposed algorithm enables the system have great potential in applications such as geological disaster warning and building health monitoring.

The uncertainty of the elevation of target area in bistatic synthetic aperture radar (BiSAR) introduces image defocus. This uncertainty becomes much worse in global navigation satellite system-based BiSAR interferometry (GNSS-based InBSAR) applications, where the primary problem is a decrease in the coherence of the image pairs. In this paper, a high-coherence oriented imaging algorithm based on adaptive elevation ramp fitting is proposed for GNSS-based InBSAR systems. First, GNSS-based InBSAR signal model is established considering elevation error. From this model, the expressions for position offset and interferometric phase error caused by the elevation error are derived. Then, to improve the elevation fitting accuracy, full-scene fitting is replaced by subarea fitting, and adaptive subarea segmentation is achieved based on the points with complete resolution cells and image valleys. Finally, elevation fitting is performed in the subareas. The algorithm can obtain high-coherence image pairs with low image resolution introduced by GNSS-based InBSAR systems. Simulation and raw data are used to prove the effectiveness of the proposed algorithm in GNSS-based InBSAR.

Global navigation satellite system-based synthetic aperture radar interferometry (GNSS-based InSAR) systems can achieve 3-D deformation retrieval by associating multiangle images of different satellites. However, the difference in the scene radar cross section (RCS) and resolution cells makes multiangle images vary considerably. In addition, low resolution will further aggravate the difference in multiangle images. In this letter, a multiangle images association algorithm is proposed for GNSS-based InSAR systems. First, the supervised area is introduced to describe the areas of the same deformation based on the persistent scatter (PS) point. Then, the initial multiangle images association results are obtained by overlapping all PS points supervised areas of all satellites. Finally, the associated areas are normalized to obtain valid associated results. The raw data from eight Beidou satellites are used to prove the effectiveness of the proposed algorithm.

In this article, a high-frequency motion error compensation algorithm for mini-unmanned aerial vehicle (UAV)-based bistatic synthetic aperture radar (BiSAR) system is proposed. Compared with the traditional SAR system platform, the mini-UAV is more vulnerable to interference from the external environment. Thus, the motion error of mini-UAV-based BiSAR systems always contains both low- and high-frequency components. Meanwhile, the independent transmitter and receiver sources would introduce more than one high-frequency motion error component, and the variance of the high-frequency motion error also needs to be considered. This article first establishes a high-frequency motion error model and the corresponding Doppler phase signal for the mini-UAV-based BiSAR system. Then, multiple forms and multiple components errors are separated and estimated on the azimuth phase. Next, the complex spatial variance is accurately modeled and compensated by estimating the platforms’ motion errors. It is suggested from the simulation and experimental results that the proposed algorithm can compensate for the motion error with complex forms under the mini-UAV-based BiSAR system condition.

Mini-unmanned aerial vehicle (UAV)-based bistatic synthetic aperture radar (BiSAR) is widely used due to its flexibility of system topology selection and simplicity. However, the motion compensation (MOCO) of the system is more difficult because the perturbance of mini-UAV platforms is greater and the spatial variance is more complex. In addition, the degrees of freedom of motion error are twice as those of the monostatic SAR. In this article, a novel MOCO algorithm based on a motion sensitivity model is proposed. First, a BiSAR system motion sensitivity model is established to assess the importance of each motion component error. Second, a novel MOCO method is proposed based on the determination of the main motion errors. Third, the proposed algorithm is validated by both simulations and real data sets. The final well-focused image suggests that the variant phase error after compensation with the new algorithm is much smaller than that with the traditional one.

Geosynchronous SAR tomography (GEO TomoSAR) is a technique that combines geosynchronous SAR (GEO SAR) with SAR tomography. It can make full use of the advantages of short revisit time and large observation area of GEO SAR to achieve timely and accurate three-dimensional (3D) reconstruction of targets. However, GEO SAR is severely affected by the temporal-spatial variant ionosphere, which causes image shift and induces residual phase between repeated orbital data. These can seriously affect tomographic performance. In the paper, the GEO TomoSAR signal model affected by the ionosphere is established based on the temporal-spatial variant total electron content (TEC) model of background ionosphere. Then the influence of ionosphere on different scatterers projected into the same pixel in GEO SAR images is analyzed, and the expression of residual phase error between repeated orbit images is given. After analysis, the influence of the background ionosphere on the GEO TomoSAR system mainly includes the relative offset of the scattering target and the imaging defocus in the elevation direction. Finally, the experimental verification was completed using US-TEC data.

Geosynchronous synthetic aperture radar (GEO SAR) has great potentials in synthetic aperture radar ground moving target indication (SAR-GMTI) due to its high time resolution and wide swath coverage. But the signal-to-noise ratio (SNR) of echo is too low and multichannel configuration cannot be realize in GEO monostatic SAR. Therefore, geosynchronous spaceborne-airborne bistatic multichannel synthetic aperture radar (GEO-SABM SAR) can realize enhanced SNR and multichannel configuration. In this paper, we establish the slant range model for GEO-SABM SAR and give the VSAR processing flowchart. Finally, the numerical experiments is given to verify the effectiveness of proposed method.

This paper proposed a 3-D surface deformation retrieval algorithm for synthetic aperture radar interferometry (InSAR) based on a Global Navigation Satellite System (GNSS) as a transmitter and a fixed receiver (GNSS-based InSAR), where simultaneous multiple GNSS transmitters and a repeat-pass concept were adopted. This paper consists of three parts. First, the interferometric phase model under repeat-pass concept was established for both general and bistatic permanent scatterer (PS) cases in the GNSS-based InSAR. Second, the 3-D deformation retrieval algorithm was presented, and the position dilution of precision was analytically derived to evaluate the performance of measured 3-D deformation. Third, using a designed transponder onboard displacement device as the bistatic PS, the feasibility of 3-D deformation retrieval using GNSS-based InSAR was confirmed by repeat-pass experiments, where four simultaneously available Beidou-2 Inclined Geosynchronous Orbit satellites were used as transmitters, and 16 repeat-pass data sets were collected. Using the proposed algorithm, the final experimental results suggested that the GNSS-based InSAR could obtain deformation estimations with better accuracy than at 5 mm in all three directions. Thus, huge potential exists for applications such as landslide prediction and infrastructure safety monitoring.

The passive Synthetic Aperture Radar with Global Navigation Satellite System (GNSS) employs GNSS satellites as transmitters and receivers mounted near the ground. Since GNSS constellations are designed for global, reliable and persistent operation, the most important advantage of such a system is the potential of permanent monitoring on the area which is overlooked by a fixed receiver. In the paper, an experimental prototype of GNSS-based SAR for the purpose of obtaining larger scene images as well as the development progress was introduced at the first place. And then a latest imaging experiment of GNSS-Based SAR for railway bridge was carried out and the image was obtained with high quality when it was firstly used for China Railway High-Speed (CRH) railway bridge imaging. The positive imaging result suggested that it is possible to achieve change information extraction using GNSS-based SAR for the further safety evaluation of CRH operation in China.

Geosynchronous SAR (GEO SAR) has better coverage and revisiting performance than the low Earth orbit SAR. Thus GEO SAR can implement the repeat-pass interferometry as its revisit time of 1 day and the orbit drift (forming baseline) induced by perturbation. In comparison, the twin GEO SAR formation configuration in which the slave satellite repeats the master satellite' track can realize shorter revisit time and adjustable baseline by tuning the orbit elements. In this paper, a validation experiment based on the Chinese Beidou IGSO navigation satellites is demonstrated to validate the feasibility and effectiveness of this concept. The validation experiment consists of data acquisition of the Beidou IGSO satellites signals, equivalent pre-processing, image processing and interferometry processing. In the end, we obtain the high-precision experimental results of deformation inversion, validating the feasibility of twin GEO SAR interferometry/differential interferometry.

This paper analyzes the multiangle imaging results for bistatic synthetic aperture radar (BSAR) based on global navigation satellite systems (GNSS-BSAR). Due to the shortcoming of GNSS-BSAR images, a multiangle observation and data processing strategy based on BeiDou-2 navigation satellites was put forward to improve the quality of images and the value of system application. Twenty-six BSAR experiments were conducted and analyzed in different configurations. Furthermore, a region-based fusion algorithm using region-of-interest (ROI) segmentation was proposed to generate a high-quality fusion image. Based on the fusion image, typical targets such as water area, vegetation area, and artificial targets were compared and interpreted among single/multiple-angle images. The results reveal that the multiangle imaging method was a good technique to enhance image information, which might extend the applications of GNSS-BSAR.

This letter conducts a point spread function (PSF) analysis for bistatic synthetic aperture radar (BSAR) systems where the transmitter is in medium Earth orbit, and the receiver is fixed on the ground. To achieve a reasonable azimuth resolution under such a configuration, the trajectory of the satellite can no longer be approximated as a straight line; therefore, current methods for PSF analysis are insufficient. The solution proposed involves extension of the generalized ambiguity function to accommodate satellite trajectory curvature. The theoretical analysis shows effects unlike those observed in monostatic or even the general BSAR and is verified by both simulation and experimental results using navigation satellites as the transmitting platforms.

This paper presents proof-of-concept methods and results on a passive synthetic aperture radar for surface change monitoring. The bistatic topology comprises navigation satellites (such as Global Positioning System, GLObal Navigation Satellite System (GLONASS), Galileo, or Beidou) as transmitters of opportunity and a fixed receiver on the ground. Surface monitoring is to be provided through coherent change detection. An experimental test bed built to confirm the concept of the system is described, and the appropriate experimental program is presented. Preliminary signal processing algorithms for change detection are derived. Experimentally measured changes are compared to the theoretical predictions, and the obtained results are discussed.

This paper proposes an extended nonlinear chirp scaling (CS) image formation algorithm for the bistatic synthetic aperture radar systems with the squinted transmitter and a fixed receiver. Since the transmitter with the squint mode was adopted in the system, two main problems, i.e., the spatial variance of the frequency-modulation rate and cubic phase terms, were introduced in the image formation algorithm. The former problem was solved by the linearity approximation of parameter$p$and deduced$q$(the second- and third-order coefficients of CS factors in range, which could be used to remove the spatial variation and high-order phase in the range direction) along the range domain while the latter one was compensated by a cubic analytical phase term in the frequency domain. A corresponding experimental hardware system and the bistatic experiments were also described in this paper. Both the simulation and experimental results validated the proposed algorithm.

This letter demonstrates the feasibility of space-surface bistatic synthetic aperture radar using navigation satellites as transmitters of opportunity and a fixed ground-based receiver. Experiments with real satellite signals are described, and the obtained imagery is presented and discussed.

This paper proposes a highly accurate bistatic range migration algorithm (RMA) for space-surface bistatic synthetic aperture radar (BiSAR) systems with asymmetric configurations when the transmitter moves along a rectilinear trajectory and the receiver stays at a fixed location. The main work here includes three aspects. First, by introducing a 2-D change of frequency variables, an analytical 2-D spectrum formula without any approximation was derived at the first time. Second, with the linearity approximation of the derived phase, a highly accurate bistatic RMA was proposed and guaranteed that the algorithm could be applied to focus the echo from a large target scene. Furthermore, the nonlinear phase of the linearity approximation was analyzed to avoid the defocus effect in the final BiSAR image. Both simulation and indoor experimental results are presented to validate our analysis.

In Geosynchronous SAR (GEO SAR), because of the increase of orbit height, the signal propagation delay time reaches up to hundreds of milliseconds, and the synthetic aperture time also reaches up to hundreds of seconds which will result in a curved synthetic aperture trajectory, thus the “Stop-and-Go” assumption and conventional imaging methods of low earth orbit SAR (LEO SAR) will lose effect in GEO SAR. In addition, because the angular velocity of earth rotation is approximately equal to that of satellite rotation, the Doppler parameter and resolution analysis in LEO SAR cannot be directly used too in GEO SAR either. In this paper, firstly, the accurate slant range model in GEO SAR is created based on the consideration of the error of “Stop-and-Go” assumption, and then the improved imaging method is proposed to compensate for the error of “Stop-and-Go” assumption and the effect of the curved synthetic aperture trajectory. Finally, based on the generalized ambiguity function (GAF) and projection theory, the accurate Doppler gradient vector is analytically obtained based on the consideration of earth rotation in GEO SAR, and the accurate resolution calculation in arbitrary direction is also derived in detail. All the simulation results verify the correctness and effectiveness of the proposed imaging method and resolution analysis method.

In this paper, considering the relative motion between satellite and earth during signal propagation time, the accurate analysis method for propagation slant range is presented in geosynchronous SAR. Furthermore, the difference between accurate analysis method and `Stop-and-Go' assumption is analytically obtained. Meanwhile based on the derived accurate slant range model, it is found that the corresponding range migration correction and azimuth reference function must consider the high order term, and therefore the modified SPECAN algorithm is proposed. The simulation results verify the correctness of `Stop-and-Go' assumption error derivation and SPECAN algorithm modification.

The key problem of the imaging processing in geosynchronous earth orbit (GEO) SAR system is the space-variance of the Doppler parameters, which will result in the defocus of the SAR image with classical imaging algorithm. In this paper, a Modified Range Migration Algorithm (RMA) is proposed to overcome the space-variance of Doppler parameters by compensating the velocity change along the location of the target. Furthermore the simulation results fully verify the effectiveness of derived algorithm after velocity compensation.