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-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 bistatic synthetic aperture radar interferometry (GNSS-based InBSAR) suffers from low image resolution, low signal-to-noise ratio (SNR), and accumulated satellite baselines because navigation satellites are used as transmitters. Therefore, traditional texture-based and leader image fixed image registration and persistent scatterer (PS) point selection algorithms cannot be adopted. In this article, a sequential image registration algorithm is proposed based on the preselected PS (prePS) point association for GNSS-based InBSAR systems. First, the prePS points are selected based on the coherence coefficient between theoretical and actual resolution cells. Then, image registration is achieved through prePS point association and subresolution offset estimation, and sequential registration is adopted to replace traditional leader image fixed registration. Finally, PS point selection and interferometric phase extraction are implemented based on the analysis of the resolution cells. Raw data from BeiDou navigation satellites are used to indicate 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.