Ground-based Synthetic Aperture Radar Research Articles

Utilizing Polarization Diversity in GBSAR Data-Based Object Classification.

In recent years, the development of intelligent sensor systems has experienced remarkable growth, particularly in the domain of microwave and millimeter wave sensing, thanks to the increased availability of affordable hardware components. With the development of smart Ground-Based Synthetic Aperture Radar (GBSAR) system called GBSAR-Pi, we previously explored object classification applications based on raw radar data. Building upon this foundation, in this study, we analyze the potential of utilizing polarization information to improve the performance of deep learning models based on raw GBSAR data. The data are obtained with a GBSAR operating at 24 GHz with both vertical (VV) and horizontal (HH) polarization, resulting in two matrices (VV and HH) per observed scene. We present several approaches demonstrating the integration of such data into classification models based on a modified ResNet18 architecture. We also introduce a novel Siamese architecture tailored to accommodate the dual input radar data. The results indicate that a simple concatenation method is the most promising approach and underscore the importance of considering antenna polarization and merging strategies in deep learning applications based on radar data.

A Reliable Observation Point Selection Method for GB-SAR in Low-Coherence Areas

Ground-Based Synthetic Aperture Radar (GB-SAR), due to its high precision, high resolution, and real-time capabilities, is widely used in the detection of slope deformations. However, the weak scattering coefficient in low-coherence areas poses a great challenge to the observation point selection accuracy. This paper introduces a selection process for reliable observation points that integrates phase and spatial information. First, for various observation points with differentiated stability, we propose to utilize maximum likelihood estimation (MLE) methods to achieve stability assessment. Second, a phase correction approach is proposed to address unwrapped phase errors encountered at less stable points. Third, adaptive filtering for deformation information at observation points is achieved using estimated variance combined with wavelet filtering thresholds. Finally, in dealing with unknown deformation trends, we propose utilizing a clustering method to accurately identify these trends, thereby enhancing the precision in identifying reliable observation points (ROPs). The experimental results demonstrate that this method enhances the accuracy of observation point selection in low-coherence areas, providing a broader observational field for deformation detection.

A Three-Dimensional Deformation Monitoring Method: Combining Optical Deformation Monitoring Based on Regression Models and GB-SAR Interferometry.

This paper introduces a method for quantifying the three-dimensional deformation of ground targets and outlines the associated process. Initially, ground-based synthetic aperture radar was employed to monitor the radial deformation of targets, and optical equipment monitored pixel-level deformation in the vertical plane of the line of sight. Subsequently, a regression model was established to transform pixel-level deformation into two-dimensional deformation based on a fundamental length unit, and the radar deformation monitoring data were merged with the optical deformation monitoring data. Finally, the fused data underwent deformation, resulting in a comprehensive three-dimensional deformation profile of the target. Through physical data acquisition experiments, the comprehensive three-dimensional deformation of targets was obtained and compared with the actual deformations. The experimental results show that the method has a relative error of less than 10%, and monitoring accuracy is achieved at the millimeter level.

Entropy-based Parameterized Amplitude and Phase Correction for MIMO Ground-based SAR

Entropy-based Parameterized Amplitude and Phase Correction for MIMO Ground-based SAR

Fast Detection of Moving Targets by Refocusing in GBSAR Imagery Based on Enlightend Search

Ground-based synthetic aperture radar (GBSAR) is widely used in mountains, mines, and other areas because it can get the sub-millimeter deformation information of monitoring scenes. This technology plays a vital role in safeguarding production operations, providing accurate disaster projections, and facilitating timely early warning dissemination. However, the moving target’s defocus/displaced signal will mask the image of GBSAR, which affects the accuracy of deformation inversion. Hence, the detection of moving targets in GBSAR imagery is essential. An algorithm for moving target detection based on refocusing is proposed in this paper to address this problem. The algorithm establishes a two-dimensional parameter search space for squint angle and relative speed. Based on the parameter searching, the improved Range Doppler (RD) algorithm is used for refocusing. The optimal 2D parameters are searched via an algorithm combining the entropy minimization principle and the enlightend search. The presence of a moving target in the observation area is determined based on whether there is an optimal parameter to minimize the entropy value of the refocused image. This approach enables the detection of moving targets in GBSAR imagery. The proposed method is verified by the synthetic data.

Extreme-point Symmetric Mode Decomposition-based Energy Integral Model for Bridge Abnormality Detection Using Ground-based Synthetic Aperture Radar

Extreme-point Symmetric Mode Decomposition-based Energy Integral Model for Bridge Abnormality Detection Using Ground-based Synthetic Aperture Radar

Enabling High-Resolution Micro-Vibration Detection Using Ground-Based Synthetic Aperture Radar: A Case Study for Pipeline Monitoring

The wellbeing of pipelines is influenced by a range of factors, such as internal and external pressures, as well as deterioration over time due to issues like erosion and corrosion. It is thus essential to establish a reliable monitoring system that can precisely examine pipeline behavior over time in order to prevent potential damages. To this end, pipelines are inspected based on internal and external approaches. Radar, as a non-contact sensing system, can be a suitable choice for external pipeline inspection. Radar is capable of the transmission and receiving of thousands of signals in a second, which reconstructs the displacement signal and is used for a vibration analysis. Synthetic aperture radar (SAR) imaging adds cross-range resolution to radar signals. However, a data acquisition rate of longer than several seconds makes it unsuitable for sub-second vibration monitoring. This study aims to address this limitation by presenting a method for high-resolution vibration monitoring using ground-based SAR (GBSAR) signals. To this end, a signal processing method by modifying the radar’s signal model is presented, which allows for estimating scattering targets’ vibration parameters and angle of arrival with high resolution. The proposed method is validated with numerical simulation and a real case study comprising water pipelines. Moreover, various analyses are presented for the in-depth evaluation of the method’s performance in different situations. The results indicate that the proposed method can be effective in detecting pipeline vibration frequencies with micro-scale amplitudes while providing high spatial resolution for generating accurate vibration maps of pipelines. Also, the comparison with the radar observations shows a high degree of agreement between the frequency responses with the maximum error of 0.25 Hz in some rare instances.

Evaluation of Atmospheric Phase Correction Performance in 79 GHz Ground-Based Radar Interferometry: A Comparison with 17 GHz Ground-Based SAR Data

Ground-based radar interferometry is capable of measuring target displacement to sub-mm accuracy. W-band ground-based radar has recently been investigated as a potential application for structural health monitoring. On the other hand, the application of W-band ground-based radar for natural slope monitoring is considered in this study due to its advantages in portability and recent cost-effective solutions. In radar interferometry, atmospheric phase screen (APS) is the most relevant phase disturbance that should be corrected for accurate displacement measurement. However, the APS effects in W-band radar interferometry have rarely been discussed. In this context, we study and evaluate the impacts of APS and its potential correction methods for 79 GHz ground-based radar interferometry using multiple-input and multiple-output (MIMO) radar. This paper presents an experimental investigation of a 79 GHz radar system using two types of field experiments conducted in an open flat field and a quarry site. In addition to the W-band radar system, a Ku-band (17 GHz) ground-based synthetic aperture radar (GB-SAR) system was jointly tested to compare different operating frequency bands. The result confirmed the accurate displacement estimation capability of the 79 GHz radar with an appropriate APS correction.

Two-Step CFAR-Based 3D Point Cloud Extraction Method for Circular Scanning Ground-Based Synthetic Aperture Radar

Ground-Based Synthetic Aperture Radar (GBSAR) has non-contact, all-weather, high resolution imaging and microdeformation sensing capabilities, which offers advantages in applications such as building structure monitoring and mine slope deformation retrieval. The Circular Scanning Ground-Based Synthetic Aperture Radar (CS-GBSAR) is one of its newest developed working mode, in which the radar rotates around an axis in a vertical plane. Such nonlinear observation geometry brings the unique advantage of three-dimensional (3D) imaging compared with traditional GBSAR modes. However, such nonlinear observation geometry causes strong sidelobes in SAR images, which makes it a difficult task to extract point cloud data. The Conventional Cell Averaging Constant False Alarm Rate (CA-CFAR) algorithm can extract 3D point cloud data layer-by-layer at different heights, which is time consuming and is easily influenced by strong sidelobes to obtain inaccurate results. To address these problems, this paper proposes a new two-step CFAR-based 3D point cloud extraction method for CS-GBSAR, which can extract accurate 3D point cloud data under the influence of strong sidelobes. It first utilizes maximum projection to obtain three-view images from 3D image data. Then, the first step CA-CFAR is applied to obtain the coarse masks of three-views. Then, the volume mask in the original 3D image is obtained via inverse projection. This can remove strong sidelobes outside the potential target region and obtain potential target area data by intersecting it with the SAR 3D image. Then, the second step CA-CFAR is applied to the potential target area data to obtain 3D point clouds. Finally, to further eliminate the residual strong sidelobes and output accurate 3D point clouds, the modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is applied. The original DBSCAN method uses a spherical template to cluster. It covers more points, which is easily influenced by the strong sidelobe. Hence, the clustering results have more noise points. Meanwhile, modified DBSCAN clusters have a cylindrical template to accommodate the data’s features, which can reduce false clustering. The proposed method is validated via real data acquired by the North China University of Technology (NCUT)-developed CS-GBSAR system. The laser detection and ranging (LiDAR) data are used as the reference ground truth to demonstrate the method. The comparison experiment with conventional method shows that the proposed method can reduce 95.4% false clustered points and remove the strong sidelobes, which shows the better performance of the proposed method.

A 2D-PRM-Based Atmospheric Phase Correction Method in GB-SAR Interferometry Application

Ground-based synthetic aperture radar (GB-SAR) interferometry has already been recognized as a powerful tool for remote sensing deformation monitoring. However, the accuracy and reliability of the deformation retrieval using GB-SAR instrument is severely affected by atmospheric phase disturbances. In this paper, an improved two-dimentional polynomial regression model (2D-PRM) based atmospheric phase compensation (APC) algorithm, in collaboration with the permanent scatterer (PS) technique, is developed in GB-SAR interferometry. In order to obtain the aliasing-free data required for the APC procedure, an efficient two-stage spatial phase unwrapping method is utilized. Then the reliable PS points are selected by jointly exploiting the amplitude, coherence and amplitude deviation index (ADI) information. After that, the appropriate APC is carried out by virtue of a 2D-PRM based least square (LS) method. Finally, we conduct the atmospheric correction experiments on a real campaign to verify the performance of the proposed scheme.

Analysis of Geometric Characteristics and Coverage for Moon-Based/Spaceborne Bistatic SAR Earth Observation

With the rapid development of Earth system science, a new understanding of the complete Earth system has highlighted the crucial importance of integrated observations, especially in research involving large-scale geoscience phenomena. As an active sensor with all-time and all-weather capabilities, synthetic aperture radar (SAR) has been widely used in recent decades for Earth observation. However, the existing spaceborne, airborne, and ground-based SAR systems have difficulty providing temporally consistent and spatially continuous Earth observation data on a global scale. As Earth’s only natural satellite, the Moon is a very promising Earth observation platform. By deploying a transmitter on the Moon and a receiver on the high-orbit satellite, a Moon-based/spaceborne bistatic synthetic aperture radar (MS-BiSAR) can be formed. In this paper, the MS-BiSAR geometric model of Earth observation was established using ephemeris and orbit propagators with reference system transformations, and three different MS-BiSAR configurations were used to calculate and analyze their geometric characteristics and Earth observation coverage. The results show that with the advantage of wide swaths, continuous observation capabilities, and large coverage, such an MS-BiSAR could significantly contribute to monitoring and understanding large-scale geoscience phenomena.

Snow profile reconstruction from tomographic UAV SAR

Snow profiles can provide reliable and detailed snow parameters for global change research, but it is still challenging to reconstruct large-scale spatiotemporally continuous snow profiles. Synthetic aperture radar (SAR) tomography (TomoSAR) has been proven to be a promising method for reconstructing the 3D structure of targets and has been used to reconstruct snow profiles from ground-based SAR. However, the experimental condition of ground-based SAR is ideal and the algorithms developed for ground-based SAR have some problems when applied to airborne and spaceborne sensors, such as large computational load and big angular diversity. In this paper, we first collected the Ku-band tomographic SAR dataset in the snow season of Altay, China using the Unmanned Aerial Vehicle (UAV) platform. Then, we reconstructed the snow profile from the dataset using three spectral estimation-based focusing algorithms. According to the non-stationary characteristic of the UAV platform and the dense medium property of snow, phase calibration and medium correction were performed, respectively. Finally, we assessed the reconstruction accuracy of the snow profile and snow depth using the ground synchronous measurement data. The reconstruction accuracy of the three focusing algorithms from high to low is the MUSIC algorithm, the Capon algorithm, and the Beamforming algorithm. Among them, the average error of the MUSIC algorithm to reconstruct the snow profile is 1.13 cm, and the RMSE and MAE of the snow depth retrieval are 3.71 cm and 3.26 cm, respectively. Although TomoSAR is still widely unexplored for snow using air-based sensors, the results show that it has the ability to reconstruct snow profiles accurately, and the proposed method can provide reliable support for reconstructing the large-scale spatiotemporal continuous snow profiles from subsequent spaceborne SAR.

Joint Estimation of Ground Displacement and Atmospheric Model Parameters in Ground-Based Radar

Atmospheric delay is the primary error in ground-based synthetic aperture radar (GBSAR). The existing compensation methods include the external meteorological data correction method, the polynomial fitting method, and the persistent scatterers SAR interferometry (PSInSAR) calibration method. Combining the polynomial fitting and the persistent scatterers targets is the most popular method of GBSAR atmospheric delay compensation. However, the displacement component of the coherent target is always ignored in the atmospheric delay compensation, which is unpractical. A joint estimation method of ground displacement and atmospheric model parameters is developed in this paper. The displacement component is determined by the spatial and temporal features of the objects. The atmospheric delay component is regarded as a systematic error represented by a quadratic polynomial related to distance. The result is resolved by the least-square method. Compared to the existing method, the root-mean-square error (RMSE) of the proposed method had a significant improvement in the validation experiment. In the real in situ experiment, the time series obtained by the GBSAR had a similar trend to that acquired by the Global Positioning System (GPS) receiver. It is indicated that the proposed method can lead to a better deformation estimation by taking the deformation component into account in the atmospheric compensation.

An Atmospheric Phase Correction Method Based on Normal Vector Clustering Partition in Complicated Conditions for GB-SAR

Atmospheric phase is the main factor affecting the accuracy of ground-based synthetic aperture radar. The atmospheric phase screen (APS) may be very complicated, due to the drastic changes in atmospheric conditions, and the conventional correction methods based on regression models cannot fit and correct it effectively. Partition correction is a feasible path to improve atmospheric phase correction (APC) accuracy for complicated APS, but the overfitting problem cannot be ignored. In this article, we propose a clustering partition method, based on the normal vector of APS, which can partition the complicated APS more reasonably, and then perform APC based on the partition results. APC, and simulation experiments on measurement data, suggests that the proposed method achieves higher accuracy than the conventional model-based methods for complicated APS and avoids severe overfitting, realizing the balance between accuracy and credibility. This article verifies the feasibility and effectiveness of using APS distribution information to guide the partition and conduct APC.

Structural displacement monitoring using ground-based synthetic aperture radar

This study develops a ground-based synthetic aperture radar (GBSAR) imaging system and interferometric processing framework for structural health monitoring (SHM). Radar remote sensing for displacement monitoring plays a vital role in SHM. Radar sensors and, more specifically, GBSAR systems have gained more attention in recent studies due to their capability to overcome the limitations of other contact or non-contact SHM sensors. This paper proposes a three-dimensional SAR imaging system and advanced signal processing scheme to extract continuous (fast) displacement components, such as structural vibrations, and discontinuous (slow) displacements, such as subsidence or uplift, using SAR time-series datasets. Using controlled experiments, this study shows that the developed GBSAR can monitor sub-millimeter displacements with high spatial resolution and 0.02 mm precision in the LOS direction. Moreover, several simulations are carried out to evaluate the proposed displacement monitoring algorithms. Accordingly, the proposed continuous monitoring framework can measure fast LOS displacements with better than 0.03 mm precision by reducing the effects of radar movement, clutters, and disturbances. Also, evaluations on a simulated monitoring scene at 20 m from the GBSAR’s LOS show that the proposed discontinuous displacement monitoring algorithm can estimate 3D displacement vectors with sub-millimeter precision. The results show that the developed GBSAR can be used for repeat-pass LOS displacement monitoring and the proposed algorithms demonstrate high potential for measuring continuous sub-second LOS displacements and long-term 3D displacement vectors.

INTERFEROMETRIC PROCESSING OF A DEVELOPED MIMO GBSAR FOR DISPLACEMENT MONITORING

Abstract. This study demonstrates the interferometric processing experiments of our developed multiple-input multiple-output ground-based synthetic aperture radar (MIMO GBSAR) system. GBSAR systems are known as precise noncontact instruments for monitoring earth dynamics. In recent years W band MIMO radars have shown interesting potential in this field due to their low cost, compact size, and high phase sensitivity. MIMO capability enables the angular discrimination of multiple targets in the same range as the radar sensor. In our previous works, we developed a high-resolution MIMO GBSAR system based on the combination of MIMO radar and mechanical rail. Accordingly, this study investigates the developed system’s displacement monitoring capability by presenting a controlled experiment, using fixed and moving corner reflectors and gathering 36 time series of data. We compare and discuss the results obtained from MIMO GBSAR and MIMO radar configurations. The results show that our developed system highly agrees with MIMO radar’s interferometric measurements while providing a better target discrimination capability and higher signal noise ratio.

Multichannel Ground-Based Bistatic SAR Receiver for Single-Pass Opportunistic Tomography

Multichannel Ground-Based Bistatic SAR Receiver for Single-Pass Opportunistic Tomography

ESMD-WSST High-Frequency De-Noising Method for Bridge Dynamic Deflection Using GB-SAR

Ground-based synthetic aperture radar (GB-SAR), as a new non-contact measurement technique, has been widely applied to obtain the dynamic deflection of various bridges without corner reflectors. However, it will cause some high-frequency noise in the obtained dynamic deflection with the low signal-to-noise ratio. To solve this problem, this paper proposes an innovative high-frequency de-noising method combining the wavelet synchro-squeezing transform (WSST) method with the extreme point symmetric mode decomposition (ESMD) method. First, the ESMD method is applied to decompose the observed dynamic deflection signal into a series of intrinsic mode functions (IMFs), and the frequency boundary of the original signal autocorrelation is filtered by the mutual information entropy (MIE) for each IMF pair. Second, the high-frequency IMF components are fused into a high-frequency sub-signal. WSST is performed to remove the influence of noise to reconstruct a new sub-signal. Finally, the de-noised bridge dynamic deflection is reconstructed by the new sub-signal, the remaining IMF components, and the residual curve R. For the simulated signal with 5 dB noise, the signal-to-noise ratio (SNR) after noise reduction is increased to 11.13 dB, and the root-mean-square error (RMSE) is reduced to 0.30 mm. For the on-site experiment for the Wanning Bridge, the noise rejection ratio (NRR) is 5.48 dB, and ratio of the variance root (RVR) is 0.05 mm. The results indicate that the proposed ESMD-WSST method can retain more valid information and has a better noise reduction ability than the ESMD, WSST, and EMD-WSST methods.

A Novel GB-SAR System Based on TD-MIMO for High-Precision Bridge Vibration Monitoring

Ground-based synthetic aperture radar (GB-SAR) is a highly effective technique that is widely used in landslide and bridge deformation monitoring. GB-SAR based on multiple input multiple output (MIMO) technology can achieve high accuracy and real-time detection performance. In this paper, a novel method is proposed to design transmitting and receiving array elements, which increases the minimum spacing of the antenna by sacrificing several equivalent phase centers. In MIMO arrays, the minimum antenna spacing in the azimuth direction is doubled, which increases the variety of antenna options for this design. To improve the accuracy of the system, a new method is proposed to estimate channel phase errors, amplitude errors, and position errors. The position error is decomposed into three directions with one compensated by the phase error and two estimated by the strong point. Finally, we validate the accuracy of the system and our error estimation method through simulations and experiments. The results prove that the GB-SAR system performs well in bridge deformation and vibration monitoring with the proposed method.

Multiple-input multiple-output radar, ground-based MIMO SAR for ground deformation monitoring

ABSTRACT This study focuses on investigating the capabilities of a Multiple-input multiple-output RADAR. A Radar interferometer, based on an electronically scanned array in MIMO configuration (MIMO‐SAR), has been assessed for operational use in monitoring phenomena of geological interest, such as landslides unstable slopes. The system applies the very well‐known and proven Ground-Based Interferometric technique. It guarantees a very short refreshing time compared to traditional systems based on the mechanical movement of the radar transceiver on a rail or the mechanical steering of a real antenna. The system can monitor several phenomena having deformation rates too high to be correctly retrieved by traditional systems currently in use. Implementing a prototype termed MELISSA allowed the testing technique’s performances in two real case studies: a landslide and an unstable volcanic flank. The experimental results were compared with LISA, a well-known Ground-Based Interferometric Synthetic Aperture Radar (GBInSAR) interferometer. MELISSA allows for obtaining an excellent accuracy, better than 0.01 mm. The range and angular resolution are on the same order of magnitude as those obtained through LISA. However, the refreshing rate obtained from MELISSA, 0.01 s, guarantees a strong coherence even in challenging environmental scenarios as a flank of an active volcano.