Journal articles: 'Ground-Based Synthetic Aperture Radar (GBSAR)'

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Lim, Chee Siong, Voon Chet Koo, and Yee Kit Chan. "The Integrated Simulation and Processing Tool for Ground Based Synthetic Aperture Radar (GBSAR)." Journal of Engineering Technology and Applied Physics 1, no. 2 (December 17, 2019): 20–24. http://dx.doi.org/10.33093/jetap.2019.1.2.5.

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Ground-based Synthetic Aperture Radar (GBSAR) is a tremendous example of the extended applications of Synthetic Aperture Radar (SAR). GBSAR is extremely useful in human-made structure observations, terrain mapping, landslide monitoring and many more. However, the process of designing and developing the GBSAR system is rather costly and time-consuming. It would be of a great advantage for system designers to have a realistic simulation and designing tool to anticipate the results before the implementation of the final design. In this paper, we are going to present the integrated simulation and designing tool that we have developed for a generic GBSAR system. We named it iSIM v2.0.

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Siong Lim, Chee, Voon Chet Koo, and Yee Kit Chan. "The Integrated Simulation and Processing Tool for Ground Based Synthetic Aperture Radar (GBSAR)." Journal of Engineering Technology and Applied Physics 1, no. 2 (December 17, 2019): 20–24. http://dx.doi.org/10.33093/jetap.2019.1.2.50.

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Ground-based Synthetic Aperture Radar (GBSAR) is a tremendous example of the extended applications of Synthetic Aperture Radar (SAR). GBSAR is extremely useful in human-made structure observations, terrain mapping, landslide monitoring and many more. However, the process of designing and developing the GBSAR system is rather costly and time-consuming. It would be of a great advantage for system designers to have a realistic simulation and designing tool to anticipate the results before the implementation of the final design. In this paper, we are going to present the integrated simulation and designing tool that we have developed for a generic GBSAR system. We named it iSIM v2.0

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Crosetto, M., O. Monserrat, G. Luzi, N. Devanthéry, M. Cuevas-González, and A. Barra. "DATA PROCESSING AND ANALYSIS TOOLS BASED ON GROUND-BASED SYNTHETIC APERTURE RADAR IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 593–96. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-593-2017.

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The Ground-Based SAR (GBSAR) is a terrestrial remote sensing technique used to measure and monitor deformation. In this paper we describe two complementary approaches to derive deformation measurements using GBSAR data. The first approach is based on radar interferometry, while the second one exploits the GBSAR amplitude. In this paper we consider the so-called discontinuous GBSAR acquisition mode. The interferometric process is not always straightforward: it requires appropriate data processing and analysis tools. One of the main critical steps is phase unwrapping, which can critically affect the deformation measurements. In this paper we describe the procedure used at the CTTC to process and analyse discontinuous GBSAR data. In the second part of the paper we describe the approach based on GBSAR amplitude images and an image-matching method.

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Pieraccini, Massimiliano, Neda Rojhani, and Lapo Miccinesi. "Compressive Sensing for Ground Based Synthetic Aperture Radar." Remote Sensing 10, no. 12 (December 5, 2018): 1960. http://dx.doi.org/10.3390/rs10121960.

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Compressive sensing (CS) is a recent technique that promises to dramatically speed up the radar acquisition. Previous works have already tested CS for ground-based synthetic aperture radar (GBSAR) performing preliminary simulations or carrying out measurements in controlled environments. The aim of this article is a systematic study on the effective applicability of CS for GBSAR with data acquired in real scenarios: an urban environment (a seven-storey building), an open-pit mine, and a natural slope (a glacier in the Italian Alps). The authors tested the most popular sets of orthogonal functions (the so-called ‘basis’) and three different recovery methods (l1-minimization, l2-minimization, orthogonal pursuit matching). They found that Haar wavelets as orthogonal basis is a reasonable choice in most scenarios. Furthermore, they found that, for any tested basis and recovery method, the quality of images is very poor with less than 30% of data. They also found that the peak signal–noise ratio (PSNR) of the recovered images increases linearly of 2.4 dB for each 10% increase of data.

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Hosseiny, B., J. Amini, and H. Aghababaei. "INTERFEROMETRIC PROCESSING OF A DEVELOPED MIMO GBSAR FOR DISPLACEMENT MONITORING." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 13, 2023): 301–6. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-301-2023.

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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.

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Zhang, H. Y., Q. P. Zhai, L. Chen, Y. J. Liu, K. Q. Zhou, Y. S. Wang, and Y. D. Dou. "THE MONITORING CASE OF GROUND-BASED SYNTHETIC APERTURE RADAR WITH FREQUENCY MODULATED CONTINUOUS WAVE SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 671–74. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-671-2017.

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The features of the landslide geological disaster are wide distribution, variety, high frequency, high intensity, destructive and so on. It has become a natural disaster with harmful and wide range of influence. The technology of ground-based synthetic aperture radar is a novel deformation monitoring technology developed in recent years. The features of the technology are large monitoring area, high accuracy, long distance without contact and so on. In this paper, fast ground-based synthetic aperture radar (Fast-GBSAR) based on frequency modulated continuous wave (FMCW) system is used to collect the data of Ma Liuzui landslide in Chongqing. The device can reduce the atmospheric errors caused by rapidly changing environment. The landslide deformation can be monitored in severe weather conditions (for example, fog) by Fast-GBSAR with acquisition speed up to 5 seconds per time. The data of Ma Liuzui landslide in Chongqing are analyzed in this paper. The result verifies that the device can monitor landslide deformation under severe weather conditions.

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Pieraccini, Massimiliano, and Lapo Miccinesi. "Cross-Pol Transponder with Frequency Shifter for Bistatic Ground-Based Synthetic Aperture Radar." Remote Sensing 10, no. 9 (August 28, 2018): 1364. http://dx.doi.org/10.3390/rs10091364.

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Ground-based synthetic aperture radar (GBSAR) systems are popular remote sensing instruments for detecting the ground changes of landslides, glaciers, and open pits as well as for detecting small displacements of large structures, such as bridges and dams. Recently (2017), a novel mono/bistatic GBSAR configuration was proposed to acquire two different components of displacement of the targets in the field of view. This bistatic configuration relies on a transponder that consists—in its basic implementation—of just two antennas and an amplifier. The aim of this article was to design and experimentally test an improved transponder with cross-polarized antennas and frequency shifter that is able to prevent possible oscillations even at very high gain, as required in long-range applications. The transponder was successfully field-tested, and its measured gain was 91 dB gain.

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Wang, Zheng, Zhenhong Li, Yanxiong Liu, Junhuan Peng, Sichun Long, and Jon Mills. "A New Processing Chain for Real-Time Ground-Based SAR (RT-GBSAR) Deformation Monitoring." Remote Sensing 11, no. 20 (October 20, 2019): 2437. http://dx.doi.org/10.3390/rs11202437.

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Due to the high temporal resolution (e.g., 10 s) required, and large data volumes (e.g., 360 images per hour) that result, there remain significant issues in processing continuous ground-based synthetic aperture radar (GBSAR) data. This includes the delay in creating displacement maps, the cost of computational memory, and the loss of temporal evolution in the simultaneous processing of all data together. In this paper, a new processing chain for real-time GBSAR (RT-GBSAR) is proposed on the basis of the interferometric SAR small baseline subset concept, whereby GBSAR images are processed unit by unit. The outstanding issues have been resolved by the proposed RT-GBSAR chain with three notable features: (i) low requirement of computational memory; (ii) insights into the temporal evolution of surface movements through temporarily-coherent pixels; and (iii) real-time capability of processing a theoretically infinite number of images. The feasibility of the proposed RT-GBSAR chain is demonstrated through its application to both a fast-changing sand dune and a coastal cliff with submillimeter precision.

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Wang, Yanping, Yang Song, Yun Lin, Yang Li, Yuan Zhang, and Wen Hong. "Interferometric DEM-Assisted High Precision Imaging Method for ArcSAR." Sensors 19, no. 13 (July 1, 2019): 2921. http://dx.doi.org/10.3390/s19132921.

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Ground-based arc-scanning synthetic aperture radar (ArcSAR) is the novel ground-based synthetic aperture radar (GBSAR). It scans 360-degree surrounding scenes by the antenna attached to rotating boom. Therefore, compared with linear scanning GBSAR, ArcSAR has larger field of view. Although the feasibility of ArcSAR has been verified in recent years, its imaging algorithm still presents difficulties. The imaging accuracy of ArcSAR is affected by terrain fluctuation. For rotating scanning ArcSAR, even if targets in scenes have the same range and Doppler with antenna, if the heights of targets are different, their range migration will be different. Traditional ArcSAR imaging algorithms achieve imaging on reference plane. The height difference between reference plane and target in scenes will cause the decrease of imaging quality or even image defocusing because the range migration cannot be compensated correctly. For obtaining high-precision ArcSAR image, we propose interferometric DEM (digital elevation model)-assisted high precision imaging method for ArcSAR. The interferometric ArcSAR is utilized to acquire DEM. With the assist of DEM, target in scenes can be imaged on its actual height. In this paper, we analyze the error caused by ArcSAR imaging on reference plane. The method of extracting DEM on ground range for assisted ArcSAR imaging is also given. Besides, DEM accuracy and deformation monitoring accuracy of proposed method are analyzed. The effectiveness of the proposed method was verified by experiments.

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Long, Sichun, Aixia Tong, Ying Yuan, Zhenhong Li, Wenhao Wu, and Chuanguang Zhu. "New Approaches to Processing Ground-based SAR (GBSAR) Data for Deformation Monitoring." Remote Sensing 10, no. 12 (December 1, 2018): 1936. http://dx.doi.org/10.3390/rs10121936.

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In this paper, aiming at the limitation of persistence scatterers (PS) points selection, a new method for selecting PS points has been introduced based on the average coherence coefficient, amplitude dispersion index, estimated signal-to-noise ratio and displacement standard deviation of multiple threshold optimization. The stability and quality of this method are better than that of a single model. In addition, an atmospheric correction model has also been proposed to estimate the atmospheric effects on Ground-based synthetic aperture radar (GBSAR) observations. After comparing the monitoring results before and after correction, we clearly found that the results are in good agreement with the actual observations after applying the proposed atmospheric correction approach.

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Kačan, Marin, Filip Turčinović, Dario Bojanjac, and Marko Bosiljevac. "Deep Learning Approach for Object Classification on Raw and Reconstructed GBSAR Data." Remote Sensing 14, no. 22 (November 10, 2022): 5673. http://dx.doi.org/10.3390/rs14225673.

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The availability of low-cost microwave components today enables the development of various high-frequency sensors and radars, including Ground-based Synthetic Aperture Radar (GBSAR) systems. Similar to optical images, radar images generated by applying a reconstruction algorithm on raw GBSAR data can also be used in object classification. The reconstruction algorithm provides an interpretable representation of the observed scene, but may also negatively influence the integrity of obtained raw data due to applied approximations. In order to quantify this effect, we compare the results of a conventional computer vision architecture, ResNet18, trained on reconstructed images versus one trained on raw data. In this process, we focus on the task of multi-label classification and describe the crucial architectural modifications that are necessary to process raw data successfully. The experiments are performed on a novel multi-object dataset RealSAR obtained using a newly developed 24 GHz (GBSAR) system where the radar images in the dataset are reconstructed using the Omega-k algorithm applied to raw data. Experimental results show that the model trained on raw data consistently outperforms the image-based model. We provide a thorough analysis of both approaches across hyperparameters related to model pretraining and the size of the training dataset. This, in conclusion, shows how processing raw data provides overall better classification accuracy, it is inherently faster since there is no need for image reconstruction and it is therefore useful tool in industrial GBSAR applications where processing speed is critical.

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Miccinesi, Lapo, Tommaso Consumi, Alessandra Beni, and Massimiliano Pieraccini. "W-band MIMO GB-SAR for Bridge Testing/Monitoring." Electronics 10, no. 18 (September 14, 2021): 2261. http://dx.doi.org/10.3390/electronics10182261.

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Interferometric radars are widely used for static and dynamic monitoring of large structures such as bridges, culverts, wind turbine towers, chimneys, masonry towers, stay cables, buildings, and monuments. Most of these radars operate in Ku-band (17 GHz). Nevertheless, a higher operative frequency could allow the design of smaller, lighter, and faster equipment. In this paper, a fast MIMO-GBSAR (Multiple-Input Multiple-Output Ground-Based Synthetic Aperture Radar) operating in W-band (77 GHz) has been proposed. The radar can complete a scan in less than 8 s. Furthermore, as its overall dimension is smaller than 230 mm, it can be easily fixed to the head of a camera tripod, which makes its deployment in the field very easy, even by a single operator. The performance of this radar was tested in a controlled environment and in a realistic case study.

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Beni, Alessandra, Lapo Miccinesi, Alberto Michelini, and Massimiliano Pieraccini. "Temporal Coherence Estimators for GBSAR." Remote Sensing 14, no. 13 (June 24, 2022): 3039. http://dx.doi.org/10.3390/rs14133039.

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Many Ground-Based Synthetic Aperture Radar (GBSAR) applications demand preliminary analysis to select areas with high-quality signal. That is, areas in which the phase can be processed to extract the desired information. The interferometric coherence and the amplitude dispersion index are important tools widely used in the literature to assess the quality of GBSAR images. So far, no direct relation has been found between the two. Indeed, they are parameters of different natures: amplitude dispersion index is calculated with only amplitude values, while coherence provides information also on the signal phase. The purpose of this article is to find a relation between the two parameters. Indeed, the amplitude dispersion index provides some practical advantages if compared to coherence estimators, especially to perform fast preliminary analysis. In this article, a theoretical relation between amplitude dispersion index and coherence is retrieved. GBSAR measurements acquired in different scenarios, at different working frequencies are presented and used to validate such a relation.

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Beni, Alessandra, Lapo Miccinesi, and Massimiliano Pieraccini. "Comparison between Compressive Sensing and Non-Uniform Array for a MIMO GBSAR with Elevation Resolution: Simulations and Experimental Tests." Electronics 12, no. 5 (February 23, 2023): 1100. http://dx.doi.org/10.3390/electronics12051100.

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Ground-based synthetic aperture radars (GBSAR) are popular instruments widely used for the monitoring of infrastructures. One of the main problems of ground-based interferometric radars is the elevation ambiguity. Multiple-input multiple-output (MIMO) arrays could solve this problem. This work proposes a study on possible MIMO configurations to achieve elevation resolution in ground-based radar measurements. Specifically, two array configurations are compared: a random sparse array suitable for the compressive sensing technique, and a non-uniform array. The two solutions are compared by means of simulations and experimental tests. An ad hoc system has been developed to jointly test the two configurations, and results obtained in a controlled and real urban scenario are shown. It is found that both systems are able to solve elevation ambiguity. The non-uniform array seems to achieve good performance in a general scenario, while the CS processing can outperform the other only after optimization, depending on the specific scenario and application.

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Lin, Yun, Yutong Liu, Yanping Wang, Shengbo Ye, Yuan Zhang, Yang Li, Wei Li, Hongquan Qu, and Wen Hong. "Frequency Domain Panoramic Imaging Algorithm for Ground-Based ArcSAR." Sensors 20, no. 24 (December 8, 2020): 7027. http://dx.doi.org/10.3390/s20247027.

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The ground-based arc-scanning synthetic aperture radar (ArcSAR) is capable of 360° scanning of the surroundings with the antenna fixed on a rotating arm. ArcSAR has much wider field of view when compared with conventional ground-based synthetic aperture radar (GBSAR) scanning on a linear rail. It has already been used in deformation monitoring applications. This paper mainly focuses on the accurate and fast imaging algorithms for ArcSAR. The curvature track makes the image focusing challenging and, in the classical frequency domain, fast imaging algorithms that are designed for linear rail SAR cannot be readily applied. This paper proposed an efficient frequency domain imaging algorithm for ArcSAR. The proposed algorithm takes advantage of the angular shift-invariant property of the ArcSAR signal, and it deduces the accurate matched filter in the angular-frequency domain, so panoramic images in polar coordinates with wide swath can be obtained at one time without segmenting strategy. When compared with existing ArcSAR frequency domain algorithms, the proposed algorithm is more accurate and efficient, because it has neither far range nor narrow beam antenna restrictions. The proposed method is validated by both simulation and real data. The results show that our algorithm brings the quality of image close to the time domain back-projection (BP) algorithm at a processing efficiency about two orders of magnitude better, and it has better image quality than the existing frequency domain Lee’s algorithm at a comparable processing speed.

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Lim, Chee Siong, Yee Kit Chan, Voon Chet Koo, and William How-Hsin Hii. "PHASE STATISTICAL MODEL AND CORRECTION IN IMAGERY OF GROUND BASED SYNTHETIC APERTURE RADAR (GBSAR) FOR LAND DEFORMATION MONITORING." Progress In Electromagnetics Research C 97 (2019): 189–200. http://dx.doi.org/10.2528/pierc19090506.

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Liu, Wang, Huang, and Yang. "An Improved Second-Order Blind Identification (SOBI) Signal De-Noising Method for Dynamic Deflection Measurements of Bridges Using Ground-Based Synthetic Aperture Radar (GBSAR)." Applied Sciences 9, no. 17 (August 30, 2019): 3561. http://dx.doi.org/10.3390/app9173561.

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Ground-based synthetic aperture radar (GBSAR) technology has been widely used for bridge dynamic deflection measurements due to its advantages of non-contact measurements, high frequency, and high accuracy. To reduce the influence of noise in dynamic deflection measurements of bridges using GBSAR—especially for noise of the instantaneous vibrations of the instrument itself caused by passing vehicles—an improved second-order blind identification (SOBI) signal de-noising method is proposed to obtain the de-noised time-series displacement of bridges. First, the obtained time-series displacements of three adjacent monitoring points in the same time domain are selected as observation signals, and the second-order correlations among the three time-series displacements are removed using a whitening process. Second, a mixing matrix is calculated using the joint approximation diagonalization technique for covariance matrices and to further obtain three separate signal components. Finally, the three separate signal components are converted in the frequency domain using the fast Fourier transform (FFT) algorithm, and the noise signal components are identified using a spectrum analysis. A new, independent, separated signal component matrix is generated using a zeroing process for the noise signal components. This process is inversely reconstructed using a mixing matrix to recover the original amplitude of the de-noised time-series displacement of the middle monitoring point among three adjacent monitoring points. The results of both simulated and on-site experiments show that the improved SOBI method has a powerful signal de-noising ability.

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Peng, J., J. Cai, and H. Yang. "A RAIL CENTRAL DISPLACEMENT METHOD ABOUT GB-SAR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 865–71. http://dx.doi.org/10.5194/isprs-archives-xli-b7-865-2016.

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This paper presents a new method to correct rail errors of Ground Based Synthetic Aperture Radar (GB-SAR) in the discontinue mode. Generally, “light positioning” is performed to mark the GB-SAR position in the dis-continuous observation mode. Usually we assume there is no difference between the marked position and the real installation position. But in fact, it is hard to keep the GB-SAR positions of two campaigns the same, so repositioning errors can’t be neglected. In order to solve this problem, we propose an algorithm to correct the rail error after analyzing the GB-SAR rail error geometry. Results of the simulation experiment and the real experiment of a landslide in Lvliang, Shanxi, China, show the proposed method achieves an mm-level precision, enabling the D-GBSAR mode to be used in engineering projects.

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Peng, J., J. Cai, and H. Yang. "A RAIL CENTRAL DISPLACEMENT METHOD ABOUT GB-SAR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 865–71. http://dx.doi.org/10.5194/isprsarchives-xli-b7-865-2016.

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This paper presents a new method to correct rail errors of Ground Based Synthetic Aperture Radar (GB-SAR) in the discontinue mode. Generally, “light positioning” is performed to mark the GB-SAR position in the dis-continuous observation mode. Usually we assume there is no difference between the marked position and the real installation position. But in fact, it is hard to keep the GB-SAR positions of two campaigns the same, so repositioning errors can’t be neglected. In order to solve this problem, we propose an algorithm to correct the rail error after analyzing the GB-SAR rail error geometry. Results of the simulation experiment and the real experiment of a landslide in Lvliang, Shanxi, China, show the proposed method achieves an mm-level precision, enabling the D-GBSAR mode to be used in engineering projects.

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Han, Jianfeng, Honglei Yang, Youfeng Liu, Zhaowei Lu, Kai Zeng, and Runcheng Jiao. "A Deep Learning Application for Deformation Prediction from Ground-Based InSAR." Remote Sensing 14, no. 20 (October 11, 2022): 5067. http://dx.doi.org/10.3390/rs14205067.

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Ground-based synthetic aperture radar interferometry (GB-InSAR) has the characteristics of high precision, high temporal resolution, and high spatial resolution, and is widely used in highwall deformation monitoring. The traditional GB-InSAR real-time processing method is to process the whole data set or group in time sequence. This type of method takes up a lot of computer memory, has low efficiency, cannot meet the timeliness of slope monitoring, and cannot perform deformation prediction and disaster warning forecasting. In response to this problem, this paper proposes a GB-InSAR time series processing method based on the LSTM (long short-term memory) model. First, according to the early monitoring data of GBSAR equipment, the time series InSAR method (PS-InSAR, SBAS, etc.) is used to obtain the initial deformation information. According to the deformation calculated in the previous stage and the atmospheric environmental parameters monitored, the LSTM model is used to predict the deformation and atmospheric delay at the next time. The phase is removed from the interference phase, and finally the residual phase is unwrapped using the spatial domain unwrapping algorithm to solve the residual deformation. The predicted deformation and the residual deformation are added to obtain the deformation amount at the current moment. This method only needs to process the difference map at the current moment, which greatly saves time series processing time and can realize the prediction of deformation variables. The reliability of the proposed method is verified by ground-based SAR monitoring data of the Guangyuan landslide in Sichuan Province.

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Lee, Hoonyol, Younghun Ji, and Hyangsun Han. "Experiments on a Ground-Based Tomographic Synthetic Aperture Radar." Remote Sensing 8, no. 8 (August 18, 2016): 667. http://dx.doi.org/10.3390/rs8080667.

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Hosseiny, Benyamin, Jalal Amini, and Hossein Aghababaei. "Structural displacement monitoring using ground-based synthetic aperture radar." International Journal of Applied Earth Observation and Geoinformation 116 (February 2023): 103144. http://dx.doi.org/10.1016/j.jag.2022.103144.

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Noferini, L., M. Pieraccini, D. Mecatti, G. Macaluso, G. Luzi, and C. Atzeni. "Long term landslide monitoring by ground‐based synthetic aperture radar interferometer." International Journal of Remote Sensing 27, no. 10 (May 2006): 1893–905. http://dx.doi.org/10.1080/01431160500353908.

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Anghel, Andrei, Zegang Ding, Holger Nies, Otmar Loffeld, David Atencia, Samuel G. Huaman, Aleksander Medella, et al. "Compact Ground-Based Interferometric Synthetic Aperture Radar: Short-Range Structural Monitoring." IEEE Signal Processing Magazine 36, no. 4 (July 2019): 42–52. http://dx.doi.org/10.1109/msp.2019.2894987.

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Broquetas, A., R. De Porrata, Ll Sagués, X. Fàbregas, and Ll Jofre. "Circular synthetic aperture radar (C-SAR) system for ground-based applications." Electronics Letters 33, no. 11 (1997): 988. http://dx.doi.org/10.1049/el:19970635.

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Hamdi, I., Y. Tounsi, M. Benjelloun, and A. Nassim. "Evaluation of the change in synthetic aperture radar imaging using transfer learning and residual network." Computer Optics 45, no. 4 (July 2021): 600–607. http://dx.doi.org/10.18287/2412-6179-co-814.

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Change detection from synthetic aperture radar images becomes a key technique to detect change area related to some phenomenon as flood and deformation of the earth surface. This paper proposes a transfer learning and Residual Network with 18 layers (ResNet-18) architecture-based method for change detection from two synthetic aperture radar images. Before the application of the proposed technique, batch denoising using convolutional neural network is applied to the two input synthetic aperture radar image for speckle noise reduction. To validate the performance of the proposed method, three known synthetic aperture radar datasets (Ottawa; Mexican and for Taiwan Shimen datasets) are exploited in this paper. The use of these datasets is important because the ground truth is known, and this can be considered as the use of numerical simulation. The detected change image obtained by the proposed method is compared using two image metrics. The first metric is image quality index that measures the similarity ratio between the obtained image and the image of the ground truth, the second metrics is edge preservation index, it measures the performance of the method to preserve edges. Finally, the method is applied to determine the changed area using two Sentinel 1 B synthetic aperture radar images of Eddahbi dam situated in Morocco.

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Song, Woo-Jin, Sung-Chul Woo, and Young-Kil Kwag. "Interference Impact Analysis of Ground Based Radar from Spaceborne High Resolution Synthetic Aperture Radar." Journal of Korean Institute of Electromagnetic Engineering and Science 19, no. 6 (June 30, 2008): 663–68. http://dx.doi.org/10.5515/kjkiees.2008.19.6.663.

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Yigit, Enes, Sevket Demirci, Caner Ozdemir, and Adnan Kavak. "A synthetic aperture radar-based focusing algorithm for B-scan ground penetrating radar imagery." Microwave and Optical Technology Letters 49, no. 10 (July 27, 2007): 2534–40. http://dx.doi.org/10.1002/mop.22724.

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Jirousek, Matthias, Sebastian Iff, Simon Anger, and Markus Peichl. "GigaRad – a multi-purpose high-resolution ground-based radar – system concept, error correction strategies and performance verification." International Journal of Microwave and Wireless Technologies 7, no. 3-4 (April 16, 2015): 443–51. http://dx.doi.org/10.1017/s175907871500063x.

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Recently DLR has developed and constructed a new experimental radar instrument [5] for various applications such as radar signature collection, synthetic aperture radar/inverse synthetic aperture radarimaging, motion detection, tracking, etc., where high performance and high flexibility have been the key drivers for system design. Consequently the multi-purpose and multi-channel radar called GigaRad is operated in X and Ku band and allows an overall bandwidth of up to 6 GHz, resulting in a theoretical range resolution of up to 2.5 cm. Hence, primary obligation is a detailed analysis of various possible error sources, being of no or less relevance for low-resolution systems. A high degree of digital technology enables advanced signal processing and error correction to be applied. The paper outlines main technical features of the radar system, the basic error correction and absolute calibration strategy, frequency limitations, and illustrates some imaging results.

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Vincent, Shweta, Sharmila Francis, Kumudha Raimond, Tanweer Ali, and Prakash Kumar. "A novel planar antenna array for a ground-based synthetic aperture radar." Serbian Journal of Electrical Engineering 16, no. 2 (2019): 195–209. http://dx.doi.org/10.2298/sjee1902195v.

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Penner, Justin, and David Long. "Ground-Based 3D Radar Imaging of Trees Using a 2D Synthetic Aperture." Electronics 6, no. 1 (January 23, 2017): 11. http://dx.doi.org/10.3390/electronics6010011.

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32

Wang, Zheng, Zhenhong Li, and Jon P. Mills. "A New Nonlocal Method for Ground-Based Synthetic Aperture Radar Deformation Monitoring." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 10 (October 2018): 3769–81. http://dx.doi.org/10.1109/jstars.2018.2864740.

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Pieraccini, M., and L. Miccinesi. "Cross‐pol long‐cable transponder for bistatic ground‐based synthetic aperture radar." Electronics Letters 54, no. 21 (October 2018): 1233–35. http://dx.doi.org/10.1049/el.2018.6081.

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Feng, Weike, Jean-Michel Friedt, Giovanni Nico, Suyun Wang, Gilles Martin, and Motoyuki Sato. "Passive Bistatic Ground-Based Synthetic Aperture Radar: Concept, System, and Experiment Results." Remote Sensing 11, no. 15 (July 25, 2019): 1753. http://dx.doi.org/10.3390/rs11151753.

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A passive bistatic ground-based synthetic aperture radar (PB-GB-SAR) system without a dedicated transmitter has been developed by using commercial-off-the-shelf (COTS) hardware for local-area high-resolution imaging and displacement measurement purposes. Different from the frequency-modulated or frequency-stepped continuous wave signal commonly used by GB-SAR, the continuous digital TV signal broadcast by a geostationary satellite has been adopted by PB-GB-SAR. In order to increase the coherence between the reference and surveillance channels, frequency and phase synchronization of multiple low noise blocks (LNBs) has been conducted. Then, the back-projection algorithm (BPA) and the range migration algorithm (RMA) have been modified for PB-GB-SAR to get the focused SAR image. Field experiments have been carried out to validate the designed PB-GB-SAR system and the proposed methods. It has been found that different targets within 100 m (like the fence, light pole, tree, and car) can be imaged by the PB-GB-SAR system. With a metallic plate moved on a positioner, it has been observed that the displacement of the target can be estimated by PB-GB-SAR with submillimeter accuracy.

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Palamà, R., M. Crosetto, O. Monserrat, A. Barra, B. Crippa, M. Mróz, N. Kotulak, M. Mleczko, and J. Rapinski. "ANALYSIS OF MINING-INDUCED TERRAIN DEFORMATION USING MULTITEMPORAL DISTRIBUTED SCATTERER SAR INTERFEROMETRY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 321–26. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-321-2022.

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Abstract. This work addresses a methodology based on the Interferometric Synthetic Aperture Radar (InSAR) to analyse and monitor ground motion phenomena induced by underground mining activities, in the Legnica-Glogow Copper District, south-western Poland. Two stacks of ascending and descending Sentinel-1 Synthetic Aperture Radar (SAR) images are processed with a small baseline multitemporal approach. A simple method to select interferograms with high coherence and eliminated images with low redundancy is implemented to optimize the interferogram netwrork. The estimated displacement maps and time series show the effect of both linear and impulsive ground motion and are validated against Global Navigation Satellite System (GNSS) measurements.

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Lee, Hoonyol, and Jihyun Moon. "Analysis of a Bistatic Ground-Based Synthetic Aperture Radar System and Indoor Experiments." Remote Sensing 13, no. 1 (December 26, 2020): 63. http://dx.doi.org/10.3390/rs13010063.

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Recent advancement of satellite synthetic aperture radar (SAR) techniques require more sophisticated system configurations such as the use of bistatic antennas or multi-frequencies. A ground-based experiment is a cost-effective and efficient way to evaluate those new configurations especially in the early stage of the system development. In this paper, a ground-based synthetic aperture radar (GB-SAR) system was constructed and operated in a bistatic mode at Ku-band where a receiving antenna (Rx) follows a transmitting antenna (Tx) separated by a baseline B. A new bistatic GB-SAR focusing algorithm was developed by modifying a conventional range-Doppler algorithm (RDA), and its performance has been evaluated by comparing the results with those from a back-projection algorithm (BPA). The results showed good performance of RDA at far range approaching nominal resolutions of 9.4 cm in range and 4.5 cm in azimuth, but limited quality at near range due to the approximation used in RDA. Signals from three trihedral corner reflectors (CR) reduced with increasing B, showing a typical bidirectional scattering behavior of CR. This GB-SAR system will be a testbed for new SAR imaging configurations with variations in antenna positions and target properties.

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Kudryashov, V. V., K. A. Lukin, V. P. Palamarchuk, and P. L. Vyplavin. "COHERENT RADIOMETRIC IMAGING WITH A Ka-BAND GROUND-BASED SYNTHETIC APERTURE NOISE RADAR." Telecommunications and Radio Engineering 72, no. 8 (2013): 699–710. http://dx.doi.org/10.1615/telecomradeng.v72.i8.50.

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Weiwei, Wang, Liao Guisheng, Zhu ShengQi, and Zhang Jie. "Compressive sensing‐based ground moving target indication for dual‐channel synthetic aperture radar." IET Radar, Sonar & Navigation 7, no. 8 (October 2013): 858–66. http://dx.doi.org/10.1049/iet-rsn.2012.0135.

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Oveis, Amir Hosein, and Mohammad Ali Sebt. "Compressed sensing‐based ground MTI with clutter rejection scheme for synthetic aperture radar." IET Signal Processing 11, no. 2 (April 2017): 155–64. http://dx.doi.org/10.1049/iet-spr.2016.0156.

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Wang, Peng, Cheng Xing, and Xiandong Pan. "Reservoir Dam Surface Deformation Monitoring by Differential GB-InSAR Based on Image Subsets." Sensors 20, no. 2 (January 10, 2020): 396. http://dx.doi.org/10.3390/s20020396.

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Ground-based synthetic aperture radar interferometry (GB-InSAR) enables the continuous monitoring of areal deformation and can thus provide near-real-time control of the overall deformation state of dam surfaces. In the continuous small-scale deformation monitoring of a reservoir dam structure by GB-InSAR, the ground-based synthetic aperture radar (GB-SAR) image acquisition may be interrupted by multiple interfering factors, such as severe changes in the meteorological conditions of the monitoring area and radar equipment failures. As a result, the observed phases before and after the interruption cannot be directly connected, and the original spatiotemporal datum for the deformation measurement is lost, making the follow-up monitoring results unreliable. In this study, a multi-threshold strategy was first adopted to select coherent point targets (CPTs) by using successive GB-SAR image sequences. Then, we developed differential GB-InSAR with image subsets based on the CPTs to solve the dam surface deformation before and after aberrant interruptions. Finally, a deformation monitoring experiment was performed on an actual large reservoir dam. The effectiveness and accuracy of the abovementioned method were verified by comparing the results with measurements by a reversed pendulum monitoring system.

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Rao, Anirudh, Jungkyo Jung, Vitor Silva, Giuseppe Molinario, and Sang-Ho Yun. "Earthquake building damage detection based on synthetic-aperture-radar imagery and machine learning." Natural Hazards and Earth System Sciences 23, no. 2 (February 23, 2023): 789–807. http://dx.doi.org/10.5194/nhess-23-789-2023.

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Abstract. This article presents a framework for semi-automated building damage assessment due to earthquakes from remote-sensing data and other supplementary datasets, while also leveraging recent advances in machine-learning algorithms. The framework integrates high-resolution building inventory data with earthquake ground shaking intensity maps and surface-level changes detected by comparing pre- and post-event InSAR (interferometric synthetic aperture radar) images. We demonstrate the use of ensemble models in a machine-learning approach to classify the damage state of buildings in the area affected by an earthquake. Both multi-class and binary damage classification are attempted for four recent earthquakes, and we compare the predicted damage labels with ground truth damage grade labels reported in field surveys. For three out of the four earthquakes studied, the model is able to identify over 50 % or nearly half of the damaged buildings successfully when using binary classification. Multi-class damage grade classification using InSAR data has rarely been attempted previously, and the case studies presented in this report represent one of the first such attempts using InSAR data.

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He, Zhen-Yu, Yang Yang, Wu Chen, and Duo-Jie Weng. "Moving Target Imaging Using GNSS-Based Passive Bistatic Synthetic Aperture Radar." Remote Sensing 12, no. 20 (October 14, 2020): 3356. http://dx.doi.org/10.3390/rs12203356.

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Current studies of global navigation satellite systems (GNSS)-based bistatic synthetic aperture radar (GNSS-SAR) is focused on static objects on land. However, moving target imaging is also very significant for modern SAR systems. Imaging a moving target has two main problems. One is the unknown range cell migration; the other is the motion parameter estimation, such as the target’s velocity. This paper proposes a moving target imaging formation algorithm for GNSS-SAR. First, an approximate bistatic range history is derived to describe the phase variation of the target signal along the azimuth time. Then, a keystone transform is employed to correct the range cell migration. To address the motion parameter estimation, a chirp rate estimation method based on short-time Fourier transform and random sample consensus is proposed with high processing efficiency and robust estimation errors in low signal-to-noise ratio scenes. The estimated chirp rate can calculate the target’s velocity. Finally, azimuth compression derivation is performed to accomplish GNSS-SAR imaging. A maritime experimental campaign is conducted to validate the effectiveness of the proposed algorithm. The two cargo ships in the SAR images have good accordance with the ground truth in terms of the target-to-receiver vertical distances along the range and the ships’ length along the cross-range.

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43

Brandewie, Aaron, and Robert Burkholder. "FEKO™ Simulation of Radar Scattering from Objects in Low Earth Orbit for ISAR Imaging." Applied Computational Electromagnetics Society 35, no. 11 (February 5, 2021): 1358–59. http://dx.doi.org/10.47037/2020.aces.j.351148.

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Objects in low earth orbit such as CubeSats and the International Space Station (ISS) move with constant velocity along a linear trajectory when viewed from a ground-based radar. The small change in attitude of the object as it flies overhead permits the generation of an inverse synthetic aperture radar (ISAR) image. In this paper, Altair’s FEKO™ software is used to model the monostatic radar scattering from the ISS as a function of frequency and aspect angle. The computed data is used for generating a simulated ISAR image from a ground-based radar. The system design requirements for the radar are calculated from the radar equation.

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Hu, Jiyuan, Jiming Guo, Yi Xu, Lv Zhou, Shuai Zhang, and Kunfei Fan. "Differential Ground-Based Radar Interferometry for Slope and Civil Structures Monitoring: Two Case Studies of Landslide and Bridge." Remote Sensing 11, no. 24 (December 4, 2019): 2887. http://dx.doi.org/10.3390/rs11242887.

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Ground-based radar interferometry, which can be specifically classified as ground-based synthetic aperture radar (GB-SAR) and ground-based real aperture radar (GB-RAR), was applied to monitor the Liusha Peninsula landslide and Baishazhou Yangtze River Bridge. The GB-SAR technique enabled us to obtain the daily displacement evolution of the landslide, with a maximum cumulative displacement of 20 mm in the 13-day observation period. The virtual reality-based panoramic technology (VRP) was introduced to illustrate the displacement evolutions intuitively and facilitate the following web-based panoramic image browsing. We applied GB-RAR to extract the operational modes of the large bridge and compared them with the global positioning system (GPS) measurement. Through full-scale test and time-frequency result analysis from two totally different monitoring methods, this paper emphasized the 3-D display potentiality by combining the GB-SAR results with VRP, and focused on the detection of multi-order resonance frequencies, as well as the configure improvement of ground-based radars in bridge health monitoring.

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Thakur, P. K., R. D. Garg, S. P. Aggarwal, P. K. Garg, and J. Shi. "Snow density retrieval using SAR data: algorithm validation and applications in part of North Western Himalaya." Cryosphere Discussions 7, no. 3 (May 3, 2013): 1927–60. http://dx.doi.org/10.5194/tcd-7-1927-2013.

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Abstract. The current study has been done using Polarimetric Synthetic Aperture Radar (SAR) data to estimate the dry snow density in Manali sub-basin of Beas River located in state of Himachal Pradesh, India. SAR data from Radarsat-2 (RS2), Environmental Satellite (ENVISAT), Advanced Synthetic Aperture Radar (ASAR) and Advanced Land Observing Satellite (ALOS)-Phased Array type L-band Synthetic Aperture Radar (PALSAR) have been used. The SAR based inversion models were implemented separately for fully polarimetric RS2, PALSAR and dual polarimetric ASAR Alternate polarization System (APS) datasets in Mathematica and MATLAB software and have been used for finding out dry snow dielectric constant and snow density. Masks for forest, built area, layover and shadow were considered in estimating snow parameters. Overall accuracy in terms of R2 value and Root Mean Square Error (RMSE) was calculated as 0.85 and 0.03 g cm−3 for snow density based on the ground truth data. The retrieved snow density is highly useful for snow avalanche and snowmelt runoff modeling related studies of this region.

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46

Bähnemann, Rik, Nicholas Lawrance, Lucas Streichenberg, Jen Jen Chung, Michael Pantic, Alexander Grathwohl, Christian Waldschmidt, and Roland Siegwart. "Under the Sand: Navigation and Localization of a Micro Aerial Vehicle for Landmine Detection with Ground-Penetrating Synthetic Aperture Radar." Field Robotics 2, no. 1 (March 10, 2022): 1028–67. http://dx.doi.org/10.55417/fr.2022034.

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Ground-penetrating radar mounted on a micro aerial vehicle (MAV) is a promising tool to assist humanitarian landmine clearance. However, the quality of synthetic aperture radar images depends on accurate and precise motion estimation of the radar antennas as well as generating informative viewpoints with the MAV. This paper presents a complete and automatic airborne ground-penetrating synthetic aperture radar (GPSAR) system. The system consists of a spatially calibrated and temporally synchronized industrial grade sensor suite that enables navigation above ground level, radar imaging, and optical imaging. A custom mission planning framework allows generation and automatic execution of stripmap and circular GPSAR trajectories controlled above ground level as well as aerial imaging survey flights. A factor graph based state estimator fuses measurements from dual receiver real-time kinematic (RTK) global navigation satellite system (GNSS) and an inertial measurement unit (IMU) to obtain precise, high-rate platform positions and orientations. Ground truth experiments showed sensor timing as accurate as 0.8 µs and as precise as 0.1 µs with localization rates of 1 kHz. The dual position factor formulation improves online localization accuracy up to 40 % and batch localization accuracy up to 59 % compared to a single position factor with uncertain heading initialization. Our field trials validated a localization accuracy and precision that enables coherent radar measurement addition and detection of radar targets buried in sand. This validates the potential as an aerial landmine detection system.

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Panzner, Berthold, Andreas Jöstingmeier, and Abbas Omar. "Radar Signatures of Complex Buried Objects in Ground Penetrating Radar." International Journal of Electronics and Telecommunications 57, no. 1 (March 1, 2011): 9–14. http://dx.doi.org/10.2478/v10177-011-0001-3.

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Radar Signatures of Complex Buried Objects in Ground Penetrating RadarThe evaluation of radar signatures of buried objects for three experimental ground penetrating radar setups will be addressed in this paper. The contribution will present corresponding results and experiences. The performance of the imaging capabilities of the designed radar system will be assessed by reconstruction of complex shaped test objects, which have been placed within the ground. The influence of system parameters of the ground penetrating radar have been varied systematically in order to analyze their effects on the image quality. Among the modified parameters are the step size in transverse plane, height of the antenna over ground, frequency range, frequency points, antennas and varying instrument settings. A signal processing technique based on synthetic aperture radar has been applied on the measured raw data. The focus radius around a specific target has been analyzed concerning the compromise between image quality and processing time. The experiments demonstrate that the designed ground penetrating radar systems are capable for detection of buried objects with high resolution.

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48

Oveis, Amir Hosein, and Mohammad Ali Sebt. "Dictionary-Based Principal Component Analysis for Ground Moving Target Indication by Synthetic Aperture Radar." IEEE Geoscience and Remote Sensing Letters 14, no. 9 (September 2017): 1594–98. http://dx.doi.org/10.1109/lgrs.2017.2724854.

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Roy, Ashish K., S. A. Gangal, and C. Bhattacharya. "Calibration of High-Resolution Synthetic Aperture Radar (SAR) Image Features by Ground-Based Experiments." IETE Journal of Research 63, no. 3 (January 19, 2017): 381–91. http://dx.doi.org/10.1080/03772063.2016.1272435.

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50

Atzeni, C., M. Barla, M. Pieraccini, and F. Antolini. "Early Warning Monitoring of Natural and Engineered Slopes with Ground-Based Synthetic-Aperture Radar." Rock Mechanics and Rock Engineering 48, no. 1 (February 21, 2014): 235–46. http://dx.doi.org/10.1007/s00603-014-0554-4.

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Journal articles on the topic 'Ground-Based Synthetic Aperture Radar (GBSAR)'

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