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To ensure the proper adoption of new technologies in identifying the potential geologic hazard on tourist routes, convolutional neural network (CNN) is applied in the radar image geologic hazard information extraction. A scientific and practical geologic hazard radar identification model is built based on identification of CNN image and calculation of big data algorithm, which can effectively improve the geologic hazard identification accuracy. Through experiments, the geologic hazard radar image data are verified, and the practicality of radar image intelligent identification under CNN and big data is also verified. The results show that the images of different resolution sizes all play a significant role in identification of geologic hazard performed by CNN. However, there are differences in the performance of different CNN models. With the continuous increase in training samples, the identification accuracy of various network models is also improved. Through radar image test, the identification capability of CNN model is the best, the highest precision is 93.61%, and the geologic hazard recall rate is 98.27%. Apriori algorithm is proposed for data processing, and the running speed and efficiency of identification models are improved, with favorable identification effect in variable data sets. To sum up, this research can provide theoretical ideas and practical value for the development of potential geologic hazard identification on tourist routes.

Monitoring urban land through satellite images has rapidly developed with the advent of modern technologies, and the increasing number of satellites plays a contributing role. While optical images have a high capability in urban monitoring, they still have some limitations, including their dependence on climatic conditions and spectral information, which lead to difficulty in making a distinction between bare land, buildings and other features. The impossibility of optical imagery at night is another issue that can make the land cover classification difficult. Synthetic aperture radar (SAR) allows imaging in all climatic conditions and at nighttime, with an ability to detect phenomena based on their geometry, roughness, and location, making the land cover classification much easier. In the present study, radar Sentinel-1 images with polarization VV and VH were used for the land classification in Tabriz. Sentinel-2 images for the same time were applied as a reference for the calibration and accuracy assessment. Maximum likelihood (ML) and support vector machine (SVM) algorithms were also employed for supervised classification. In both algorithms, the classification was performed in windows with different sizes once by the SAR backscattering coefficient (σ0) and then by combining the backscattering coefficients with the statistical data obtained from the texture. The results showed that the use of radar images only with backscattering intensity resulted in poor performance while using the gray-level co-occurrence matrix (GLCM) of texture features increased the accuracy. The transmitted frequencies of radar images have different redistributions to different phenomena. The numerical results obtained from the radar image classification show that using only the radar image redistribution led to low accuracies at both VV and VH polarization, but the use of the textural analysis significantly increased the accuracy of the classifications. The statistical results obtained from the ML and SVM classifications for radar images at VV and VH polarization indicated that the latter performed better than the former. When texture analysis was not used in the classes, the classification accuracy was low with kappa values of 0.37 and 0.42 for VV and VH polarization, respectively. The use of texture analysis and obtaining the optimum window size is increase the classification accuracy with a better performance for VH polarization. The SVM classification method with a kappa coefficient of 0.72% showed better performance than the ML one with a kappa coefficient of 0.61%. Conclusively, in the absence of Sentinel-2 datasets, Sentinel-1 images are good alternatives if the preserved texture information is available for the land cover classification. Results of this research are of great importance for developing the remote sensing methods and their techniques can be considered as progressive research in the domain of remote sensing sciences.

We present a modified methodology for phase-resolved surface wave reconstruction from incoherent X-band marine radar images. The method is based on the linear wave theory and uses the linear dispersion relation to extract the valuable signals associated with gravity waves. A parameter optimization of the proposed modification is performed based on simulated synthetic radar images. The quantitative comparisons in the accuracy of the standard and modified reconstruction methods are made for both simulated and real radar images. The correlation coefficient between reconstructed and true wave elevations is improved up to 0.9–0.92 for the present modified method from 0.69 to 0.74 for the standard method for the simulated sea surfaces. The wave spectra reconstructed from the real X-band radar measurements are in good agreement with those obtained from the independent point measurement by Miros RangeFinder for both unimodal and bimodal seas.

With the rapid development of tunnel engineering in China, geological conditions encountered in tunnel construction are becoming more and more complex. Construction safety has become the top priority of tunnel construction. To avoid geological hazards such as landslides, water inrush, and mud gushing caused by blind construction, ground penetrating radar (GPR) is widely used in the medium distance ahead of a tunnel face. The advanced prediction of GPR is often interfered by factors such as environment, operation, data processing, image interpretation, and other factors, which cause final results to have errors. Therefore, it is highly necessary to track and test prediction effects, and also, to choose scientific and effective evaluation methods for accuracy of the prediction result. Based on the geological radar forecast data of the Yujiashan tunnel, this paper sums up the interference factors of the ground radar forecast and discusses the advantages and disadvantages of different evaluation methods. On this basis, the analytic hierarchy process (AHP) which has many applications in earth sciences, physics, nanotechnology and etc., is improved by integrating the principle of maximum membership of fuzzy comprehensive evaluation. Then, a four-level evaluation model is established, which judges the accuracy of GPR geological prediction. Meanwhile, two geological predictions in the Yujiashan tunnel are selected to be applied in the model. Finally, the recognition level of the accuracy evaluation model is more accurate. The comparison between the actual excavation exposure and the radar image interpretation results shows that the geological radar advance prediction results are more accurate. This verifies the rationality and applicability of using the improved AHP to evaluate the accuracy of geological radar advance prediction.

Applications of Synthetic Aperture Sensors (SAR) and particularly Differential Interferometric Synthetic Aperture Radar (DInSAR) have provided new opportunities for detecting and monitoring of slow and even fast land deformations such as landslides and also updating their inventory maps. Employing this technique has made possible continuous detection and monitoring of small land movements with high precision over a wide spread area. In this study, two image series including 12 radar images with descending orbit acquired by ASAR sensor of ENVISAT satellite and 10 radar images with ascending orbit collected by PALAR sensor of ALOS satellite were selected and processed by the DInSAR method in order to detect landslides in the Doab-Samsami basin in Chaharmahal and Bakhtiari province, Iran. Landslides detected in the study area cover over 5959 hec according to the processing of ASAR and PALSAR images collected between 2003 and 2011, whereas landslides detected by field studies cover over 5056 hec. Based on the results of the radar processing technique for detecting and mapping of landslides, the ASAR images can provide more details of slides due to their shorter wavelengths but the PALSAR images have comparatively greater penetration and lower incoherence due to the longer wavelengths. Results of the receiver operator characteristic (ROC) method show a well agreement between the landslides map provided by the DInSAR approach and field study. The area under the curve of receiver operator characteristic (ROC) curve was estimated to be 0.95 with a standard deviation of 0.02 at 95% confidence level. The Cohen’s Kappa of 0.61 indicate relatively good conformity between classification of the detected landslide distribution in the study area based on the DInSAR method and field survey.

The Lunar penetrating radar (LPR) carried by Chang’E-3 (CE-3) mission is an important application of radar in lunar exploration. An opportunity of significance to detect the information of regolith and the subsurface structure on the landing site is offered by LPR aboard the Yutu rover. On the basis of a data processing flow, a low-frequency radar image has been available for mapping subsurface structure. The noise interfered data give a huge challenge for geological stratification and interpretation. To solve the limitation imposed by noise, we adopt the shearlet transform as a promising tool for data analysis and noise attenuation. The different distributions of the noise and signal in the shearlet domain decrease the difficulty of noise attenuation. To optimize the denosing strategy, we replace a local adaptive thresholding function with the conventional hard threshold. The quality of the processed data is improved, which is helpful for geological stratification and interpretation. Finally, by combining these data with the regional geology and previous research, especially the LPR data, we can provide an interpretation of the LPR CH-1.

When ground-penetrating radar inspection is used to determine the parameters of wood and concrete structural elements employed in commercial construction, both the radar scanning process and subsequent reduction of the data obtained should be automated. An automated antenna positioner was developed for ground-penetrating radar inspection of structural elements. A laboratory mockup of this automated antenna positioner was developed and examined to determine its performance. We demonstrate that such a positioner can be used for automated radar scanning and identification of high- and low-density zones in wood structures. We also show that the automated antenna positioner can be used to measure the electrophysical parameters of the host environment. An algorithm was developed to process the images of reinforced-concrete slabs, improve radar contrast, and detect metallic inclusions. Wiener filtration is found to be effective for noise suppression for reconstruction of rebar from radar images of reinforced-concrete slabs. Ground-penetrating-radar techniques can be used to verify component integrity and identify high-and low-density zones in structural elements.

Quality assurance and condition assessment of concrete structures is an important topic world-wide due to the aging infrastructure and increasing traffic demands. Common topics include, but are not limited to, localisation of rebar or tendon ducts, geometrical irregularities, cracks, voids, honeycombing or other flaws. Non-destructive techniques such as ultrasound or radar have found regular, successful practical application but sometimes suffer from limited resolution and accuracy, imaging artefacts or restrictions in detecting certain features. Until the 1980s X-ray transmission was used in case of special demands and showed a much better resolution than other NDT techniques. However, due to safety concerns and cost issues, this method is almost never used anymore. Muon tomography has received much attention recently. Novel detectors for cosmic muons and tomographic imaging algorithms have opened up new fields of application, such as the investigation of freight containers. Muon imaging also has the potential to fill some of the gaps currently existing in concrete NDT. As a first step towards practical use and as a proof of concept we used an existing system to image the interior of a reference reinforced 600 kg concrete block. Even with a yet not optimized setup for this kind of investigation, the muon imaging results are at least of similar quality compared to ultrasonic and radar imaging, potentially even better. The data acquisition takes more time and signals contain more noise, but the images allowed to detect the same important features that are visible in conventional high energy X-ray tomography. In our experiment, we have shown that muon imaging has potential for concrete inspection. The next steps include the development of mobile detectors and optimising acquisition and imaging parameters.

In recent decades, agricultural plains have been subject to significant subsidence in different parts of the world including Iran. Tehran plain is one of these parts exposed to land subsidence. In this paper, as the most effective method to monitor the subsidence, synthetic aperture radar interferometry (InSAR) makes use of two radar images to measure the ground surface deformation between two acquisitions. For this purpose, two different radar datasets including 16 ENVISAT ASAR images spanning from 2003 to 2008 and 12 Sentinel-1A images spanning from 2016 to 2017 were applied in InSAR small baseline subset (SBAS) time series analysis. Two distinct subsidence time series achieved from the two datasets were integrated using an appropriate model which was fitted to all data obtained from two time series results from 2003 to 2017 and verified running leveling measurements. The average value of the root mean square errors (RMSEs) calculated between the model and the achieved time series at all pixels is 0.02 m which is an illustration of the accuracy of the proposed model to join the two deformation time series results. Furthermore, the integration results were additionally used to discover the storage properties of the aquifer system by stress–strain relationship and the effect of aquitard thickness on subsidence of Tehran plain by subsidence-aquitard thickness (fine-grained interbed) relationship, respectively. The stress–strain relationship shows a low correlation between subsidence and groundwater exploitation where a direct relationship between subsidence and aquitard thickness is clearly obvious.

The article presents recent capabilities of active and passive earth observation sensors along with related processing image chains, for monitoring UNESCO World Heritage properties. Exceptional heritage sites and landscapes are found in dynamic environments, whereas both anthropogenic and natural changes are observed. The use of radar and optical satellite imageries can be used as a systematic observation tool for stakeholders, to map drastic or slowly driven landscape changes towards the better protection and management of these sites and their surrounding areas. The study presents the results from the analysis of the European Copernicus Sentinel-1 and Sentinel-2 satellite images over two broader areas in the Eastern Mediterranean basin that hold important UNESCO World Heritage properties. Initially, a recent strong earthquake of a 6.7 magnitude scale in the Aegean Sea is studied using radar Sentinel-1 images. These radar images were processed through the Hybrid Pluggable Processing Pipeline (HyP3) cloud platform for analyzing both significant changes of the VV (vertical transmit, vertical receive) and VH (vertical transmit, horizontal receive) backscattering signal as well as through an Interferometric Synthetic Aperture Radar (InSAR) analysis. Then, long-term changes in Cyprus during the last two decades are monitored by a Sentinel-2 image compared to the European Corine Land Use Land Cover data of 2000. These changes are mapped after a supervised classification process using the random forest (RF) classifier. The overall results demonstrate that the recent developments of the space sector in all its segments (resolution of the sensors, the capacity to storage in the cloud, processing advancements and open-access datasets and tools) can be beneficial for monitoring UNESCO World Heritage properties.

Marsh is a wetland type characterized by hydromorphic soils, herbaceous vegetation, aquatic and emergent vegetation; usually, the apparent water surface does not exceed 25% of the area. Multi-polarized active remote sensors with different frequencies have characteristics that make them ideal for mapping and delineating marsh areas since they provide information on canopy roughness, vegetation moisture and amount of biomass. Therefore, the main objective of this study is to develop a method based on multi-frequency radar satellites images to delineate marsh areas using decision tree classification. In order to reach this objective, we sought to answer the following questions: (1) Are L-band SAR images more efficient for marshes delineation than C-band SAR images? (2) Is multi-sensor (L and C-band) integration more accurate for marsh areas delineation than a single sensor? and (3) What are the most efficient channels for marshes delineation? Our findings showed that L-band images present greater proportion correct (PC) for marshes delineation compared to C-band images. However, the greatest PC was found using integration of Alos Palsar 1 and Sentinel 1 satellites images, reaching more than 72% of correctness. Regarding the polarization importance to Alos Palsar 1 image, HVVH presented the highest importance, with 29%, followed by VH and HV polarizations, both with 28%. For Sentinel 1 image, the most important polarization was VH, with 22%, followed by VV + VH that presented 20%. HVVH polarization was the most important in Alos and Sentinel images integration, with 35%, followed by Alos Palsar HV and VH, with 34 and 33%, respectively. Thus, we concluded that the method based on SAR multi-frequency data integration used in this study can be easily applied by other researchers interested in marsh delineation since the radar images used are freely available and can be processed and manipulated in free GIS software.

Spaceborne synthetic aperture radar (SAR) performance may get degraded due to component degradation, aging, thermal variations of the environment and so on. Performance degradation should be corrected when the spaceborne SAR is in orbit. Calibration is implemented on the radar imagery by radiometric calibration which gives the relationship between digital number and backscattering coefficient. Radiometric calibration gives calibration constant, which is the key parameter to convert the SAR digital numbers into backscattering coefficient. With radiometrically calibrated image, the performance of one sensor can be compared with other sensors through multiple acquisitions to improve quantitative analysis of SAR performance. Radiometric calibration is done by ground deployed corner reflectors (CR). CRs have well-known radar cross sections, and hence, the derived radar cross sections from the SAR image are compared with the theoretical RCS to characterize the SAR sensor. An attempt is made to study the effectiveness of calibration, using different combination of CRs (various sizes and shapes) by deploying CR’s in the Jain Global institutions—JGI global Campus, Jakkasandra, Bangalore, and average sigma-naught (σ0) is estimated through multiple acquisitions of Sentinel-1A. Sentinel-1A was launched by European Space agency in 2014, having SAR as a payload. Sentinel-1A is an open source system which operates in four different modes with different resolution schemes. In this paper, Interferometric Wide swath mode of Sentinel-1A is used as that particular mode of acquisition is available over Jain Global campus. This paper presents the general criteria for selection of CRs and the measures taken during deployment. Integral method is used to estimate the calibration coefficient of Sentinel SAR data with different interpolation factors.

Satellite-based remote sensing has a key role in the monitoring earth features, but due to flaws like cloud penetration capability and selective duration for remote sensing in traditional remote sensing methods, now the attention has shifted towards the use of alternative methods such as microwave or radar sensing technology. Microwave remote sensing utilizes synthetic aperture radar (SAR) technology for remote sensing and it can operate in all weather conditions. Previous researchers have reported about effects of SAR pre-processing for urban objects detection and mapping. Preparing high accuracy urban maps are critical to disaster planning and response efforts, thus result from this study can help to users on the required pre-processing steps and its effects. Owing to the induced errors (such as calibration, geometric, speckle noise) in the radar images, these images are affected by several distortions, therefore these distortions need to be processed before any applications, as it causes issues in image interpretation and these can destroy valuable information about shapes, size, pattern and tone of various desired objects. The present work aims to utilize the sentinel-1 SAR datasets for urban studies (i.e. urban object detection through simulation of filter properties). The work uses C-band SAR datasets acquired from Sentinel-1A/B sensor, and the Google Earth datasets to validate the recognized objects. It was observed that the Refined-Lee filter performed well to provide detailed information about the various urban objects. It was established that the attempted approach cannot be generalised as one suitable method for sensing or identifying accurate urban objects from the C-band SAR images. Hence some more datasets in different polarisation combinations are required to be attempted.

In radar imaging, stochastic target models are routinely used to describe distributed scatterers. In such models, the reflectivity of a target or clutter is a realization of a stochastic process with certain autocorrelation properties. While most targets reflect the impinging electromagnetic radiation instantaneously, some targets with complicated geometry and/or material composition may exhibit delayed scattering. Detecting such delays will provide valuable data for target identification. However, the scattering delay can be confused with the signal propagation delay, and this difference is sometimes rather subtle. Due to the stochastic nature of the radar data, the classification errors are inevitable. The misclassification rate depends on the parameters characterizing the radar system, imaging scene, and observation settings. A convolutional neural network is applied to the problem of discrimination between the instantaneous and delayed targets in synthetic aperture radar images. A trained neural network demonstrates the discrimination quality commensurate with that of the benchmark maximum likelihood-based classifier.

The existence of walls surrounding targets leads to multi-path return at the radar receiver, which provides additional information about the target, and thus can be exploited to strengthen the quality of through-wall radar imaging (TWRI). Based on this, a multi-path exploitation method is proposed to identify the location of the multi-path ghost. An algorithm that combining the modified Green’s function with back projection algorithm is presented to associate and map the multi-path ghosts to the location of real targets. The theoretical analysis is verified according to the simulation results obtained using gprMAX software as well as practical radar measured data, and our proposed method is shown to outperform that in conventional multi-path exploitation method (Setlur et al., IEEE Trans. Geosci. Remote Sens. 49:4021–4034, 2011).

Situation awareness is understood as a key requirement for safe and secure shipping at sea. The primary sensor for maritime situation assessment is still the radar, with the AIS being introduced as supplemental service only. In this article, we present a framework to assess the current situation picture based on marine radar image processing. Essentially, the framework comprises a centralized IMM–JPDA multi-target tracker in combination with a fully automated scheme for track management, i.e., target acquisition and track depletion. This tracker is conditioned on measurements extracted from radar images. To gain a more robust and complete situation picture, we are exploiting the aspect angle diversity of multiple marine radars, by fusing them a priori to the tracking process. Due to the generic structure of the proposed framework, different techniques for radar image processing can be implemented and compared, namely the BLOB detector and SExtractor. The overall framework performance in terms of multi-target state estimation will be compared for both methods based on a dedicated measurement campaign in the Baltic Sea with multiple static and mobile targets given.

As one of the important sea state parameters for navigation safety and coastal resource management, the ocean wave direction represents the propagation direction of the wave. A novel algorithm based on an optical flow method is developed for the ocean wave direction inversion of the ocean wave fields imaged by the X-band radar continuously. The proposed algorithm utilizes the echo images received by the X-band wave monitoring radar to estimate the optical flow motion, and then the actual wave propagation direction can be obtained by taking a weighted average of the motion vector for each pixel. Compared with the traditional ocean wave direction inversion method based on frequency-domain, the novel algorithm is fully using a time-domain signal processing method without determination of a current velocity and a modulation transfer function (MTF). In the meantime, the novel algorithm is simple, efficient and there is no need to do something more complicated here. Compared with traditional ocean wave direction inversion method, the ocean wave direction of derived by using this proposed method matches well with that measured by an in situ buoy nearby and the simulation data. These promising results demonstrate the efficiency and accuracy of the algorithm proposed in the paper.

To improve the accuracy of nowcasting, a new extrapolation technique called particle filter blending was configured in this study and applied to experimental nowcasting. Radar echo extrapolation was performed by using the radar mosaic at an altitude of 2.5 km obtained from the radar images of 12 S-band radars in Guangdong Province, China. The first bilateral filter was applied in the quality control of the radar data; an optical flow method based on the Lucas–Kanade algorithm and the Harris corner detection algorithm were used to track radar echoes and retrieve the echo motion vectors; then, the motion vectors were blended with the particle filter blending algorithm to estimate the optimal motion vector of the true echo motions; finally, semi-Lagrangian extrapolation was used for radar echo extrapolation based on the obtained motion vector field. A comparative study of the extrapolated forecasts of four precipitation events in 2016 in Guangdong was conducted. The results indicate that the particle filter blending algorithm could realistically reproduce the spatial pattern, echo intensity, and echo location at 30- and 60-min forecast lead times. The forecasts agreed well with observations, and the results were of operational significance. Quantitative evaluation of the forecasts indicates that the particle filter blending algorithm performed better than the cross-correlation method and the optical flow method. Therefore, the particle filter blending method is proved to be superior to the traditional forecasting methods and it can be used to enhance the ability of nowcasting in operational weather forecasts.

Extremely slow landslides, those with a displacement rate

Among the very popular Artificial Intelligence (AI) techniques, Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been retained to process Ground Echoes (GE) on meteorological radar images taken from Setif (Algeria) and Bordeaux (France) with different climates and topologies. To achieve this task, AI techniques were associated with textural approaches. We used Gray Level Co-occurrence Matrix (GLCM) and Completed Local Binary Pattern (CLBP); both methods were largely used in image analysis. The obtained results show the efficiency of texture to preserve precipitations forecast on both sites with the accuracy of 98% on Bordeaux and 95% on Setif despite the AI technique used. 98% of GE are suppressed with SVM, this rate is outperforming ANN skills. CLBP approach associated to SVM eliminates 98% of GE and preserves precipitations forecast on Bordeaux site better than on Setif’s, while it exhibits lower accuracy with ANN. SVM classifier is well adapted to the proposed application since the average filtering rate is 95–98% with texture and 92–93% with CLBP. These approaches allow removing Anomalous Propagations (APs) too with a better accuracy of 97.15% with texture and SVM. In fact, textural features associated to AI techniques are an efficient tool for incoherent radars to surpass spurious echoes.

The safe, reliable, and efficient detection and subsequent removal of buried landmines or other unexploded ordnance (UXO) still remains a challenge. According to Landmine Monitor (2015), almost 60 states suffer the threat of antipersonnel mine contamination, while the total extent of contaminated areas is likely to be thousands of square kilometers and the total number of threat objects may be well over 100 million. In contrast, total annual clearing rates are in the order of about 230,000 landmines and about 200 km2 for the year of 2014, as the number of new contaminations continues to grow. Although non-technical surveillance for the identification of contamination improves slowly, more progress is needed in the basic detection of UXO. Although many new technologies have been investigated the last 20 years, the classical ones like metal detectors and dogs in cooperation with humans operators are still the most commonly ones used today. Hence, the detection process for many scenarios is unacceptably slow and dangerous for the operators, as they are typically less than 1 m away from the threats. Hence, a sensor technology enabling a sufficient stand-off distance for operators and reliable detection at high area throughput is desirable. The following paper describes the physical background, the system design, and representative measurement results of our innovative radar approach to this problem.

This study presents a new fusion method namely supervised cross-fusion method to improve the capability of fused thermal, radar, and optical images for classification. The proposed cross-fusion method is a combination of pixel-based and supervised feature-based fusion of thermal, radar, and optical data. The pixel-based fusion was applied to fuse optical data of Sentinel-2 and Landsat 8. According to correlation coefficient (CR) and signal to noise ratio (SNR), among the used pixel-based fusion methods, wavelet obtained the best results for fusion. Considering spectral and spatial information preservation, CR of the wavelet method is 0.97 and 0.96, respectively. The supervised feature-based fusion method is a fusion of best output of pixel-based fusion level, land surface temperature (LST) data, and Sentinel-1 radar image using a supervised approach. The supervised approach is a supervised feature selection and learning of the inputs based on linear discriminant analysis and sparse regularization (LDASR) algorithm. In the present study, the non-negative matrix factorization (NMF) was utilized for feature extraction. A comparison of the obtained results with state of the art fusion method indicated a higher accuracy of our proposed method of classification. The rotation forest (RoF) classification results improvement was 25% and the support vector machine (SVM) results improvement was 31%. The results showed that the proposed method is well classified and separated four main classes of settlements, barren land, river, river bank, and even the bridges over the river. Also, a number of unclassified pixels by SVM are very low compared to other classification methods and can be neglected. The study results showed that LST calculated using thermal data has had positive effects on improving the classification results. By comparing the results of supervised cross-fusion without using LST data to the proposed method results, SVM and RoF classifiers showed 38% and 7% of classification improvement, respectively.

This paper studies karst cave and soil cavity development in the process of karst collapse as a means of disaster prevention and control. Using the karst area of the Shandong section of the Beijing–Shanghai high-speed railway as a test area, a numerical simulation method is presented as well as the model test to study the characteristics of typical radar images of different types of karst caves and soil cavities. By combining theoretical analysis with field test results, the method and characteristic standards for nondestructive testing using ground-penetrating radar to determine the grouting effect on a karst roadbed are given. The radar spectrum characteristics are analyzed for a cave, water-filled cave, clay-filled cave, karst cave with dense grouting and karst cave with grouting that is not dense. Comprehensive analysis of the results of indoor model testing and numerical analysis, combined with the engineering practice of evaluating the effect of grouting treatment in the selected section of the Beijing–Shanghai high-speed railway, clarifies the basic characteristics of map recognition of radar detection of karst grouting. The morphological characteristics of the karst caves in the geological radar images are usually composed of many hyperbolic strong reflection waves. Multiple-reflection wave groups of high amplitude, low frequency, and equal distance indicate the side wall of the cave. The bottom interface of the cave is not obvious, although when the bottom part of the cave is filled with water or clay, silt, sand and gravel material. The bottom of the reflection wave will be enhanced. When the cave is filled with material, the electromagnetic wave energy decays rapidly, the high-frequency part is absorbed, and the reflection is mostly low frequency wave. The characteristics of radar images of the slurry distribution area are obvious in the grouting zone. A horizontal direction results in arc-shaped distribution, and a vertical direction has more regular distribution. The results of the study are helpful in the exploration of karst caves and soil caves, and the evaluation of the effect of grouting treatment in mitigating karst collapse.

Faced with the challenge of thousands of frames of radar interferometric images, automated feature extraction promises to spur data understanding and highlight geophysically active land regions for further study. We have developed techniques for automatically determining surface fault slip and location using deformation images from the NASA Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), which is similar to satellitebased SAR but has more mission flexibility and higher resolution (pixels are approximately 7 m). This radar interferometry provides a highly sensitive method, clearly indicating faults slipping at levels of 10 mm or less. But interferometric images are subject to decorrelation between revisit times, creating spots of bad data in the image. Our method begins with freely available data products from the UAVSAR mission, chiefly unwrapped interferograms, coherence images, and flight metadata. The computer vision techniques we use assume no data gaps or holes; so a preliminary step detects and removes spots of bad data and fills these holes by interpolation and blurring. Detected and partially validated surface fractures from earthquake main shocks, aftershocks, and aseismicinduced slip are shown for faults in California, including El Mayor-Cucapah (M7.2, 2010), the Ocotillo aftershock (M5.7, 2010), and South Napa (M6.0, 2014). Aseismic slip is detected on the San Andreas Fault from the El Mayor-Cucapah earthquake, in regions of highly patterned partial decorrelation. Validation is performed by comparing slip estimates from two interferograms with published ground truth measurements.

The single-frequency Radar Imaging Satellite-1 (RISAT-1) Synthetic Aperture Radar (SAR) (launched in April, 2012) is designed to provide a wide variety of imaging options compared to the prevailing SAR missions. The scale of the calibration task for the RISAT-1 mission is increased with the inclusion of multiple beams and polarizations (single, dual and quad) along with circular/hybrid polarization capabilities, for the first time from space. Significant efforts have been made in the evolution of SAR processor for the provision of radiometrically and geometrically calibrated products to the users. After the commissioning phase, RISAT-1 was put into routine operations in Oct. 2012. Since then extensive beam coverage of Amazon rainforest in various modes was carried out to evaluate the calibration parameters and SAR processor performance. Subsequently, the processor version 1.2.0 was improved to version 1.3.0 and with further modifications to version 1.4.0. This article details the analysis carried out at each processor version, by quality evaluation team, to quantify the parameter behaviour and to give feedback on the processor performance to the SAR processor team. The various radiometric quality parameters evaluated and monitored are shape of the antenna pattern, gamma-naught, backscattering coefficient (sigma-naught), speckle index, radiometric resolution and Noise Equivalent Sigma Zero (NESZ). Data studied for the analysis is in Single Look Complex (SLC) for stripmap mode and multilook (Ground range) for ScanSAR and stripmap mode in dual and circular polarizations. The performance of version 1.4.0 for all the beams (near-to-far) is found to be consistent and meeting the mission specifications for the product.

Directional wave spectra and integrated wave parameters can be derived from X-band radar sea surface images. A vessel on the sea surface has a significant influence on wave parameter inversions that can be seen as intensive backscatter speckles in X-band wave monitoring radar sea surface images. A novel algorithm to eliminate the interference of vessels in ocean wave height inversions from X-band wave monitoring radar is proposed. This algorithm is based on the characteristics of the interference. The principal components (PCs) of a sea surface image sequence are extracted using empirical orthogonal function (EOF) analysis. The standard deviation of the PCs is then used to identify vessel interference within the image sequence. To mitigate the interference, a suppression method based on a frequency domain geometric model is applied. The algorithm framework has been applied to OSMAR-X, a wave monitoring system developed by Wuhan University, based on nautical X-band radar. Several sea surface images captured on vessels by OSMAR-X are processed using the method proposed in this paper. Inversion schemes are validated by comparisons with data from in situ wave buoys. The root-mean-square error between the significant wave heights (SWH) retrieved from original interference radar images and those measured by the buoy is reduced by 0.25 m. The determinations of surface gravity wave parameters, in particular SWH, confirm the applicability of the proposed method.

Two new methods for fusion of high-resolution optical and radar satellite images have been proposed to extract roads in high quality in this paper. Two fusion methods, including neural network and knowledge-based fusion are introduced. The first proposed method consists of two stages: (i) separate road detection using each dataset and (ii) fusion of the results obtained using a neural network. In this method, the neural networks are separately applied on high-resolution IKONOS and TerraSAR-X images for road detection, using a variety of texture parameters. The outputs of two neural networks, as well as the spectral features of optical image, are used in a third neural network as inputs. The second method is a knowledge-based fusion using thresholds of narrow roads and vegetation gray levels. First roads are extracted from each source separately. The outputs are then compared and advantages and disadvantages of each data source are investigated . The results obtained from accuracy assessment show the efficiency of the proposed methods. Furthermore, the comparison of the results showed the superiority of the first algorithm.

In this paper, truncated-correlation photothermal coherence tomography (TC-PCT) was used as a nondestructive inspection technique to evaluate glass-fiber reinforced polymer (GFRP) composite surface cracks. Chirped-pulsed signal that combines linear frequency modulation and pulse excitation was proposed as an excitation signal to detect GFRP composite surface cracks. The basic principle of TC-PCT and extraction algorithm of the thermal wave signal feature was described. The comparison experiments between lock-in thermography, thermal wave radar imaging and chirped-pulsed photothermal radar for detecting GFRP artificial surface cracks were carried out. Experimental results illustrated that chirped-pulsed photothermal radar has the merits of high signal-to-noise ratio in detecting GFRP composite surface cracks. TC-PCT as a depth-resolved photothermal imaging modality was employed to enable three-dimensional visualization of GFRP composite surface cracks. The results showed that TC-PCT can effectively evaluate the cracks depth of GFRP composite.

Oil pollution in the world’s oceans is predominantly caused by operational discharges from tankers (predominant oil dumped during cleaning operations). These discharges result in over 2 million tons of oil which are introduced annually comparable to one full-tanker disaster per week. Land-based sources such as urban waste and industrial discharges, which compass the ocean via rivers, are also a major contamination factor. The Caspian Sea is the largest inland water body on Earth without a direct connection to the world’s oceans, which makes it a unique water reservoir. This fact makes the Caspian Sea particularly vulnerable and subject to human influence, for example, pollution from shipping activities and oil transports by ships. The research study shows results from a monitoring campaign in northern and central parts of the Caspian Sea (Kazakhstan sector) for the period from 2005 to 2012 (April–October), based on visual interpretation of radar images from ASAR ENVISAT. Radar data (529 radar images) such as ASAR_WSM_1P and ASAR_IM_1P were obtained from the archives of the European Space Agency. In 529 ASAR ENVISAT images, 160 images were selected, on which 329 oil spills were detected. Examples of individual slicks were analyzed by visual image interpretation. The total surface of contamination for the entire study period was estimated by 822.66 km2. The average size of the oil spills ranged from 1.09 to 10.62 km2, in different years. The monitoring of oil spills over the Kazakhstan sector of the Caspian Sea could show that it is possible to state with certainty that the main source of oil pollution is shipping (confirmed by analysis of slicks form and clusters along shipping routes) and the most contaminated area is its southern part, where there is more intensive navigation in the direction of Aktau–Baku (Azerbaijan), Aktau–Turkmenbashi (Turkmenistan), and Aktau–Neka (Iran). In the northern part of the Caspian Sea, it was determined that a low number of spills could have been caused by fishing and marine activities in the development area of the Kashagan oil field. The result is based on comparing the contamination map and the map of navigation, as well as through an analysis of the geometry of the slicks. Monitoring results largely coincided with the results of work carried out in the region, and at different times for some of its sites. These findings based on the radar data showed advantages of ASAR ENVISAT satellite monitoring for the control of oil pollution on the sea surface.

In this work, we estimated soil moisture (SM) by using dielectric constant properties of soil with radar backscattering coefficient (bc) from simulated annealing techniques of RISAT-1 (radar imaging satellite, based on synthetic aperture radar (SAR) technique) data. We examined the performance of simulated annealing in retrieving SM where the vegetation cover is not very high (NDVI ≈ 0.35 for wheat-dominated area on January 19, 2013).To overcome the land surface model limits on SM estimation accuracy, point measurement spatial coverage limits, and microwave remote sensing spatial-temporal sampling limits, we reduced uncertainties through a combination of these approaches. Near-surface SM measurements from the 5.35-GHz (C-band) channels of RISAT-1 were collocated against ground-truth data (collected during the flight time of RISAT-1 over the study area), to establish SAR-SM relationships for FRS-1(circular horizontal (RH) and circular vertical (RV)) data set of RISAT-1. Comparison with the limited ground-based point (total 24 points) measurements of SM content exhibited a net improvement when near-surface SM observations were assimilated. Comparison of the SM derived from the sigma naught (σ 0) (of RISAT-1 data set FRS-1) using the inversion algorithm with the observed measurements (using time domain reflectometry) of SM showed root mean square error of 0.24, nRMSE = 0.03, R-RMSE = 0.38, MAE = 0.63, NRMSE = 1.02, NSE = 1, d = 0.87, r 2 = 0.65, and RMSE% = 12.79 for RH polarized image while RV polarized image failed each statistical test for predicting good SM with respect to the observed SM. The ability to extract additional information comes at the expense of including more measurements, especially at frequencies lower than the L-band. This approach is therefore intended for future space-borne systems.

The line-of-sight velocity of scattering facets is related to the Doppler signals of X-band coherent marine radar from the oceanic surface. First, the sign Doppler Estimator is applied to estimate the Doppler shift of each radar resolution cell. And then, in terms of the Doppler shift, a retrieval algorithm extracting the vertical displacement of the sea surface has been proposed. The effects induced by radar look-direction and radar spatial resolution are both taken into account in this retrieval algorithm. The comparison between the sea surface spectrum of buoy data and the retrieved spectrum reveals that the function of the radar spatial resolution is equivalent to a low pass filter, impacting especially the spectrum of short gravity waves. The experimental data collected by McMaster IPIX radar are also used to validate the performance of the retrieval algorithm. The derived significant wave height and wave period are compared with the in situ measurements, and the agreement indicates the practicality of the retrieval technology.

We propose a new method for determining the wind-velocity vector above the ocean surface using the data of a polarimetric synthetic aperture radar. The preliminary calculations show that for wind waves, the location of the maximum in the radar image is unambiguously related to the wind velocity, whereas the wind direction is retrieved with an uncertainty of 180°, which is related to the central symmetry of the image spectrum. To eliminate the ambiguity when determining the wind direction, a criterion based on the information on the sign of the coefficient of correlation among the complex signals on the co- and cross polarizations is used. It is shown that using the polarimetric radar, it is theoretically possible to obtain information on both the wind velocity and direction without exact radar calibration.

The tracking and imaging radar (TIRA) L-band radar of the Fraunhofer Institute for High Frequency Physics and Radar Techniques (FHR) in Wachtberg has been used since 1993 for the conduction of space debris measurement campaigns, called Beampark experiments (BPE). The results from BPEs are used for the regular validation of statistical debris models such as ESA’s MASTER model. It is an experimental radar system which is employed among other things in the frame of BPEs. In these experiments, the line-of-sight (LOS) of the radar is fixed at a defined direction and measurements are taken for 24 h. The aim of these campaigns is to obtain statistical information on the state of the space debris environment in Low Earth Orbit. BPEs are typically conducted in mono-static east-staring mode. The aim of the current paper is to gain an overview of how the measurement results of mono-static BPEs using TIRA are influenced by the direction of the LOS with a focus on south-staring BPEs. The analysis is purely theoretical and employs ESA’s Program for Radar and Optical Observation Forecasting (PROOF-2009). The paper starts out with a look at the range window as a function of the LOS elevation which is required to cover LEO altitudes up to 2000 km. This information is required to determine the minimum allowed pulse repetition interval necessary to obtain unambiguous range information. In a next step, 24 h BPEs are simulated covering azimuth angles between 0° and 360° and elevation angles between 10° and 90°. Looking at the number of crossings and detections at various object sizes, orbit types and debris sources allows an assessment of specific LOS orientations. Due to the relatively high latitude of the sensor at about 50° north, debris at low inclinations can only be detected if the radar’s LOS is directed due south. The lower the LOS elevation is, the more objects on low inclination orbits will cross TIRA’s beam. A low LOS elevation however requires an extended range window to cover the same altitude region. This leads to an increase of the minimum detectable object size with respect to altitude which is additionally aggravated by the limitation of the maximum transmit power at low elevations for reasons of electromagnetic compatibility. A trade-off study is performed to determine the most favorable LOS elevation setting for a south-staring BPE. The paper closes with a top-level look at possible approaches to decreasing the detectable object size for south-staring BPEs.

In this paper, we mainly study the system design of sparse microwave imaging radar and report some of the preliminary results of airborne experiments performed with it. Sparse microwave imaging radar is a novel concept which introduces the sparse signal processing theory to microwave imaging replacing the conventional matched filtering processing method. With the exploitation of the sparse microwave imaging, the radar system could achieve better performance. As a newly developed concept, the two main types of applications of sparse microwave imaging are found in adopting the sparse signal processing theory to current radar systems, and in designing an optimized sparse microwave imaging system. Here we are trying the latter that mainly aims at lower PRF and wider swath. We first introduce the theories of sparse microwave imaging radar and its imaging algorithm. Then we discuss the system designing principles, including the sampling scheme, signal bandwidth, SNR and multi-channel mode. Based on the relationships of these parameters, we provide a design example of radar parameters. In the end, we exploit an airborne experiment using our designed radar system with jittered azimuth sampling strategy. Some preliminary analysis from the experiment result is also provided.

A detection method of offshore area depth utilizing the x-band microwave radar is proposed. The method is based on the sea clutter imaging mechanism of microwave radar, and combined with dispersion equation of the liner wave theorem and least square method (LSM), consequently get the inversion results of water depth in the detected region. The wave monitoring system OSMAR-X exploited by the Ocean State Laboratory, Wuhan University, based on a microwave radar has proven to be a powerful tool to monitor ocean waves in time and space. Numerical simulation and inversion of offshore area depth are carried out here; since JONSWAP model can give description of stormy waves in different growth phase, it is suitable for simulation. Besides, some results from measured data detected by OSMAR-X x-band radar located at Longhai of Fujian Province, China, validates this method. The tendency of the average water depths inferred from the radar images is in good agreement with the tide level detected by Xiamen tide station. These promising results suggest the possibility of using OSMAR-X to monitor operationally morphodynamics in coastal zones. This method can be applied to both shore-based and shipborne x-band microwave radar.

A compressed sensing method with basis selection is applied to a millimeter-wave synthetic aperture radar (SAR) imaging system. With a large candidate set of bases to choose from and without any a priori knowledge of the proper basis, the proposed method selects the sparsifying basis during the first few iterations of the L1 optimization according to the information from incomplete measurements and the coherence between the measurement matrix and sparsifying matrices. Several decision metrics can be used to select the basis, including the impulsiveness and Gini index of the available image at the current iteration. The proposed method is tested on two examples: a simulated image and its SAR measurement, and an experimental measurement obtained at 150 GHz via roaster scanning. The results from the simulation and experiment indicate that the proposed algorithm can always find a very good basis from the set of over 270 bases within the first two to five iterations of the L1 optimization.

Different from conventional spaceborne or airborne synthetic aperture radar (SAR) with optimal aperture length, an imaging radar with highly suboptimal aperture length acquires the data in short bursts by a geometry spreading over a large range. A polarlike or pseudopolar format grid is introduced to sample data close to the resolution, which presents the design of a separable kernel for efficient FFT implementation. The proposed imaging algorithm formulates the reflectivity image of the target scene as an interpolation-free double image series expansion with two usual approximation-induced phase error terms being taken into account, whereby more generalized application scenarios with high frequency, large bandwidth or larger aperture length for imaging a target scene located within either the far-field or the near-field of the radar aperture are processable with high accuracy. In addition, convergence acceleration methods in computational mathematics are introduced to accelerate the convergence of the image series expansion, so as to make the algorithm more efficient. The proposed algorithm has been validated both qualitatively and quantitatively with an extensive collection of numerical simulations and field measurements of ground-based SAR (GB-SAR) data set.

Earthquakes can directly trigger multiple simultaneous slope failures in mountainous regions. Among these slope failures, flow-like landslides with high velocities and long run-outs can result in damage that is more destructive than other types of landslides. Timely and accurate recognition of the locations and magnitudes of flow-like landslides is essential for post-disaster relief. Synthetic aperture radar (SAR) sensors are suitable for deformation monitoring because of their capability to operate at day or night and in all weather conditions. Interferometric synthetic aperture radar (InSAR) is an advanced technique that extracts three-dimensional terrain and changes information from the radar images at a regional scale. The focus of this study is the Donghekou landslide-debris flow that was triggered by the 2008 Wenchuan earthquake. ALOS/PALSAR remote satellite images were interpreted by InSAR to generate digital surface elevation models. A comparison of data from a typical InSAR configuration with field survey data proves that the former is an effective method for rapidly detecting flow-like landslides in a mountainous area. The comparison also shows that the accuracy of the results is closely related to the correlation between the satellite radar images used, and further that the study accuracy would improve with the inclusion of better correlation. In this way, representations of pre- and post-landslide terrains could be generated for use in numerical simulations. The InSAR method has particular significance for areas without terrain data prior to slope failure, and can provide basic data for landslide hazard assessments.

Near Real Time (minutes or hours) radar imaging of ground targets located anywhere on an hemi- sphere, with or without interferometric coherence with previous passes, can be obtained with different solutions that are considered here. Geosynchronous systems, from the one proposed in 1978 by Tomiyasu to telecom satellite compatible solutions, and Low, Medium or Geosynchronous Earth Orbit constellations are discussed. Their benefits, problems, and sizes are briefly summarized, and a comparative table is presented. If interfer-ometric coherence is requested, continuous imaging is obtained only if a very wide geostationary aperture is progressively scanned, eventually using a MIMO (Multiple Input Multiple Output) combination of several slow librating small satellites. Instead, fast librating, strip mapping, large geosynchronous satellites do provide high resolution imaging, but interferometry (and thus coherent change detection) is achievable only after a minimum delay of 12 h, i.e., when the target comes in sight without need to squint the antenna. Hence, both complex and simple systems reach full resolution interferometric imaging and thus coherent change detection capability only after 12 h.

Optical and infrared remote sensing may provide first-order clues for the identification of potential impact structures on the Earth. Despite the free availability of at least optical image data at highest resolution, research has shown that remote sensing analysis always remains inconclusive and extensive groundwork is needed for the confirmation of the impact origin of such structures. Commonly, optical image data and digital terrain models have been employed mainly for such remote sensing studies of impact structures. With the advent of imaging radar data, a few excursions have been made to also employ radar datasets. Despite its long use, capabilities of imaging radar for studying surface and subsurface structures have not been exploited quantitatively when applied for the identification and description of such features due to the inherent complexity of backscatter processes. In this work, we make use of higher-level derived radar datasets in order to gain clearer qualitative insights that help to describe and identify potential impact structures. We make use of high-resolution data products from the ALOS PALSAR-1 and ALOS PALSAR-2 L-band sensors to describe the heavily eroded Oasis impact structure located in the Libyan Desert. While amplitude radar data with single polarization have usually been utilized to accompany the suite of remote sensing datasets when interpreting impact structures in the past, we conclude that the integration of amplitude data with HH/HV/HH–HV polarization modes in standard and, in particular, in Ultra-Fine mode, as well as entropy–alpha decomposition data, significantly helps to identify and discriminate surface units based on their consolidation. Based on the overarching structural pattern, we determined the diameter of the eroded Oasis structure at 15.6 ± 0.5 km.

We experimentally demonstrate a chaos through-wall imaging radar using ultra-wideband chaotic-pulse-position modulation (CPPM) microwave signal. The CPPM signal based on logistic map with 1-ns pulse width and 1-GHz bandwidth is implemented by a field programmable gate array (FPGA) and then up-converted as the radar transmitting signal. Two-dimensional image of human objects behind obstacles is obtained by correlation method and back projection algorithm. Our experiments successfully perform through-wall imaging for single and multiple human objects through 20-cm thick wall. The down-range resolution of the proposed radar is 15 cm. Furthermore, the anti-jamming properties of the proposed radar in CPPM jamming, linear frequency-modulated jamming, and Gaussian noise jamming environments are demonstrated by electromagnetic simulations using the finite-difference time-domain. The simulation results show the CPPM microwave signal possesses excellent jamming immunity to the noise and radio frequency interference, which makes it perform superbly in multiradar environments.

The main goal of this research was to detect oil spills, to determine the oil spill frequency, and to approximate oil leak sources around the Oil Rocks Settlement, the Chilov and Pirallahi Islands in the Caspian Sea using multi-temporal ENVISAT Advanced Synthetic Aperture Radar Wide Swath Medium-resolution images acquired in the period of 2009–2010. The processing of ENVISAT satellite images for oil spill detection included the following stages: filtering of images for noise reduction and imagery enhancement, preliminary segmentation for detection of dark features, interactive visual interpretation for the discrimination among other oil spill look-alikes, computation of oil spill frequency, and approximation of oil leak sources. The analysis showed that the appearance of the oil slicks in the radar imagery significantly depends on the Caspian Sea wind speed. The best wind speed conditions for the detection of oil spill were determined to be 2.5–14 m/s. The hot spot areas of detected oil spill frequencies for 2009–2010 were the following: 2–25 (4,658.83 sq. km.), 26–45 (302.83 sq. km.), 46–75 (77.95 sq. km.). The most critical oil leak sources were observed around the Oil Rocks Settlement. The exponential regression analysis between wind speeds and oil slick areas in 2009 with R 2 equal to 0.61 showed that larger oil spill areas were observed with lower wind speeds. The spatiotemporal patterns of currents in the Caspian Sea explained the multi-directional spatial distribution of oil spills around the Oil Rocks Settlement.

Multi-frequency synthetic aperture radar (SAR) data was acquired over the Macquarie Marshes Nature Reserve in north central New South Wales (NSW), Australia, to demonstrate the potential of imaging radar for mapping and monitoring wetland extent and inundation patterns in an inland, semi-arid wetland environment. A sequence of ALOS PALSAR and TerraSAR-X images acquired between January 2007 and April 2008 captured the wetlands in three successive phases: dry, wet and transitional. Visual observation of multi-temporal and multi-frequency SAR backscatter data confirmed the capacity of SAR to respond to changes in surface water, soil moisture and biomass. Longer wavelength L-band SAR provides a tool for discriminating wetlands, mapping surface water and detecting below-canopy inundation. Shorter wavelength X-band SAR provides a tool for detecting flushes in growth of vegetation in response to higher soil moisture from flooding. PALSAR data demonstrated a high capacity for discrimination of different wetland classes, while TerraSAR-X data was less suited to discriminating between cover types. The integration of X- and L-band data revealed the extent of floodplain inundation and presence of aquatic vegetation in ponded areas. Given a stable, well-calibrated time-series of SAR data, change analysis provides a mechanism for understanding the hydrological and/or ecological change in an area. In the longer term, it is envisioned that radar will provide a valuable tool within an operational system for monitoring the impact of environmental flows in NSW inland wetlands.

The strength of the surface wave signal in marine X-band radar (MR) images strongly depends on range and azimuth (i.e., the angle between antenna look and peak wave direction). Traditionally, MR wave analysis is carried out in a set of rectangular windows covering the radar field of view (FOV). The FOV is typically partially obstructed, e.g., due to the coastline or ship superstructures. Especially for ships that are subject to regular course changes, this results in an increased variability or error associated with wave parameters. Using MR measurements from R/P FLIP, acquired off California during the 2010 US Office of Naval Research (ONR) high resolution air–sea interaction (Hi-Res) experiment, this study quantifies the dependency of the radar-based 2D wave spectrum and parameters on range and azimuth. With the help of reference data from a nearby Datawell Waverider buoy, we propose empirical methods to remove the dependency and we illustrate their efficacy.

High resolution inverse synthetic aperture radar (ISAR) imaging is demonstrated by using a 0.22 THz stepped-frequency (SF) imaging radar system. The synthesis bandwidth of the terahertz (THz) SF radar is 12 GHz, which are beneficial for high resolution imaging. The resolution of ISAR image can reach centimeter-scale with the use of Range-Doppler algorithm (RDA). Results indicate that high resolution ISAR imaging is realized by using 0.22THz SF radar coupled with turntable scanning, which can provide foundations for further research on high-resolution radar image in the THz band.

We consider a bistatic radar sensor network that consists of multiple separated radar transmitters and radar receivers, which are deployed to detect targets among a set of points of interest. Any transmitter-receiver pair with the same frequency forms a bistatic radar. In contrast to the disk-based sensing model in a traditional sensor network, the detection probability of a bistatic radar depends on both locations of the transmitter and receiver. Given the radar transmitters' locations and illuminating frequencies, we study the problem of joint radar receiver placement and frequency selection to maximize the target detection probability. We first study the case where there is a set of candidate locations to place the radar receivers, and propose a simple algorithm with approximation ratio at least 0.63. We then consider the case where there is no constraint for radar receivers' locations, and develop an approximation algorithm which is provably close to optimal. Finally, the numerical results are presented to show the efficacy of our algorithms.

We propose a ground moving target detection method for dual-channel Wide Area Surveillance (WAS) radar based on Compressed Sensing (CS). Firstly, the method of moving target detection of the WAS radar is studied. In order to reduce the sample data quantity of the radar, the echo data is randomly sampled in the azimuth direction, then, the matched filter is used to perform the range direction focus. We can use the compressive sensing theory to recover the signal in the Doppler domain. At last, the phase difference between the two channels is compensated to suppress the clutter. The result of the simulated data verifies the effectiveness of the proposed method.

Phased array radar has been applied broadly because of its sound performance. But signal of phased array radar is of a wide variety of types. Therefore, recognition of phased array radar is the most puzzling aspect of the whole emitter identification domain. To solve the problem, the article proposes the method that identifies phased array radar by pulse amplitude information, and studies the phased array radar, models transmit signal of them, and receiving signal by radar countermeasure reconnaissance receiver. From constructing template of pulse train’s amplitude vector of mechanical scanning radar, computing distance of samples and standard template, finding threshold of the template matching arithmetic, the article puts forward the template matching algorithm of radar beam scan type recognition to identify phased array radar automatically.

International audience; This paper investigates the optimization of the coded orthogonal frequency division multiplexing (OFDM) transmitted signal in a synthetic aperture radar (SAR) context. We propose to design OFDM signals to achieve range ambiguity mitigation. Indeed, range ambiguities are well known to be a limitation for SAR systems which operates with pulsed transmitted signal. The ambiguous reflected signal corresponding to one pulse is then detected when the radar has already transmitted the next pulse. In this paper, we demonstrate that the range ambiguity mitigation is possible by using orthogonal transmitted wave as OFDM pulses. The coded OFDM signal is optimized through genetic optimization procedures based on radar image quality parameters. Moreover, we propose to design a multiple-input multiple-output (MIMO) configuration to enhance the noise robustness of a radar system and this configuration is mainly efficient in the case of using orthogonal waves as OFDM pulses. The results we obtain show that OFDM signals outperform conventional radar chirps for range ambiguity suppression and for robustness enhancement in 2×2 MIMO configuration.

Interferometric radar is a rather popular equipment able to remotely perform dynamic testing of large structures (towers, bridges, buildings …) by detecting the relative displacement in the line of sight between the radar and geometric discontinuities in the engineering structure under test. This technique has been demonstrated effective when the radar is positioned at ground on a stable position. Unfortunately there are practical cases for which a suitable stable position is not available. This is the case of the towers of San Gimignano (Italy) that are embedded in the narrow streets of the old town. A solution to monitor the towers is to install the radar on the top of a tower, which is located in a central position of the town. The tower is not a stable arrangement for the radar and in this paper the authors propose and test the use of an accelerometer fixed to the radar head for detecting and removing its own movement.