Advanced Synthetic Aperture Radar (SAR) systems acquire a large volume of data, which necessitates the development of efficient data compression methods, beyond the current conventional techniques. Sentinel-1, as one the most popular SAR missions, provides global freely accessible data. However, the available raw data (i.e., Level-0 products) are quantized before being transferred, thus the statistics are different, hindering the validation of new algorithms mainly based on machine/deep learning paradigms. To enable elaboration of further SAR raw data compression, in this study, we propose a procedure to add random quantization noise to the decoded Sentinel-1 SAR raw data in order to obtain adapted uniformly quantized raw data that resemble the statistics of the uncompressed SAR raw data onboard the satellites. This method opens further opportunities to create large benchmarks for SAR raw data for data compression and other applications. The performance of data compression techniques (Block Adaptive Quantization (BAQ) and a complex-valued autoencoder-based data compression scheme) is evaluated on the adapted uniformly quantized raw data, and the effectiveness of the defined procedure is demonstrated.

In this work, the perspective of micro-Doppler analysis from synthetic aperture radar (SAR) images is assessed for large-scale areas. We propose a processing chain that implies a coarse vibrometry estimation based on azimuth sub-aperture decomposition and local Doppler centroid computations, followed by an in-depth analysis based on conventional SAR micro-Doppler algorithms. The methdology is assessed quantitatively using simulated data in keeping with Sentinel-1 IW imaging parameters.

Interference originating from ground-based active sources, such as radar systems, can represent a substantial obstacle to achieving high-quality and dependable Synthetic Aperture Radar (SAR) imagery. This interference can lead to partial image degradation, errors in image interpretation, and deviations in parameter extraction. Notably, the Sentinel-1, operating in C-band, is among the most widely used SAR remote sensing satellites. Given the prevalence of ground-based radar systems operating in the C-band, interference significantly impacts Sentinel-1 data, thereby necessitating effective mitigation strategies. In this article, we propose an innovative approach for mitigating interference in SAR images by employing a combination of a filter bank time-frequency distribution (dual of the classical Short-Time Fourier Transform) computed on the spectrum of the range compressed signal and a linear combination of order statistics (L-statisics). The methodology involves sorting each constant-time line of the time-frequency distribution matrix in ascending order, which places the interference at the right-hand side of the time-frequency plane. Subsequently, the interference-free range profile is determined by coherently summing along the frequency axis of the bins identified as unaffected by interference (the first q% of frequency bins from the sorted distribution). The method is evaluated on a dataset acquired by Sentinel-1A on 26.04.2021 over the city of Doha, Qatar, which is affected by interference from a radar of the Patriot Missile System.

In this work, the perspective synergy of Ground-Based Real Aperture Radar (GBRAR) measurements and micro-Doppler analysis of space-borne Synthetic Aperture Radar (SAR) images for the monitoring of vibration frequencies of large viaduct is discussed. A methodology for the processing GBRAR data and merging of ground-based and space-borne data is described. GBRAR data are interferometrically processed to provide time-range maps of Line-of-Sight (LoS) displacements. First results of GBRAR Ku-band measurements are presented. Vibration frequencies of different structural elements of the viaduct are derived. As a co-product, displacements of the bridge deck due to the crossing of vehicles are also obtained.

Sea ice plays a pivotal role in the Earth’s climate system and exhibits high sensitivity to shifts in temperature and atmospheric conditions. The precise and timely assessment of sea ice parameters is essential for comprehending and forecasting the climate changes. However, the vast volume of satellite data covering ice-covered regions is impractical to be subjectively assessed. Hence, the utilization of automated algorithms becomes mandatory to fully exploit the continuous data streams from satellites. In this paper, we propose a UNet transformer-based architecture, called UT-MHTA, to sea ice segmentation using SAR satellite imagery. Our UT-MHTA network replaces the conventional multi-head attention (MHA) block with a multi-head transposed attention (MHTA) which can capture long-range pixel interactions, while still remaining suitable for large images. Our method demonstrates superior performance compared to state-of-the-art methods, without drastically raising the computational complexity. In particular, UT-MHTA achieves a mean intersection over union (mIoU) of 68.76% on the AI4Arctic data set, with an inference time of 865ms for a 400 km2product.

The application of deep learning methods in various fields is rapidly increasing. The development of complex-valued (CV) networks that can process CV data has provided many opportunities for utilizing the immense capabilities of deep networks for CV data, including Synthetic Aperture Radar (SAR). However, the physical model and basic properties of the original SAR data must be preserved in the CV architecture. Without these properties, the physical parameters cannot be accurately retrieved from the SAR data. This study evaluates the competency of CV deep architectures to preserve the properties of the original SAR data and how it affects the retrieval of physical parameters. Ocean Surface Current (OSC) is an important parameter for ocean circulation and plays a vital role globally. In this work, the correlation Doppler estimation (CDE) method is used to estimate the OSC from SAR data before and after reconstruction with the CV autoencoder. The obtained OSCs are compared, and we demonstrate the ability of the CV deep architectures to learn the data model and preserve the original Doppler centroid (fDC) information in the SAR data. This research paves the way for the development of CV deep architectures for physical parameter retrieval and prediction from CV SAR data in future studies.

Recent advances in Synthetic Aperture Radar (SAR) sensors have enabled the acquisition of very high-resolution images with wide swaths, large bandwidth and in multiple polarization channels. As a result of the significant increase of SAR data size, an effective compression of the acquired data is of paramount importance. However, conventional data compression methods demonstrate limited effectiveness when applied to SAR data. In order to tackle this problem, in this study, a Complex-Valued (CV) end-to-end deep learning-based architecture based on convolutional autoencoders is proposed to compress Single Look Complex (SLC) SAR data. By relying on dual polarization SAR data, one of the polarization channels of the data is used as the side information to assist the reconstruction of the compressed channel with lower data loss. The obtained results demonstrate the remarkable potential and capability of CV deep learning-based methods for SAR data compression.

In this paper, an efficient routine for elliptical grid generation employed for the back-projection sped-up is proposed. The grid accommodates the particular bistatic setup formed by a space-born transmitter (Sentinel-l) and a ground-based stationary receiver (COBIS). Herein, the adapted elliptical grid is designed such that the computational complexity of the standard back-projection algorithm used for SAR image formation decreases. Specifically, the computational load is mitigated by reducing the density of the points in the cross-range direction. Such an elliptical grid leads to the formation of the SAR image in any arbitrary plane, preserving the range–azimuth (cross-range) spectrum of the final SAR image, making it suitable for further Doppler processing algorithms (e.g., common band selection in SAR interferometry).

In this work, a synthetic aperture radar imaging system is evaluated. The acquisition scenario involves a Pulson P440 UWB-radar and a localization system based on stereo-cameras. The main goal of the sensing system is to image areas behind walls. However, since the SAR acquisition is performed by a human operator and considering the positioning errors, our focus is to compensate for these errors by developing an autofocus SAR procedure. Emergency and military are the envisaged applications. The technique proposed in this paper relies on a strong point scatterer and it is validated on both simulated and real-world data.

The Inverse Synthetic Aperture Radar (ISAR) provides images of objects that are rotating with respect to the radar. An efficient image focusing algorithm is required to generate ISAR imagery from the echoes of raw data. On the other hand, the dual-polarization decomposition technique enables precise retrieval of scattering mechanisms (H-α), allowing for various applications. In this paper, we propose a novel study case of 2D ISAR imaging of partial polarimetric data of natural targets. First, a stack of 2D complex-valued raw data with VV and VH polarizations is calibrated, and then the image focusing is applied using a match-filter and spherical-wave front compensation (SWFC) method. The eigenvector descriptors based decomposition is employed, and the scattering mechanism is identified using the Lee and Pottier H-α plane. To the best of the authors’ knowledge, ISAR images are used for the first time for this study. Given that decomposition enhances target characterization for studying scattering mechanisms, the application of the Radar Vegetation Index (RVI) demonstrates how dual-polarized ISAR images can be used for vegetation identification.

Single-pass bistatic Synthetic Aperture Radar (SAR) Tomography, which involves a space-borne transmitter and a ground-based multi-channel receiver, presents an alternative to traditional TomoSAR. Typically, spectral-based reconstruction methods that either, require a large number of receiving elements or a specific configuration for a limited number of elements are considered, for achieving a desired resolution. However, these algorithms perform poorly for closed target scenarios.In this study, we propose a novel approach for estimating the elevation profile using a reduced number of channels, without the need for a specific array configuration. We treat the problem as an underdetermined system of equations and employ Compressed Sensing (CS) algorithms to solve it. The performance of the proposed approach is compared against the spectral-based approach using Monte Carlo simulations and demonstrated on real data measurements.

Sea ice is a crucial component of the Earth’s climate system and is highly sensitive to changes in temperature and atmospheric conditions. Accurate and timely measurement of sea ice parameters is important for understanding and predicting the impacts of climate change. Nevertheless, the amount of satellite data acquired over ice areas is huge, making the subjective measurements ineffective. Therefore, automated algorithms must be used in order to fully exploit the continuous data feeds coming from satellites. In this paper, we present a novel approach for sea ice segmentation based on SAR satellite imagery using hybrid convolutional transformer (ConvTr) networks. We show that our approach outperforms classical convolutional networks, while being considerably more efficient than pure transformer models. ConvTr obtained a mean intersection over union (mIoU) of 63.68% on the AI4Arctic data set, assuming an inference time of 120ms for a 400×400 km2product.

Recent studies revealed that the time-domain synthetic aperture radar (SAR) processors are more appropriate for future innovative SAR missions (e.g., ROSE-L, Harmony) not only due to their ability to form the SAR image on user-defined regions of interest (ROIs) but also for the straightforward accommodation to configurations wherein the azimuth spectrum folding occurs (e.g., TOPSAR). In this letter, we propose an accelerated Back-Projection (BP) SAR processor working in conjunction with a fast routine for generating the elliptical grid (laying on arbitrary planes) necessary for the sub-aperture based BP speed-up. The proposed workflow forms all the sub-aperture SAR images on the same coarse elliptical grid which before the coarse-to-fine grid interpolation are translated in the azimuth base-band. The fine-resolution SAR image is obtained by coherently integrating the sub-aperture images together with the concatenation of the corresponding fraction from the azimuth spectrum (unfolding) making the Single Look Complex (SLC) outcome proper for further Doppler-based processing. Our validation experiments indicate that the most suitable family of imaging planes is the one containing the receiver (fan-like grid) on bistatic scenarios with relatively large transmitter-receiver separation. The processing gain has been enhanced by one order of magnitude under low amplitude and phase distortions.

This paper presents a multi-channel ground-based bistatic SAR receiver architecture designed to perform single-pass tomography using the Sentinel-1 satellites as transmitters of opportunity. The bistatic receiver presents only 3 imaging channels, which is an extreme case for single-pass tomographic estimation. The three antennas are placed in a non-uniform configuration, such that the two antenna separations are in a 2:1 ratio. For a fixed array length, the non-uniform 3-element array will extend the maximum unambiguous height (relative to the 3-element uniform array), while keeping the elevation resolution cell around the Rayleigh limit. In the proposed processing flow, for each Sentinel-1 overpass on the envisaged orbits, the bistatic SAR image of each channel is focused on a two-dimensional grid, and afterwards the elevation profile of a given area is computed using the Capon estimator. The proposed architecture was evaluated in a measurement campaign performed between June-November 2021 using an electronic target with two transmit antennas placed on a vertical pole situated at 58.5 m from the ground receiver. For an array length of 2.6 m, the overall root mean squared error of the relative height was below 10 cm, while the unambiguous interval and the height resolution cell were around 3.75 m and 1.2 m, respectively. The experimental data from this measurements campaign provide the first quantitative assessment of spaceborne transmitter/stationary receiver single-pass bistatic SAR tomography in a controlled environment. In the long term, these results may contribute to future multi-static spaceborne SAR missions for which a single-pass tomographic capability is envisioned.

Deep learning models have achieved remarkable success in many different fields and attracted many interests. Several researchers attempted to apply deep learning models to Synthetic Aperture Radar (SAR) data processing, but it did not have the same breakthrough as the other fields, including optical remote sensing. SAR data are in complex domain by nature and processing them with Real-Valued (RV) networks neglects the phase component which conveys important and distinctive information. A Complex-Valued (CV) end-to-end deep network is developed in this study for the reconstruction and classification of CV-SAR data. Azimuth subaperture decomposition is utilized to incorporate physics-aware attributes of the CV-SAR into the deep model. Moreover, the correlation coefficient amplitude (Coherence) of the CV-SAR images depends on the SAR system characteristics and physical properties of the target. This coherency should be considered and preserved in the processing chain of the CV-SAR data. The coherency preservation of the CV deep networks for CV-SAR images, which is mostly neglected in the literature, is evaluated in this study. Furthermore, a large-scale CV-SAR annotated dataset for the evaluation of the CV deep networks is lacking. A semantically annotated CV-SAR dataset from Sentinel-1 Single Look Complex StripMap mode data (S1SLC_CVDL dataset) is developed and introduced in this study. The experimental analysis demonstrated the better performance of the developed CV deep network for CV-SAR data classification and reconstruction in comparison to the equivalent RV model and more complicated RV architectures, as well as its coherency preservation and physics-aware capability.

Spaceborne synthetic aperture radar (SAR) can provide accurate images of the ocean surface roughness day-or-night in nearly all weather conditions, being an unique asset for many geophysical applications. Considering the huge amount of data daily acquired by satellites, automated techniques for physical features extraction are needed. Even if supervised deep learning methods attain state-of-the-art results, they require a great amount of labelled data, which are difficult and excessively expensive to acquire for ocean SAR imagery. To this end, we use the subaperture decomposition (SD) algorithm to enhance the unsupervised learning retrieval on the ocean surface, empowering ocean researchers to search into large ocean databases. We empirically prove that SD improves the retrieval precision with over 20% for an unsupervised transformer auto-encoder network. Moreover, we show that SD brings an important performance boost when Doppler centroid images are used as input data, leading the way to new unsupervised physics guided retrieval algorithms.

Despite the state-of-the-art performance of the deep learning methods for Synthetic Aperture Radar (SAR) data classification, the Real-Valued (RV) networks neglect the phase component of the Complex-Valued (CV) SAR data and lose a lot of useful information. CV deep architectures have been developed in the recent years to exploit the amplitude and phase components of the CV data, in different fields. However, the superiority of CV models over RV models are proved to be different for each application, and more investigation into the advantages and disadvantages of implementing CV models for SAR data classification is necessary. In this study, the performance of the CV Convolutional Neural Network (CV-CNN) for Polarimetric SAR (PolSAR) data classification is compared with its RV equivalent network, in different contexts.

The Sentinel-1 SAR images acquired using the TOPSAR modes i.e., IW and EW on cross-polarization are significantly affected by the thermal noise on low-back-scattering areas. For example, in the arctic and some desert zones both inter- swath and inter-burst noise amplification occurs. In this paper we propose a workflow for removing the thermal noise from Sentinel-1 ground detected SAR images on low back-scattering conditions by employing the co-polarization SAR image and the Gaussian Process Regression. Our processing flow uses the noise vectors provided in the European Space Agency (ESA) ground detected product and scales them such that a slightly over-denoised image is produced. Then, the Gaussian Process Regression is used to map the co-polarization SAR image into the cross-polarization SAR image. Prior to this step, a radiometric correction is applied on the co-polarization data, since its pixel values are heavily dependent on the incidence angle. Finally, the denoised cross-polarization image is obtained as a linear combination between the over-denoised version and the predicted image. Since, the co-polarization channel is employed for the prediction of the missing values in the cross-polarization channel there is no need for co-registration and the de noising procedure is trustworthy.

The spectral characteristics of single-look complex - inter-ferometric wide (SLC-IW) swath, terrain observation by progressive scan (TOPS), are significantly different from those of strip-map (SM). Due to the burst mode and series of sub-swaths, the target area is scanned for a short period of time. Therefore, swath width comes at the expense of azimuth resolution. To eliminate quadratic phase drift and achieve SLC baseband, significant processing is required. De-ramping is a necessary step to compute ocean circulation parameters. In this work, we extract ocean parameters from the complex echo signal based on data driven Doppler centroid$(f_{DC})$regardless of the OCN product information and geophysical$f_{DC}$image. The radial surface velocity (RSV) is retrieved from Doppler history, and the significant wave height (SWH) is estimated with an empirical relationship of RSV. The results of ocean circulation parameters are promising when compared with benchmark and in-situ data. This work demonstrates the efficacy and necessity of de-ramping the TOPS data for subsequent use in a variety of ocean remote sensing applications.

Spaceborne synthetic aperture radar (SAR) can provide meters-scale images of the ocean surface roughness day-or-night in nearly all weather conditions. This makes it a unique asset for many geophysical applications. Sentinel-l SAR wave mode (WV) vignettes have made possible to capture many important oceanic and atmospheric phenomena since 2014. However, considering the amount of data provided, expanding applications requires a strategy to automatically process and extract geophysical parameters. In this study, we propose to apply subaperture decomposition (SD) as a preprocessing stage for SAR deep learning models. Our data-centring approach surpassed the baseline by 0.7%, obtaining state-of-the-art on the TenGeoP-SARwv data set. In addition, we empirically showed that SD could bring additional information over the original vignette, by rising the number of clusters for an unsupervised segmentation method. Overall, we encourage the development of data-centring approaches, showing that, data preprocessing could bring significant performance improvements over existing deep learning models.

Coastline extraction by exploiting optical images is challenging during adverse weather conditions. This letter proposes coastline extraction from synthetic aperture radar (SAR) data. Since collectingin-situdata is expensive and not always possible, the Doppler parameter is used to delineate coastlines when neitherin-situdata nor cloud-free optical images are available. We propose a novel coastline extraction method based on classic coastal dynamic variation, such as Doppler centroid (fDC), since coastline is static and has zero Doppler with respect to the dynamic sea-state. The results of the Doppler-based novel technique allow us to investigate the impact of natural hazards on coastline degradation. We compare the proposed method to state-of-the-art (SOA) coastline extraction methods based on polarimetric correlations and the reference method from Sentinel-2. The results show that using scattering from dual and cross-polarization for coastline extraction is more reliable than using co-polarization. Based on empirical distributions and using the constant false alarm rate (CFAR) method, the relevant threshold has been adapted to distinguish land and sea in an unsupervised manner. We compare the results of polarimetric and Sentinel-2 with Doppler-based coastline extraction, which emphasizes the accuracy of the proposedfDCmethod for extracting coastlines at full resolution.

Exploring polarimetric diversity of Synthetic Aperture Radar (SAR) data is directly applicable to conventional monostatic cases. For this, the mostly used convention is the Backscatter Alignment. While establishing important advantages for the monostatic case (possibility to have equal values on the cross-polarimetric channels), it has been proven to introduce some difficulties for the bistatic case. This appears in relation to the so-called conjugate similarity operation, when (mathematically) asymmetric scattering matrices occur. In this paper, we propose the detailed algorithm which provides a solution to the conjugate similarity operation, in the case of general scattering matrices. The proposed algorithm is based on the real representation matrix transformation. Further, we investigate the characterization of canonical bistatic scatterers (three elementary targets). Raw bistatic polarimetric signals are obtained by using simulations with a computationally electromagnetic (EM) software, capable of complete EM analysis. The eigenvalue classification illustrates the potential of additional information brought using the proposed Real Representation Scattering Matrix (RRSM). The presence of complex eigenvalues is investigated in relation to the bistatic angle and one nonreciprocity parameter.

This article introduces a compressive sensing (CS)-based approach for increasing bistatic synthetic aperture radar (SAR) imaging quality in the context of a multiaperture acquisition. The analyzed data were recorded over an opportunistic bistatic setup including a stationary ground-based-receiver opportunistic C-band bistatic SAR differential interferometry (COBIS) and Sentinel-1 C-band transmitter. Since the terrain observation by progressive scans (TOPS) mode is operated, the receiver can record synchronization pulses and echoed signals from the scene during many apertures. Hence, it is possible to improve the azimuth resolution by exploiting the multiaperture data. The recorded data are not contiguous and a naive integration of the chopped azimuth phase history would generate undesired grating lobes. The proposed processing scheme exploits the natural sparsity characterizing the illuminated scene. For azimuth profiles recovery greedy, convex, and nonconvex CS solvers are analyzed. The sparsifying basis/dictionary is constructed using the synthetically generated azimuth chirp derived considering Sentinel-1 orbital parameters and COBIS position. The chirped-based CS performance is further put in contrast with a Fourier-based CS method and an autoregressive model for signal reconstruction in terms of scene extent limitations and phase restoration efficiency. Furthermore, the analysis of different receiver-looking scenarios conducted to the insertion in the processing chain of a direct and an inverse Keystone transform for range cell migration (RCM) correction to cope with squinted geometries. We provide an extensive set of simulated and real-world results that prove the proposed workflow is efficient both in improving the azimuth resolution and in mitigating the sidelobes.

Autonomous driving is disrupting the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations on its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key exploitable results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI-controlled vehicle demonstrators) achieved until its final year 3.

Equivalent matrix representations in radar polarimetry have long been studied and used as tools for modeling and understanding the scattering mechanisms. We include here the Kennaugh, Graves, or covariance matrices which are today seen as alternative representations of the same physical quantity, the scattering matrix. In this paper, we briefly explore some of the properties of the algebraic real representation of a complex matrix, a mathematical construction which has been introduced in the literature as an alternative way of performing consimilarity transformations (rather than by the usual Graves power decomposition, with applications limited only to those involving symmetric scattering matrices). Besides the theoretical presentation on the subject, the main goals of the paper are to study some of the advantages and limitations of using the 4 × 4 real matrix form and to compare consimilarity transformation results obtained through the real representation to those given by the power representation.

Bistatic radar receivers that use an opportunistic transmitter require a reference channel to capture the original transmitted signal, which is then used as a reference signal for constructing the matched-filter during the range compression step. Because the reference signal is received from line-of-sight, it is orders in magnitude larger than the reflections captured by the receive channel. It is generally difficult to construct the system such that the reference signal is not leaked into the received signal, either via coupling in the circuitry or via reflections off objects in the vicinity of the receiver. Due to its much larger amplitude, the reference signal can easily mask smaller targets with its side-lobes. In this paper we propose a novel deconvolution method for bistatic SAR images as a means of eliminating leakage of the reference signal.

Autonomous driving has the potential to disruptively change the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations by its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI controlled vehicle demonstrators) achieved until year 2.

This paper presents a time-domain synthetic aperture radar (SAR) processor designed for Sentinel-l Terrain Observation by Progressive Scans (TOPS) monostatic data. The processor focuses Interferometric Wide (IW) swath and Extra Wide (EW) swath data on a selected region-of-interest (ROI) and consists of the following main stages: decoding of Level-0 data, selection of the relevant burst and pulses for the targeted ROI, range compression, azimuth frequency unfolding and resampling, and image focusing by a fast subaperture-based version of the back-projection algorithm. The performances of the developed processor are assessed on datasets acquired in IW and EW imaging modes. Such a time-domain processor can be regarded as a first step towards a geometry/frequency-agnostic SAR processing kernel for future monostatic/multi static spaceborne SAR missions.

Synthetic Aperture Radar Tomography (TomoSAR) is one primary remote sensing technique for deriving elevation estimations and recovering the 3D spatial information of an observed scene. This article presents a novel method for improved single-pass bistatic SAR tomography estimation, in the case of a spaceborne transmitter-stationary receiver architecture, with only three imaging channels at the receiver. The approach is based on the use of the coarray space to create a virtual set of acquisitions, as received from an array with a larger number of antenna elements. The proposed configuration is evaluated through simulations.

From simple scattering mechanism extraction and throughout more complex applications (e.g., land classification, disaster monitoring), polarimetry has become a key element for remote sensing. For the particular case of bistatic polarimetry, the development of a theoretical basis has not been yet aligned with a comprehensive experimental validation. At the moment, an exhaustive search across the polarimetric scientific literature will reveal that for true bistatic geometries (i.e., significant angular separation between transmitter and receiver), only a small number of qualitative investigations have been made and there is still work to be done. In the current paper, one of the most popular polarimetric decomposition methods (H - α) is applied to dual-pol VV-VH data, in both bistatic (space-surface geometry with ground-based receiver) and monostatic configurations. Images from both geometries are displaying a common, urban scene. Comparing the obtained results, objective observations are presented.

In this paper is presented a new methodology for synthetic aperture radar images focusing called bidimensional mixed compressive sensing back-projection (CS-BP-2D). Spatial compressibility of the radar images is exploited by constructing the sparsity basis using the backprojection focusing framework and solving the reconstruction problem by means of the orthogonal matching pursuit algorithm (OMP).

The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. Cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of lighting conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. We propose two FCNs that take spectrograms of the beat signals as input, and provide the corresponding clean range profiles as output. We propose two architectures for interference mitigation which outperform the classical zeroing technique. Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to objectively compare their future work in this domain. The data set is available for download at: http://github.com/ristea/arim.

Among the actual trends that will affect society in the coming years, autonomous driving stands out as having the potential to disruptively change the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations by its own, which currently is not reached with state-of-the-art approaches also due to missing reliable environment perception and sensor fusion. PRYSTINE will realize Fail-operational Urban Surround perceptION (FUSION) which is based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. In this paper, we detail the vision of the PRYSTINE project and we showcase the results achieved during the first year.

The paper proposes a methodology to perform azimuth focusing of spaceborne transmitter-stationary receiver bistatic synthetic aperture radar (SAR) data across multiple along-track apertures to increase azimuth resolution. The procedure uses as input several azimuth apertures (continuous groups of range compressed pulses) from one or more satellite bursts and comprises the following stages: azimuth antenna pattern compensation, slow time resampling, reconstruction of missing azimuth samples between neighbouring sets of pulses using an auto-regressive model and back-projection focusing of the resulting multi-aperture range image. The approach is evaluated with real bistatic data acquired over an area of Bucharest city, Romania.

This paper addresses the feasibility of exploiting a radargrammetric procedure for the retrieval of height estimates using stereo images acquired in a space-surface (spaceborne transmitter-stationary receiver) bistatic geometry. Currently, the research interest concerning this particular direction is still in its infancy, as there are very few papers partially covering the subject. The method proposed in this study is applied to a set of SAR images (displaying an urban area of the Bucharest city) in order to assess the elevation of a group of selected targets within the remotely sensed zone.

This paper presents the first results obtained by repeat-pass bistatic synthetic aperture radar (SAR) interferometry using a fixed C-band ground-based receiver and the Sentinel-1A/B satellites as transmitters of opportunity. The methodology developed to obtain repeat-pass bistatic SAR interferograms uses as input a stack of range compressed bistatic acquisition data and mainly consists in the following stages: raw inter-ferograms computation on a two-dimensional grid in ground geometry, atmospheric phase screen removal and topographic phase compensation. The displacements of a high-rise building were estimated using two stacks of bistatic SAR images acquired between April-June 2017 over an area of Bucharest city, Romania.

With a continuous increase in multi-temporal synthetic aperture radar (SAR) images, leading to enable mapping applications for Earth environmental observation, the number of algorithms for detection of different types of terrain changes has greatly expanded. In this paper, a SAR image change detection method based on normalized compression distance (NCD) is proposed. The procedure mainly consists in dividing two time series images in patches, computing a collection of similarities corresponding to each pair of patches and generating the change map with a histogram-based threshold. The experimental results were computed using 2 Sentinel 1A images over the city of Bucharest, Romania and 2 TerraSAR-X images over the Elbe River and its surrounding area, Germany.

This paper makes an analysis of repeat-pass bistatic synthetic aperture radar (SAR) interferometry performed with a stationary ground-based receiver and a satellite as transmitter of opportunity. A numerical approach is developed in order to asses the sensitivity of the repeat-pass across-track bistatic interferometric phase to height (relative to the digital elevation model used for focusing) and displacements (in the bistatic lines of sight, between consecutive acquisitions). Compared to the monostatic case, the conversion from height/displacement to phase is not straightforward and is dependent on the considered geometry. The method is applied for a bistatic SAR interferogram generated over an area of Bucharest city, Romania, using a ground receiver with one imaging channel and Sentinel-1A/B as transmitter of opportunity.

Ground-based fixed receiver bistatic synthetic aperture radar (SAR) is a technology increasingly used in urban monitoring, complementing and enriching the traditional monostatic SAR, but the acquisition geometry is more complex than in the monostatic case. Hence, in the design and configuration of real bistatic SAR systems simulations are needed. In this regard we have presented in [1] a simulator for this geometry that could be used as a way to explore the possibilities given by this configuration. One of the many SAR applications that could be transposed to the bistatic case is displacement measurement. It can be used in urban and non-urban environments, as a way of monitoring the change in the position of some objects of interest, like dams and buildings, using the interferometric phase obtained from two or more SAR acquisitions. This paper aims to investigate by simulation displacement measurements in ground-based fixed receiver bistatic geometry.

This paper proposes a detector of multi-component non-stationary signals based on the matched signal transform (MST). In the MST domain, a non-stationary signal is localized at its frequency modulation rate with the transform's basis modulation function. The MST can be numerically implemented either as a freestanding discrete version of an integral transform, or for faster computation, as a time resampled version of the original signal followed by a fast Fourier transform. We analyze the noise statistics in the MST domain and derive the analytical forms of the probability density function for both implementations, considering the non-stationary signal embedded in white Gaussian noise. We propose a detector based on the squared magnitude of the MST and show how its detection performances depend on the chosen implementation. All the theoretical derivations are validated through Monte Carlo simulations.

This paper presents the first SAR imaging results obtained with a fixed ground-based system used in bistatic configuration having the TerraSAR-X satellite as transmitter of opportunity. The system's characteristics and signal processing flow are presented relative to the state of the art. Compared to previous works on bistatic SAR imaging where a significant amount of processing is dedicated to time/frequency synchronization between the satellite transmitter and ground receiver, we show that a bistatic SAR image with only meter-range geographic offset can be obtained using the state vectors from a monostatic image and minimal synchronization efforts consisting in acquisitions triggered by an amplitude threshold and stabilization of the local oscillator with a GPS-disciplined reference. We present the first bistatic image in ground geometry obtained over an area of Bucharest and compare it with a monostatic image refocused on the same grid.

In this paper we present the early development of a SAR simulator for ground-based fixed-receiver bistatic geometry. Firstly, we describe the assumptions the simulator is based on, then a short presentation of the mathematical model that lays behind the simulator is given. Furthermore, we give some details about its implementation and finally, we present some results.

This letter proposes a micro-Doppler (m-D) reconstruction method for spaceborne synthetic aperture radar (SAR) images using azimuth time-frequency tracking of the phase history. The algorithm involves an azimuth defocusing of the SAR image in order to gain access to the phase history, followed by a time-frequency tracking algorithm. The tracking in azimuth is based on local polynomial phase modeling using as estimator for the polynomial coefficients the high-order ambiguity function. The approach is presented in the context of vibration estimation for infrastructure monitoring applications, with an emphasis on the estimation of vibration parameters from the reconstructed m-D. The procedure is tested and compared with state-of-the-art methods by various simulation scenarios in keeping with typical high-resolution SAR imaging parameters. Finally, the developed algorithm is applied on real data acquired by the TerraSAR-X satellite over the Puylaurent water dam in France.

The multiplicative model, expressed as a product between the square root of a scalar positive quantity (texture) and the description of an equivalent homogeneous surface (speckle), is one of the most appropriate and disseminated models used to describe high-resolution polarimetric synthetic aperture radar (PolSAR) clutter. Generally, the texture is assumed polarization independent, which causes PolSAR data to present a spherical symmetry property, allowing for the usage of most of the algorithms present in the literature. Nevertheless, the existence of polarization-dependent clutter has also been reported, for which specific algorithms need to be derived. Therefore, it becomes clear that the first step in SAR data analysis should be the validation of the model employed. Within this context, this paper presents a new methodological framework to assess the conformity of multivariate high-resolution SAR data with respect to the product model in terms of asymptotic statistics. More precisely, spherical symmetry is investigated by applying statistical hypothesis testing on the structure of the quadricovariance matrix. Simulated data, data from the P-band airborne data set acquired by the Office National d'Études et de Recherches Aérospatiales (ONERA) over the French Guiana in 2009 in the frame of the European Space Agency campaign TropiSAR and a RAMSES X-band image acquired over Brétigny, France, are taken into consideration to investigate the performance of the derived test. The detection results are qualitatively and quantitatively analyzed, and some important conclusions are drawn regarding the methodology employed in analyzing SAR data.

This paper proposes a parameters estimation algorithm for signals composed of multiple non-stationary components having the same basis modulation function which is described by an a priori known model and depends on a few unknown parameters. The procedure is based on time warping the signal in turn with every basis function resulted from different model parameters combinations and evaluating the concentration of the warped signal spectrum. The estimated parameters of the model are the ones which provide the best spectral concentration. Onwards, the amplitude, phase and modulation rate for each component are determined from the signal warped with the optimal basis function. The algorithm is tested with simulations and real data consisting of de-chirped radar signals and acoustic signals with harmonic components from underwater mammals.

This paper proposes a vibration-induced micro-Doppler estimation method for oscillating targets in synthetic aperture radar (SAR) images using azimuth time-frequency tracking and a matched signal transform. The approach involves an azimuth defocusing of the SAR image in order to access the phase history. The tracking in azimuth is based on local polynomial phase modeling using as estimator for the polynomial coefficients the high-order ambiguity function. The vibration frequency is obtained from the spectrum of the tracked instantaneous frequency law, while the oscillation amplitude is estimated using a matched signal transform. The procedure is tested by simulations and on real SAR images acquired by the TerraSAR-X satellite over the Puylaurent water-dam in France.

The multiplicative model, expressed as a product between the square root of a scalar positive quantity (texture) and the description of an equivalent homogeneous surface (speckle), is one of the most disseminated models used to describe high-resolution Polarimetric Synthetic Aperture Radar clutter. Recently, a statistical test was proposed to verify the validity of the model. Within this context, this paper analysis, qualitatively and quantitatively, a P-band airborne dataset acquired by the Office National d'Études et de Recherches Aérospatiales (ONERA) over the French Guiana in 2009 in the frame of the European Space Agency campaign TropiSAR, carefully investigating the regions were the aforementioned does not hold.

Infrastructure monitoring applications can require the tracking of slowly moving points of a certain structure. Given a certain point from a structure to be monitored, in the context of available spaceborne synthetic aperture radar (SAR) products, where the image is already focused in a slant range-azimuth grid, it is not obvious if this point is the scattering center, if it is in layover or if it is visible from the respective orbit. This paper proposes a scattering centers detection and tracking procedure based on refocusing a set of SAR images on a provided high-resolution grid of the structure. The refocusing procedure is designed for high-resolution spotlight and sliding spotlight SAR images and consists of an azimuth defocusing followed by a modified back-projection algorithm on the given set of points. The scattering centers of the refocused image are detected in the 4-D tomography framework by testing if the main response is at zero elevation in the local elevation-velocity spectral distribution obtained using the Capon estimator. The mean displacement velocity is estimated from the peak response on the zero elevation axis, whereas the displacements time series for detected single scatterers is obtained as phase difference of complex amplitudes. The algorithm is tested by simulations with an emphasis on its behavior for a low number of satellite passes and applied on real data acquired with the TerraSAR-X satellite over the Puylaurent dam. The relative displacements between scattering regions show very good agreement with in situ measurements.

Infrastructure monitoring applications can require the tracking of slowly moving points of a certain structure. Given a certain point from a structure to be monitored, in the context of available SAR products where the image is already focused in a slant range - azimuth grid, it is not obvious if this point is the scattering center, if it is in layover or if it is visible from the respective orbit. This paper proposes a scattering center monitoring procedure based on refocusing a set of SAR images on a provided high-resolution DEM of the structure. The scattering centers of the refocused image are detected in the 4-D tomography framework by testing if the main response is at zero elevation in the local elevation-velocity spectral distribution obtained using the Capon estimator. The algorithm is validated on real data acquired with the TerraSAR-X satellite over the Puylaurent water dam in France during March-June 2012. The relative displacements between scattering regions show very good agreement with the in situ measurements.

The frequency-modulated continuous-wave (FMCW) radar is an alternative to the pulse radar when the distance to the target is short. Typical FMCW radar implementations have a homodyne architecture transceiver which limits the performances for short-range applications: The beat frequency can be relatively small and placed in the frequency range affected by the specific homodyne issues (dc offset, self-mixing, and 1/f noise). In addition, one classical problem of an FMCW radar is that the voltage-controlled oscillator adds a certain degree of nonlinearity which can cause a dramatic resolution degradation for wideband sweeps. This paper proposes a short-range X-band FMCW radar platform which solves these two problems by using a heterodyne transceiver and a wideband nonlinearity correction algorithm based on high-order ambiguity functions and time resampling. The platform's displacement measurement capability was tested on range profiles and synthetic aperture radar images acquired for various targets. The displacements were computed from the interferometric phase, and the measurement errors were situated below 0.1 mm for metal bar targets placed at a few meters from the radar.

A frequency modulated continuous wave (FMCW) X-band radar platform for millimetric displacements measurement of short-range targets is presented in this paper. The platform's transceiver is based on a heterodyne architecture because the beat frequency is relatively small for short-range targets and it can be placed in the frequency range influenced by the specific homodyne architecture problems: DC offset, self-mixing and 1/f noise. The platform's displacement measurement capability was tested on range profiles and SAR images acquired for various targets. The displacements were computed from the interferometric phase. The measurement errors were situated below 0.1 mm for metal bar targets placed at a few meters from the radar.

Polarimetry and multi-pass interferometry extend the dimensionality of SAR images, therefore the necessity to have multivariate statistic (and non-Gaussian) distributions as models for these types of data: such are the SIRV (Spherically Invariant Random Vectors). However, as the stochastic model becomes more complex, correctly estimating its parameters gets difficult. More, although they are versatile, the SIRV models are not guaranteed to match the PolSAR / InSAR data. To evaluate the pertinence of those models with respect to the PolSAR and multi-pass InSAR data, through one of their most important statistic properties, namely sphericity, it is the purpose of this paper. The proposed analysis is illustrated with spaceborne multi-pass InSAR TerraSAR-X data.