Smart antennas for space-borne synthetic aperture radars

This paper proposes the precise spaceborne synthetic aperture radar (SAR) image formation technique based on the analysis of critical error factors that severely degrade the SAR image quality. These are error factors related to the antenna beam pointing, the effective velocity, and the Doppler centroid. We newly developed the spaceborne SAR system simulator which is able to analysis effects that critical errors of the user-designed spaceborne SAR induce on a focused image. Using it, effects of critical errors are analyzed for spaceborne SAR image formation. Analysis results show that these cause phase distortion of rawdata, distort the symmetry of the azimuth impulse response function (IRF) of point targets in the focused image, and defocus the SAR image. To resolve these problems, we suggest to make use of the phase gradient algorithm (PGA) to compensate phase distortion induced by antenna beam pointing errors. Also, the effective velocity of the illuminated beam and the Doppler centroid of spaceborne SAR rawdata are exactly calculated by proposed methods using both orbit state vectors and the rawdata acquisition geometry based on the newly defined twoway slant range equation model. Furthermore, azimuth block processing is used to reduce signal level of ambiguities produced by sidelobes of antenna beam. The experimental results on the simulated SAR data show that proposed methods are able to reduce error effects as well as improve the focused SAR image quality greatly.

Spaceborne synthetic aperture radar (SAR) and ground-based radar interferometers (GBRIs) can be used to detect spatially detailed crustal deformations that are difficult to detect by on-site observations, the Global Navigation Satellite System, tiltmeters, and so on. To make such crustal deformation information readily available to those engaged in evaluating volcanic activities and researching the mechanisms, we are preparing a database within the Japan Volcanological Data Network data sharing system to store crustal deformation detected by spaceborne SAR and GBRIs (Subtheme 2-1, Project B, the Integrated Program for Next Generation Volcano Research and Human Resource Development). In this study, we examined methods to reduce atmospheric delay noise in SAR interferometry using the numerical weather model and determined the methods for resampling the analytical values of the numerical weather model and estimating atmospheric delay to efficiently determine atmospheric delay. We show that the atmospheric delay can be estimated with higher accuracy by properly combining the isobaric surface and ground surface data of the mesoscale model (MSM) provided by the Japan Meteorological Agency. We are developing a multi-type portable SAR system as a GBRI system such that it would allow campaign observations whenever increased volcanic activities are observed and acquire crustal deformation with a higher temporal resolution than spaceborne SAR for storage in the database. This system employs L-band radar, which has a higher penetrability against vegetation. Two modes of observations are possible: ground-based SAR and car-borne SAR. The prototype was fabricated to conduct experiments necessary to develop a working model. The experimental observations was carried out around Asama volcano, and we confirmed that clear fringe was obtained.

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.

Spaceborne synthetic aperture radar (SAR) is a promising remote sensing technique, as it can produce high-resolution imagery over a wide area of surveillance with all-weather and all-day capabilities. However, the spaceborne SAR sensor may suffer from severe radio frequency interference (RFI) from some similar frequency band signals, resulting in image quality degradation, blind spot, and target loss. To remove these RFI features presented on spaceborne SAR images, we propose a multi-dimensional calibration and suppression network (MCSNet) to exploit the features learning of spaceborne SAR images and RFI. In the scheme, a joint model consisting of the spaceborne SAR image and RFI is established based on the relationship between SAR echo and the scattering matrix. Then, to suppress the RFI presented in images, the main structure of MCSNet is constructed by a multi-dimensional and multi-channel strategy, wherein the feature calibration module (FCM) is designed for global depth feature extraction. In addition, MCSNet performs planned mapping on the feature maps repeatedly under the supervision of the SAR interference image, compensating for the discrepancies caused during the RFI suppression. Finally, a detailed restoration module based on the residual network is conceived to maintain the scattering characteristics of the underlying scene in interfered SAR images. The simulation data and Sentinel-1 data experiments, including different landscapes and different forms of RFI, validate the effectiveness of the proposed method. Both the results demonstrate that MCSNet outperforms the state-of-the-art methods and can greatly suppress the RFI in spaceborne SAR.

To achieve high-resolution and wide-swath (HRWS) imaging, a space-time waveform-encoding (STWE) spaceborne synthetic aperture radar (SAR) system is adopted. In rugged terrain, the beam-pointing mismatch problem will appear when the traditional digital beamforming (DBF) technique is used to separate the received echoes. This problem leads to decreasing the received echo’s gain, deteriorating the quality of the image product and making the interpretation of SAR image difficult. To address this problem, an advanced echo separation scheme for STWE spaceborne SAR based on the DBF and blind source separation (BSS) is put forward in this paper. In the scheme, the echoes are transmitted within the adjacent pulse repetition intervals to simulate multiple beams, and the scattered echoes are received by the sixteen-channel antennas in elevation simultaneously. In post-processing, a detailed flow is adopted. In the method, the DBF is firstly performed on received echoes. Due to the error caused by terrain undulation, the degree of echo separation is not enough. Then, the BSS is performed on the multiple echoes obtained after the DBF processing. Finally, the highly separated echo signal can be obtained. In this scheme, there is no need to perform the direction of arrival (DOA) estimation before the DBF processing, which saves valuable computing resources. In addition, to verify the proposed scheme, point target and distributed target simulations based on the 16-channel data of an elevation X-band DBF-SAR system are carried out. The results of point targets indicate that the residual echo caused by rough terrain can be reduced by more than 14 dB using the proposed scheme. The proposed scheme can be directly implemented into existing SAR systems; thus, it does not increase the complexity of the system design. The scheme has the potential to be applied to future spaceborne SAR missions.

Radio spectrum bands allocated to spaceborne synthetic aperture radar (SAR) imagery are shared by multiple missions. In practical radio spectrum environments, these bands are also used by some ground radars, e.g., C-band weather radar. Due to this fact, radio frequency interference (RFI) may occur for a spaceborne SAR when its received signals contain the transmitted waveforms from another SAR or radar operating at the same frequency band. This particular class of RFI is usually linear-frequency-modulation (LFM) signals, which can cause bright radiometric artifacts in focused SAR images. Most existing signal processing approaches designed for addressing this problem belong to the class of preprocessing methods, which removes RFI in level-0 raw radar data before SAR focusing. In this article, we propose a postprocessing kernel—2-D SPECtral ANalysis (2-D SPECAN) filter, for removing the class of LFM RFI in level-1 SLC images. The filtering consists of three main steps: Step 1: focus LFM RFI artifacts in SLC images as point-like responses in the spectral domain via 2-D SPECAN; Step 2: perform 2-D notch filtering in the spectral domain to remove the most contribution of the RFI responses; and Step 3: transform the filtered spectrum back into the SLC image domain using the inverse operation of the 2-D SPECAN. For computation efficiency, we design a simplified processing flow and adopt a blockwise processing strategy. Experiments with several Sentinel-1 SLC images demonstrate that severe RFI artifacts in SLC images can be removed significantly by the proposed method.

This paper first summarizes the state of the art in spaceborne SAR systems and applications. The second part of this paper gives an overview of new concepts, techniques and technologies for future SAR systems, allowing an increase of flexibility the SAR operation mode as well as a reduction in the overall system costs. Several innovative concepts and technologies as bi and multistatic configurations, parasitic SAR, sparse aperture systems and digital beamforming will play an important role for future spaceborne SAR constellations.

Precise range history model and accurate focusing algorithms are challenges for high-resolution spaceborne Synthetic Aperture Radar (SAR) due to the curved orbits of satellites and spatial variations of signal models. The existing range models include HRM, AHRM and DRM. This paper focuses on the range history model for spaceborne SAR. In the paper, an accurate range model based on fourth-order Doppler parameters is proposed to accurately formulate the range history between SAR and targets. Compared with other models, such as DRM4 which is also based on fourth-order Doppler parameters, the proposed range model has much improved accuracy. All the simulation results validate the high precision of the proposed range model.

Spaceborne synthetic aperture radar (SAR) is an advanced microwave imaging technology that provides all-weather and all-day target information. However, as spaceborne SAR resolution improves, traditional echo signal models based on airborne SAR design become inadequate due to the curved orbit, Earth rotation, and increased propagation distance. In this study, we propose an accurate range model for high-resolution spaceborne SAR by analyzing motion trajectory and Doppler parameters from the perspective of the space geometry of spaceborne SAR. We evaluate the accuracy of existing range models and propose an advanced equivalent squint range model (AESRM) that accurately fits the actual range history and compensates for high-order term errors by introducing third-order and fourth-order error terms while maintaining the simplicity of the traditional model. The proposed AESRM's concise two-dimensional frequency spectrum form facilitates the design of imaging algorithms. Point target simulations confirm the effectiveness of the proposed AESRM, demonstrating significant improvements in fitting accuracy for range histories characterized by nonlinear trajectories. The developed AESRM provides a robust foundation for designing imaging algorithms and enables higher resolution and more accurate radar imaging.

The scientific developments carried out in the last years with spaceborne synthetic aperture radar (SAR), as well as the comparisons with in-situ sensors, have demonstrated that SAR is a reliable remote sensing tool to study wave fields on the open ocean. In their so-called SAR image mode, SAR systems on board satellites are able to scan ocean areas about 100 × 100 km2 with a spatial resolution about 20 × 20 m2. These SAR images are able to provide information about the spatial variability of wave fields, as well as other phenomena that occur on the ocean surface, such as the local wind field. This work investigates the capabilities of spaceborne SAR to extract sea state information for those areas close to coastal locations, where the incoming wave fields present high spatial inhomogeneities. For this purpose, ERS-2 SAR and ENVISAT Advanced SAR (ASAR) images acquired over the North Sea and the Bay of Biscay are used to study ocean waves in shallow waters under different climate and oceanographic conditions. In addition, the potential of ENVISAT ASAR capability to scan the ocean surface with dual polarizations (horizontal, HH, and vertical, VV) is analyzed in this work. The relevance of these investigations for different practical applications concerning the analysis of ocean waves is discussed.

The stripmap mode is a basic and important mode for spaceborne synthetic aperture radars (SARs). Estimating the time-varying area observed by spaceborne SARs operating in stripmap mode is a practical but challenging field of research. In this article, we propose a novel method that parameterizes the time-varying area observed by the spaceborne SAR operating in the boresight stripmap mode into a fixed antenna attitude. Based on the principle of minimizing the dihedral angle between the plane containing the ideal estimated scatterers and the plane containing the actual parabolic antenna edge of a spaceborne SAR, an objective function is established for estimating the area observed by a spaceborne SAR operating in the boresight stripmap mode. Then, simulation experiments are designed to validate the feasibility and the robustness of the proposed method. The experimental simulation results show that the proposed method is feasible, and even under low signal-to-noise ratio (SNR) conditions of 10 dB, the proposed method still has good robustness.

When processing high-resolution spaceborne synthetic aperture radar (SAR) data, the orbit curvature is a key aspect that must be taken into account. The non-hyperbolic range history makes most SAR imaging approaches not suitable for the curved orbit. Based on the two-dimensional spectrum derived by series reversion (SR), a modified Omega-K algorithm (OKA) is proposed in this paper. Making use of the reference function calculated by SR, an accurate bulk compression is implemented. Following, a modified Stolt interpolation is applied based on least square (LS), to perform the residual range-variant processing efficiently. The method described can achieve satisfactory focusing results for spaceborne SAR, without a large number of computation. Point targets simulations have validated the presented research.

The spaceborne Synthetic Aperture Radar (SAR) is a broadly acknowledged technique to monitor the vast open ocean. However, a frequent confusion between the SAR image azimuth angle <inline-formula>$ {\phi }_{\mathrm{I}\mathrm{M}\mathrm{G}} $</inline-formula> and the satellite flight direction <inline-formula>$ {\phi }_{\mathrm{S}\mathrm{A}\mathrm{T}} $</inline-formula> exists in the data processing and geophysical applications, which influences for instance the accuracy of SAR wind inversions. An overview of <inline-formula>$ {\phi }_{\mathrm{I}\mathrm{M}\mathrm{G}} $</inline-formula> derivation from the transformation of the spatial satellite coordinate systems is presented in this paper, along with a method for calculating <inline-formula>$ {\phi }_{\mathrm{I}\mathrm{M}\mathrm{G}} $</inline-formula> based on the Ground Control Points (GCP). By analyzing a set of 0.7 million Sentinel-1 SAR wave mode images acquired in 2016, we quantify the systematic bias between <inline-formula>$ {\phi }_{\mathrm{I}\mathrm{M}\mathrm{G}} $</inline-formula> and <inline-formula>$ {\phi }_{\mathrm{S}\mathrm{A}\mathrm{T}} $</inline-formula> and find out that this bias is related to the incidence angle and orbital direction. It can be accurately fitted using a fifth-order polynomial relative to latitude. The misuse of <inline-formula>$ {\phi }_{\mathrm{S}\mathrm{A}\mathrm{T}} $</inline-formula> is demonstrated to approximate <inline-formula>$ {\phi }_{\mathrm{I}\mathrm{M}\mathrm{G}} $</inline-formula> can lead to nonnegligible wind retrieval errors. The spatial distribution of this wind error is non-uniform and differs between ascending and descending orbits, with a wind speed bias of ±0.5 m·s^–1. Results obtained in this study evidence that the precise estimate of <inline-formula>$ {\phi }_{\mathrm{I}\mathrm{M}\mathrm{G}} $</inline-formula> are significant to various SAR applications in relevant studies. The proposed method here also holds practical reference for other satellite SAR missions.

The next generation of spaceborne synthetic aperture radar (SAR) remote sensing systems will emphasize on high-resolution and wide-coverage imaging. For these design goals, variable pulse repetition interval (PRI), which is also called staggered SAR, is a promising candidate to deal with the inherent problem of blind range effect in conventional SAR with constant PRI. This letter presents an improved work of signal processing to cope with two unavoidable problems in staggered mode: the loss of echo signal and nonuniform sampling in azimuth. Since the existing signal processing methods are mostly based on a high oversampling ratio, which would increase the range ambiguity to signal ratio and the amount of data to be downlinked, this letter focuses on signal processing with low oversampling factors. A two-step processing scheme - recovery and reconstruction - is proposed to recover the missing sampled data using the spectral-estimation techniques first and then reconstruct the in-band energy and minimize the ambiguity energy, which is from band unlimited SAR raw data, to obtain uniform sampling in azimuth. Point target simulations and artificially gapped data generated from China's S-band spaceborne SAR system HJ-1-C are shown to validate the proposed scheme.

High-resolution space-borne synthetic aperture radar (SAR) images are important sources of information for marine monitoring and target recognition. For inshore port scenes, there is a wide variety of ship targets; moreover, as the scattering intensity of the land background is much higher than that of the sea surface, similar structures on land in SAR images can cause significant interference to the identification of ship targets, resulting in a high false alarm rate and false recognition rate. Therefore, the classification of ship targets in SAR images still requires professional interpretation and correction to eliminate possible false alarms and recognitions. In this paper, by imitating the basic structural requirements for manual discrimination of target categories, a space-borne SAR high-resolution inshore ship classification and correction method of “Pre-recognition – Fine Recognition – Matching Correction” is designed based on the YOLO recognition network. Taking three types of typical ship targets as examples, a high-resolution SAR inshore ship target dataset and a high-resolution SAR ship component architecture dataset are constructed to train the pre-recognition network and the fine recognition network respectively. The latter is based on the former to recognize the component composition of possible targets, and forms a component arrangement architecture vector. The final recognition results are obtained by matching and correcting the similarity distance with the three types of targets. This method can reduce the false alarm rate in ship target recognition in complex inshore port scenes to less than 10% and correct some false recognition results, thereby improving the accuracy of ship target identification and reducing the cost for manual correction.