48 research outputs found
Accurate Reconstruction and Suppression for Azimuth Ambiguities in Spaceborne Stripmap SAR Images
In this letter, an accurate mathematical model for azimuth ambiguity in stripmap synthetic aperture radar (SAR) images is first constructed, with an azimuth ambiguity factor (AAF) defined as the residual amplitude and phase terms of ambiguities. Next, a novel framework for reconstructing and suppressing azimuth ambiguity is proposed based on the analysis of the AAF. In this framework, azimuth ambiguities are accurately reconstructed by applying reconstruction filters in the range Doppler and 2-D frequency domain, and then, the reconstructed signal is used for suppressing azimuth ambiguities. Moreover, the proposed framework does not depend on the statistical characteristics of a SAR image and is capable of reducing the space-variant ambiguities. As verified by both simulated data and real TerraSAR-X data, the proposed method is capable of suppressing azimuth ambiguities in SAR images
Multistatic SAR Imaging: Comparison of Simulation Results and Experimental Data
Synthetic aperture radar (SAR) systems in a multistatic configuration are a promising candidate for future Earth observation and reconnaissance radar systems. They overcome the sampling constraints inherent to single-channel SAR systems. Thus, a multistatic SAR system enables the acquisition of high-resolution images while maintaining wide-swath coverage. Employing several small satellites instead of a single large one, a cost-efficient system with graceful degradation characteristics can be envisaged. Additionally, such a constellation or swarm of sensors offers interferometric and tomographic capabilities, which a single-satellite system is not able to provide. This paper shows results of multistatic experiments obtained with TerraSAR-X and TanDEM-X and compares these results with theoretical simulations. The key parameters analyzed are the Doppler spectrum and the azimuth ambiguity suppression
Arrayed synthetic aperture radar
In this thesis, the use of array processing techniques applied to Single Input
Multiple Output (SIMO) SAR systems with enhanced capabilities is investigated.
In Single Input Single Output (SISO) SAR systems there is a high resolution,
wide swath contradiction, whereby it is not possible to increase both cross-range
resolution and the imaged swath width simultaneously. To overcome this, a
novel beamformer for SAR systems in the cross-range direction is proposed. In
particular, this beamformer is a superresolution beamformer capable of forming
wide nulls using subspace based approaches.
SIMO SAR systems also give rise to additional sets of received data, which
includes geometrical information about the SAR and target environment, and
can be used for enhanced target parameter estimation. In particular, this thesis
looks at round trip delay, joint azimuth and elevation angle, and relative target
power estimation. For round trip delay estimation, the use of the traditional
matched filter with subspace partitioning is proposed. Then by using a joint
2D Multiple Signal Classification (MUSIC) algorithm, joint Direction of Arrival
(DOA) estimation can be achieved. Both the use of range lines of raw SAR
data and the use of a Region of Interest (ROI) of a SAR image are investigated.
However in terms of imaging, MUSIC is not well-suited for SAR, due to its
target response not corresponding to the target's true power return. Therefore a
joint DOA and target power estimation algorithm is proposed to overcome this
limitation.
These algorithms provide the framework for the development of three processing
techniques. These allow sidelobe suppression in the slant range direction, along
with the reconstruction of undersampled data and region enhancement using
MUSIC with power preservation.Open Acces
SPHR-SAR-Net: Superpixel High-resolution SAR Imaging Network Based on Nonlocal Total Variation
High-resolution is a key trend in the development of synthetic aperture radar
(SAR), which enables the capture of fine details and accurate representation of
backscattering properties. However, traditional high-resolution SAR imaging
algorithms face several challenges. Firstly, these algorithms tend to focus on
local information, neglecting non-local information between different pixel
patches. Secondly, speckle is more pronounced and difficult to filter out in
high-resolution SAR images. Thirdly, the process of high-resolution SAR imaging
generally involves high time and computational complexity, making real-time
imaging difficult to achieve. To address these issues, we propose a Superpixel
High-Resolution SAR Imaging Network (SPHR-SAR-Net) for rapid despeckling in
high-resolution SAR mode. Based on the concept of superpixel techniques, we
initially combine non-convex and non-local total variation as compound
regularization. This approach more effectively despeckles and manages the
relationship between pixels while reducing bias effects caused by convex
constraints. Subsequently, we solve the compound regularization model using the
Alternating Direction Method of Multipliers (ADMM) algorithm and unfold it into
a Deep Unfolded Network (DUN). The network's parameters are adaptively learned
in a data-driven manner, and the learned network significantly increases
imaging speed. Additionally, the Deep Unfolded Network is compatible with
high-resolution imaging modes such as spotlight, staring spotlight, and sliding
spotlight. In this paper, we demonstrate the superiority of SPHR-SAR-Net
through experiments in both simulated and real SAR scenarios. The results
indicate that SPHR-SAR-Net can rapidly perform high-resolution SAR imaging from
raw echo data, producing accurate imaging results
Opportunistic radar imaging using a multichannel receiver
Bistatic Synthetic Aperture Radars have a physically separated transmitter and receiver where one or both are moving. Besides the advantages of reduced procurement and maintenance costs, the receiving system can sense passively while remaining covert which offers obvious tactical advantages. In this work, spaceborne monostatic SARs are used as emitters of opportunity with a stationary ground-based receiver. The imaging mode of SAR systems over land is usually a wide-swath mode such as ScanSAR or TOPSAR in which the antenna scans the area of interest in range to image a larger swath at the expense of degraded cross-range resolution compared to the conventional stripmap mode. In the bistatic geometry considered here, the signals from the sidelobes of the scanning beams illuminating the adjacent sub-swath are exploited to produce images with high cross-range resolution from data obtained from a SAR system operating in wide-swath mode. To achieve this, the SAR inverse problem is rigorously formulated and solved using a Maximum A Posteriori estimation method providing enhanced cross-range resolution compared to that obtained by classical burst-mode SAR processing. This dramatically increases the number of useful images that can be produced using emitters of opportunity. Signals from any radar satellite in the receiving band of the receiver can be used, thus further decreasing the revisit time of the area of interest. As a comparison, a compressive sensing-based method is critically analysed and proves more sensitive to off-grid targets and only suited to sparse scene. The novel SAR imaging method is demonstrated using simulated data and real measurements from C-band satellites such as RADARSAT-2 and ESA’s satellites ERS-2, ENVISAT and Sentinel-1A. In addition, this thesis analyses the main technological issues in bistatic SAR such as the azimuth-variant characteristic of bistatic data and the effect of imperfect synchronisation between the non-cooperative transmitter and the receiver
 Ocean Remote Sensing with Synthetic Aperture Radar
The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography
Interferometric Synthetic Aperture RADAR and Radargrammetry towards the Categorization of Building Changes
The purpose of this work is the investigation of SAR techniques relying on multi image acquisition for fully automatic and rapid change detection analysis at building level. In particular, the benefits and limitations of a complementary use of two specific SAR techniques, InSAR and radargrammetry, in an emergency context are examined in term of quickness, globality and accuracy. The analysis is performed using spaceborne SAR data
Antenna Modeller for Synthetic Aperture Radar Applications. Electromagnetic and Radiometric Considerations
The objective of the present Master Thesis is designing an optimizer of the excitation coefficients of a phased array antenna