A despeckling technique based on multiple image reconstruction and selective 3-dimensional flltering is proposed. Multiple SAR images are reconstructed from a single SAR image by employing compressive sensing (CS) theory. In order to obtain multiple images from single SAR image, multiple subsets of pixels are selected from input SAR image by imposing restriction that each subset has at least 20% difierent pixels from any other subset. These subsets are taken as measurement vectors in CS framework to obtain multiple SAR images. A despeckled image is obtained by employing selective 3-dimensional flltering to multiple reconstructed SAR image. The proposed technique is tested on single look complex TerraSAT-X data set, and experimental results exhibit that the proposed technique outperformed benchmark despekling methods in terms of visual quality and despeckling quality metrics.

/pdf/sar-image-despeckling-by-selective-3d-filtering-of-multiple-3lgxxar74p.pdf

SAR Image Despeckling by Selective 3D Filtering of Multiple Compressive Reconstructed Images

Bistatic forward-looking synthetic aperture radar (BFSAR) is a kind of bistatic SAR (BiSAR) system that can image the forward-looking terrain in the flight direction of a receiver. Current literature and reports about BFSAR imaging are mainly concentrated on stationary scene imaging. In this paper, the ground moving target (GMT) theories for BFSAR are discussed. Firstly, the BFSAR signal models of a general GMT in both time-domain and Doppler-domain are shown. Meanwhile, the echo characteristics of the GMT are analyzed, which include the migration characteristic, the Doppler characteristic, the coupling characteristic, etc. After the above analysis, a GMT detection and imaging method for BFSAR is presented. The main purpose of the GMT detection method is to find out the difference between the GMT and the stationary background in an appropriate domain. However, for BFSAR, it is hard to do this since the relative movements between the BFSAR sensors and the GMT are strongly coupled, i.e., their respective influences cannot be easily separated. Different from the existing detection methods which use Doppler frequency (Doppler centroid) in Doppler-domain or space-time characteristics in space-time domain to differentiate the GMT from stationary background, the main idea of the method proposed in this paper is to distinguish the GMT from stationary background by their different Doppler frequency rates (DFRs) in DFR-domain. However, in order to do this, the stationary background’s DFRs must be equalized and the GMT’s DFR should be distinct from the same DFR of the stationary background. Then, the product second-order ambiguity function (PSAF) is used to detect and focus the GMT. Numerical simulations and experimental data processing verify the validity of the GMT theories and the method shown in this paper.

Bistatic forward-looking SAR ground moving target detection and imaging

Echoes of bistatic forward-looking synthetic aperture radar (BFSAR) disperse to multiple range cells and exist spatial frequency extension as well as Doppler spectrum extension (i.e., namely space–time extension). Furthermore, the characteristics of BFSAR clutter are strongly nonstationary and spatial variants. Because of the abovementioned issues, clutter and moving targets are fully overlapped in the initial 3-D space–time–range raw data domain, and the clutter cannot be suppressed effectively. To solve this problem, a BFSAR multipulse displaced phase center antenna (MP-DPCA) method is proposed in this article. First, keystone transform without Doppler ambiguity is applied to remove the coupling between the space–time and range domains. Hence, the overall complex 3-D processing in the space–time–range domain is reduced to independent 2-D processing in each space–time domain. Subsequently, a spatial-dechirp processing is applied in the space–time domain to eliminate the spatial frequency extension. Meanwhile, Doppler parameters of clutter point scatterers are equalized by nonlinear chirp scaling processing in the frequency and time domains. Accordingly, the Doppler spectrum extension of point scatterers can be eliminated by a uniform azimuth dechirp processing. After aforesaid three steps, the clutter and moving targets are separated in the space–time domain. Finally, based on the linear phase difference of clutter between the channels, a multipulse canceller can be designed to suppress the clutter. Compared with the existing space–time adaptive procession (STAP) and DPCA methods, this method not only overcomes the nonstationary problem in BFSAR but also conquers the strict application conditions of DPCA. Simulation results are given to verify the effectiveness of the proposed method.

Bistatic Forward-Looking SAR MP-DPCA Method for Space–Time Extension Clutter Suppression

Bistatic forward-looking synthetic aperture radar (BFSAR) breaks through the limitations of the conventional monostatic SAR on the forward-looking imaging. However, the problems of range cell migration (RCM) caused by the linear range walk and 2-D spatial variability of Doppler parameters become more serious and complicated in translational variant BFSAR. In this article, a keystone transform is introduced to correct the linear RCM. Based on the characteristics of a small aperture, the nonlinear chirp scaling (NCS) is discussed in the frequency domain to equalize the azimuth-range-dependent Doppler parameters. The improved NCS in our newly proposed BFSAR imaging algorithm, especially the re-definition of range direction and the model of spatial variant phase, differentiates this article from all the existing studies in the literature on BFSAR signal processing. Simulation results and real data processing further validate the effectiveness of the proposed algorithm.

A Frequency-Domain Imaging Algorithm for Translational Variant Bistatic Forward-Looking SAR

This research introduces compressed sensing (CS) prin- ciple into inverse synthetic aperture radar (ISAR) imaging of non- uniform rotation targets, and high azimuth resolution can be achieved with limited number of pulses. Firstly, the sparsity of the echoed signal of radar targets with non-uniform rotation in certain matching Fourier domain is analyzed. Then the restricted isometry property (RIP) and incoherence of partial matching Fourier matrices are checked, following which an ISAR imaging method based on CS for both random sparse aperture and short aperture cases is proposed. In particular, consid- ering the dependence of the sparse dictionary on the relative rotation parameter, a parameter estimation method by the optimal search in fractional Fourier domain is presented. Simulation experiments verify the efiectiveness as well as superiority of the proposed imaging method over traditional methods in terms of imaging performance.

/pdf/isar-imaging-of-non-uniform-rotation-targets-with-limited-39bo5j32vz.pdf

ISAR Imaging of Non-Uniform Rotation Targets with Limited Pulses via Compressed Sensing

High speed analog-to-digital (A/D) sampling and a large amount of echo storage are two basic challenges of high resolution synthetic aperture radar (SAR) imaging. In this paper, a novel SAR imaging algorithm which named CS-MTMAB is proposed based on compressed sensing (CS) and multiple transmitters multiple azimuth beams (MTMAB). In particular, this new algorithm, which respectively reconstructs the targets in range and azimuth directions via CS technique, simultaneously provides a high resolution and wide- swath two-dimensional map of the spatial distribution of targets with a signiflcant reduction in the number of data samples beyond the Nyquist theorem and with an implication in simpliflcation of radar architecture. The simulation results and analysis show that this new imaging scheme allows the aperture to be compressed and presents many important applications and advantages among which include reduced on-board storage constraints, higher resolution, lower peak side-lobe ratio (PSLR) and integrated side-lobe ratio (ISLR), less sampled data than the traditional SAR imaging algorithm, and also indicate that it has high robustness and strong immunity in the presence of serious noise. Finally, the real raw airborne SAR data experiment is performed to validate the proposed processing procedure.

/pdf/applications-of-compressed-sensing-for-multiple-transmitters-43evqp4pof.pdf

Applications of Compressed Sensing for Multiple Transmitters Multiple Azimuth Beams SAR Imaging

Synthetic aperture radar (SAR) techniques are typically used to achieve a high azimuth resolution, but conventional monostatic SAR is not applicable in forward direction because of azimuth ambiguities and poor Doppler resolution. In this paper, we present a new bistatic forward-looking SAR imaging algorithm based on point target reference spectrum using the two-dimensional(2-D) principle of stationary phase to improve the Doppler resolution and to avoid azimuth ambiguities. Based on the bistatic configuration and the novel algorithm, SAR not only can image the forward-looking terrain of the aerocraft, but also plays sufficient role in selflanding and navigation. Finally, simulations are used to validate the proposed spectrum and processing algorithm.

http://www.li-jing.com/papers/Bistatic%20Forward-Looking%20SAR%20Imaging%20Based%20on%20Two-Dimensional%20Principle%20of%20Stationary%20Phase.pdf

Bistatic forward-looking SAR imaging based on two-dimensional principle of stationary phase

High speed analog-to-digital (A/D) sampling and a large amount of echo storage are two basic challenges of high resolution synthetic aperture radar (SAR) imaging. To address these problems, a novel SAR imaging algorithm is proposed based on compressed sensing (CS) in this paper. In particular, this new algorithm provides the approach of receiving echo data via two-dimensional (2-D) random sparse sampling with a significant reduction in the number of sampled data beyond the Nyquist theorem and with an implication in simplification of radar architecture. Then CS technique is used to reconstruct the targets in range and azimuth directions, respectively. The simulation results and error analysis show that the proposed CS-based imaging method presents many important applications and advantages which include less sampled data, lower peak side-lobe ratio (PSLR) and integrated side-lobe ratio (ISLR) and higher resolution than the traditional SAR imaging algorithm.

https://www.li-jing.com/papers/A%20Novel%20Two%20Dimensional%20Imaging%20AlgorithmBased%20on%20Compressed%20Sensing%20for%20Multi-Channel%20SAR.pdf

Two-dimensional random sparse sampling for high resolution SAR imaging based on compressed sensing

High speed A/D sampling and large scale data storage are two basic challenges of the high resolution SAR system. The developing of radar system is limited by these two challenges under the Nyquist sampling theory. Compressed sensing (CS) is a new approach of sparse signals recovered beyond the constraints of Nyquist sampling technique. With the consideration of these problems that might happen and the advantage of CS theory, a novel SAR image processing algorithm based on compressive sensing was proposed in this paper. Using the data whose sampling rate is lower than the required Nyquist sampling rate, the CS-based algorithm operates at range and azimuth dimensional respectively. Experimental results show the presented algorithm based on compressed sensing have a better performance than the conventional SAR algorithm even with only smaller samples, and also indicate that the presented algorithm is robustness with existence of serious noise

http://www.li-jing.com/papers/A%20novel%20SAR%20imaging%20algorithm%20based%20on%20compressed%20sensing.pdf

A novel SAR imaging algorithm based on compressed sensing

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Papers

Applications of Compressed Sensing for Multiple Transmitters Multiple Azimuth Beams SAR Imaging

Bistatic forward-looking SAR imaging based on two-dimensional principle of stationary phase

Two-dimensional random sparse sampling for high resolution SAR imaging based on compressed sensing

A novel SAR imaging algorithm based on compressed sensing