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Spotlight Synthetic Aperture Radar Signal Processing Algorithms

Synthetic aperture radar (SAR) interferometry (InSAR) is primarily used in remote-sensing applications and has created a new class of radar data that has significantly evolved during the last couple of decades. Most of the InSAR applications (e.g., topographic mapping and deformation monitoring) typically use a technique called phase unwrapping (PU). In this article, we present an overview of PU techniques in InSAR signal processing. First, we review the established single-baseline (SB) PU methods and then describe innovative PU techniques and concepts related to multibaseline (MB) PU and large-scale (LS) PU. In addition, we discuss several numerical processing examples of these PU techniques. It is our hope that this review will provide guidelines to future researchers to enhance further PU algorithmic developments.

Phase Unwrapping in InSAR : A Review

Phase unwrapping is a crucial signal processing problem in several applications that aims to restore original phase from the wrapped phase. In this letter, we propose a novel framework for unwrapping the phase using deep fully convolutional neural network termed as PhaseNet. We reformulate the problem definition of directly obtaining continuous original phase as obtaining the wrap-count (integer jump of 2 $\pi$ ) at each pixel by semantic segmentation and this is accomplished through a suitable deep learning framework. The proposed architecture consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The relationship between the absolute phase and the wrap-count is leveraged in generating abundant simulated data of several random shapes. This deliberates the network on learning continuity in wrapped phase maps rather than specific patterns in the training data. We compare the proposed framework with the widely adapted quality-guided phase unwrapping algorithm and also with the well-known MATLAB's unwrap function for varying noise levels. The proposed framework is found to be robust to noise and computationally fast. The results obtained highlight that deep convolutional neural network can indeed be effectively applied for phase unwrapping, and the proposed framework will hopefully pave the way for the development of a new set of deep learning based phase unwrapping methods.

PhaseNet: A Deep Convolutional Neural Network for Two-Dimensional Phase Unwrapping

The prediction of Visual Attention data from any kind of media is of valuable use to content creators and used to efficiently drive encoding algorithms. With the current trend in the Virtual Reality (VR) field, adapting known techniques to this new kind of media is starting to gain momentum. In this paper, we present an architectural extension to any Convolutional Neural Network (CNN) to fine-tune traditional 2D saliency prediction to Omnidirectional Images (ODIs) in an end-to-end manner. We show that each step in the proposed pipeline works towards making the generated saliency map more accurate with respect to ground truth data.

SalNet360: Saliency Maps for omni-directional images with CNN

This is essentially the subject of waveform diversity This new flexibility demands new ways of characterising waveform properties and optimising waveform design

Waveform Design and Diversity for Advanced Radar Systems

Space-time adaptive processing based on clutter sparse recovery (SR-STAP) methods outperform traditional statistical-STAP algorithms in scenarios with limited training numbers. However, the computational burden of current SR-STAP methods is extremely heavy, particularly when the number of discretized angle and Doppler grid points is large, which hinders these methods from coming into practical use. This letter proposes a spectrum-aided reduced-dimension SR-STAP method to overcome this issue. The proposed method employs the clutter spectrum estimated by training samples to design the reduced-dimension dictionary. By solving a reduced-dimension sparse recovery problem, the computational load of the proposed method can be reduced significantly while only slightly degrading the performance of clutter suppression and target detection compared with current SR-STAP methods. Numerical experiments using both simulated and measured data validate the effectiveness of the proposed method.

A Novel STAP Based on Spectrum-Aided Reduced-Dimension Clutter Sparse Recovery

This paper studies the performance of the frequency-domain algorithms (FDAs) for low-frequency ultra-wideband synthetic aperture radar (UWB SAR) data processing. First, a generalized theoretical derivation of the FDAs is presented from the viewpoint of SAR signal processing. The derivation not only provides a deeper understanding to the imaging principle of the extended Omega-K algorithm (EOKA), but also makes it compatible and comparable with the other FDAs. Second, the performance comparison on different FDAs is made based on theoretical analysis, simulation and experimental data. The comparison results show that the Omega-K algorithm (ωKA) has the highest imaging precision in the ideal case (i.e, no motion error), but its application is limited by the poor ability of compensating motion errors. In contrast, the EOKA and nonlinear chirp scaling algorithm (NCSA) have excellent performance on dealing with the motion error, but they can only be applied under specific preconditions. Besides, as cetner frequency gets lower, the fractional bandwidth and integration angle get larger, the imaging precision of NCSA greatly decreases, while the ωKA and EOKA still keep high precision.

Performance Evaluation of Frequency-Domain Algorithms for Chirped Low Frequency UWB SAR Data Processing

In this paper, a fast time-domain imaging algorithm called bistatic fast factorized back projection algorithm (BFFBPA) is proposed for the general bistatic VHF/UHF ultra-wideband synthetic aperture radar. It cannot only accurately dispose the large spatial variant range cell migrations, serious range-azimuth coupling and complicated motion error, but also achieve the imaging efficiency similar to frequency-domain algorithms. It represents subimages in elliptical polar coordinate to reduce the computational load. The imaging geometry with arbitrary motion in this coordinate system is provided, and the bistatic back projection algorithm (BPA) is derived to provide a basis for the proposed BFFBPA. Considering motion errors, the more accurate sampling requirements for elliptical subimages is deduced to offer the near-optimum tradeoff between the imaging quality and efficiency, and the constraint of motion errors for acceptable sampling requirements is discussed. Based on this sampling requirement, the advantage of using elliptical subimage grids for this BFFBPA is analyzed. A phase error correction is performed to reduce the impact of phase errors caused by interpolations in the BFFBPA. The speed-up factor of this BFFBPA with respect to the bistatic BPA is derived. Simulation results and evaluations are given to prove the correctness of the theory analysis and validity of the proposed method.

Fast Time-Domain Imaging in Elliptical Polar Coordinate for General Bistatic VHF/UHF Ultra-Wideband SAR With Arbitrary Motion

This paper presents an efficient raw signal generation method based on equivalent scatterer and subaperture processing for the one-stationary bistatic synthetic aperture radar (SAR). It considers the moving platform's motion error to generate the precise raw signal for real SAR systems. First, the imaging geometry in elliptical polar coordinates is built, and then, the scene is divided into several equidistant elliptical rings. Based on the equivalent scatterer model, the approximate SAR system transfer function is derived; thus, each pulse's raw signal is calculated by the convolution of the transmitted signal and transfer function, via the fast Fourier transform (FFT). To further improve the simulation speed, the subaperture and subscene processing is used. The transfer function for the same subaperture's pluses is calculated simultaneously by the weighted sum of all subscenes' equivalent scattering coefficient in the same equidistant elliptical ring, which is performed by the nonuniform FFT (NUFFT). This method only involves the FFT, NUFFT, and complex multiplication, which means the easier operation and higher efficiency. The implementation, limitation, and evaluation of the proposed method are discussed. Simulation results are given to prove the correctness and validity of the proposed method.

Efficient Raw Signal Generation Based on Equivalent Scatterer and Subaperture Processing for One-Stationary Bistatic SAR Including Motion Errors

Phase unwrapping (PU) is significant in reconstructing the digital elevation model of a scene from its interferometric synthetic aperture radar (InSAR) data. The classical minimum discontinuity (MD) PU algorithm by Flynn is highly accurate but involves time-consuming computation. To overcome this shortcoming, we proposed a preunwrapping-assisted MD (PAMD) PU method, which has high efficiency and the same unwrapping quality as Flynn's algorithm. In the PAMD method, the phase image is first unwrapped by the preunwrapping algorithm, which generates and optimizes the dipole cuts. The preunwrapping algorithm is fairly fast, and the obtained result is much closer to the optimal solution of MD; therefore, it can be further optimized by Flynn's algorithm efficiently. Tests on simulated and real InSAR data confirm the accuracy and the efficiency of the proposed method.

Xiaotao Huang

Papers

A Novel STAP Based on Spectrum-Aided Reduced-Dimension Clutter Sparse Recovery

Performance Evaluation of Frequency-Domain Algorithms for Chirped Low Frequency UWB SAR Data Processing

Fast Time-Domain Imaging in Elliptical Polar Coordinate for General Bistatic VHF/UHF Ultra-Wideband SAR With Arbitrary Motion

Efficient Raw Signal Generation Based on Equivalent Scatterer and Subaperture Processing for One-Stationary Bistatic SAR Including Motion Errors

An Efficient Minimum-Discontinuity Phase-Unwrapping Method