Proposed Imaging Algorithm Research Articles

Multifrequency beam-based migration in inhomogeneous media using windowed Fourier transform frames

SUMMARY We consider the problem of inhomogeneous subsurface imaging using beam waves. The formulation is based on the ultra-wide-band phase-space beam summation (UWB-PS-BS) method, which is structured upon windowed Fourier transform (WFT) expansions of surface fields and sources. In this approach, the radiated field is given as a superposition of beam propagators. Here, we use the beams first for expanding the surface sources and the scattered data, and then for imaging where we use the backpropagation and cross-correlation of beams. This formulation enables a target oriented imaging approach, where we take into account only pairs of source and receiver beams that pass near a region of interest, and thus extract only the relevant data arriving from this region. It also leads to a priori sparse representation of both the beam domain data and the beam propagators. A physical cogent for the beam domain data is obtained under the Born approximation. The beam domain data can be approximated as the local interaction between the beam propagators and the medium reflectivity. Thus, one may interpret the beam domain data as a local Snell’s law reflection in the direction defined by the vector summation of the incident beam and backpropagated beam ray parameters. We demonstrate a physical model for the beam domain data and the salient features of the proposed imaging algorithm using numerical examples.

3-D Short-Range Imaging With Irregular MIMO Arrays Using NUFFT-Based Range Migration Algorithm

3-D imaging with irregular planar multiple-input-multiple-output (MIMO) arrays is discussed. Due to signal acquisition on irregular spatial sampling grids by using these antenna arrays, the fast Fourier transform (FFT)-based imaging algorithms cannot readily be used for image formation. To avoid the application of computationally intensive coherent summation algorithms such as filtered backprojection or Kirchhoff migration, we propose a nonuniform FFT (NUFFT)-based MIMO Range Migration Algorithm (i.e., NUFFT-based MIMO-RMA) for efficient microwave imaging. The algorithm exploits NUFFT to reconstruct the wavenumber–domain spectra related to each Fourier frequency. It is generic and applicable to 3-D imaging with irregular planar MIMO arrays. The effects of irregular spatial sampling and signal bandwidth on the imaging performance and computational efficiency of the proposed algorithm are analyzed. Finally, some numerical simulations and experimental results are presented to demonstrate the performance of the proposed imaging algorithm.

KT and azimuth sub‐region deramp‐based high‐squint SAR imaging algorithm mounted on manoeuvring platforms

Owing to the existence of high-order motion parameters and vertical velocity, severe range–azimuth coupling and two-dimensional spatial variability of the imaging parameters degrade the image quality of high-squint synthetic aperture radar (SAR) mounted on manoeuvring platforms. To accommodate these issues, a novel sub-aperture SAR imaging algorithm based on keystone transform (KT) and azimuth sub-region deramp is proposed. In the range dimension processing, a correction function is constructed based on the scene centre to realise non-spatial-variant range cell migration (RCM) correction and range–azimuth decoupling, and then the residual spatial-variant RCM is removed by the KT. In the azimuth dimension processing, considering the impact of the range distortion on the azimuth modulated phase, accurate analytical formula of the signal in the azimuth time domain is deduced. On this basis, a fast azimuth compression method based on the azimuth sub-region deramp is proposed, and the theoretical effective imaging area is analysed. The theoretical analysis and the simulations show that the proposed imaging algorithm has high computational efficiency and can effectively expand the imaging area of high-squint SAR mounted on manoeuvring platforms.

Accurate Near-Field Millimeter-Wave Imaging of Concave Objects—A Case Study of Dihedral Structures Under Monostatic Array Configurations

With the fast progress of high-resolution microwave and millimeter-wave (MMW) near-field imaging systems, image artifacts caused by complex high-contrast target become a nonnegligible issue for accurate surface reconstruction and recognition. Among different types of artifacts, those caused by high-order scattering within concave structures are particularly difficult to interpret and to correct. This article focuses on analyzing these unconsidered effects in conventional imaging or beamforming algorithms and chooses a typical imaging scenario with dihedral structures under near-field monostatic cylindrical imaging geometry for discussion. The mechanism of multiple reflections and the formation of artifacts are firstly investigated, and the artifacts are classified into two categories based on the number of reflection times. Then shooting and bouncing rays (SBRs) method is utilized to establish an analytical forward model including the mechanism of multiple reflections. Furthermore, a new imaging algorithm based on this forward model is proposed to obtain more accurate reconstructions. By compensating phase shifts of different multiple reflection signals with the corresponding range equations, dihedral-type objects with different opening angles can be accurately recovered. Numerical results are presented to verify the applicability of the forward model and to validate the effectiveness of the proposed imaging algorithm. In the end, experimental results confirm the practicability of the proposed method.

Focusing High-Squint Synthetic Aperture Radar Data Based on Factorized Back-Projection and Precise Spectrum Fusion

This paper presents a microwave imaging algorithm for high-squint airborne synthetic aperture radar (SAR), which combines back-projection and spectrum fusion together. Two spectrum center functions are proposed for linear and nonlinear trajectories respectively, which are the main contributions of this paper, and not considered in conventional work for high-squint SAR. For linear trajectory, the whole aperture data is first divided into sub-apertures with equal length, and the sub-aperture data is backprojected to a unified polar coordinate to generate multiple low-resolution sub-images. Then, these sub-images are corrected by an accurate spectrum center function, which is caused by the presence of squint angle. After spectrum center correction, spectrums of these sub-images can be coherently connected in cross-range wavenumber domain, generating the whole aperture spectrum. Next, the full-resolution image can be obtained by cross-range Fourier transform. For nonlinear trajectory, the deviations introduce extra spectrum shift, which degrades the focusing performance. Another spectrum center function is proposed according to angular-variant motion-error model, which helps to perform precise spectrum fusion. The proposed imaging algorithm is called high-squint accelerated factorized back-projection (HS-AFBP), and it helps to improve the focusing precision. Both the simulation and real data experiments validate the effectiveness of the proposed HS-AFBP algorithm.

New imaging algorithm for range resolution improvement in passive Global Navigation Satellite System‐based synthetic aperture radar

This study proposes a new imaging algorithm for passive Global Navigation Satellite System-based synthetic aperture radar to improve range resolution. In the proposed algorithm, to reduce range compressed pulse mainlobe ambiguity caused by pseudo-random noise code frequency, second-order differentiation is carried out to the square of range compressed signal with respect to range delay lag. Because the carrier phase value is distorted in the squaring procedure, thereafter, a recovery processing is applied to each identified range compressed pulse for preserving the carrier phase for azimuth compression. Both simulation and experimental results indicate that compared to the conventional imaging algorithm, a significant enhancement in range resolution can be achieved by the proposed imaging algorithm. Meanwhile, from the results, the proposed algorithm will not degrade the compressed scene illumination level.

Obtaining 3D High-Resolution Underwater Acoustic Images by Synthesizing Virtual Aperture on the 2D Transducer Array of Multibeam Echo Sounder

In recent decades, imaging sonar has been the most widely employed remote sensing instruments in the field of underwater detection. The multibeam echo sounder (MBES) plays an important role in obtaining high-accuracy seabed topography. However, the resolution of the MBES substantially decreases with the increasing distance. Synthetic aperture sonar (SAS) achieves constant resolution on the along-track, improving the fineness of the image. However, conventional side-scan SAS usually only achieves 2D images, and gaps always exist. In this modeling and experimental research paper, we propose a novel underwater acoustic imaging scheme to improve the imaging performance of MBES, based on the complementarity of MBES and SAS systems. We design a 2D transducer array to increase the detection efficiency and obtain spatial gain. Moreover, the processing scheme is analyzed to design the working parameters in actual engineering applications. We exploit a target echo simulation approach to establish the research basics of the imaging algorithms, which also reflects the shapes and shadows of targets to match actual situations as realistically as possible. The proposed imaging algorithm synthesizes a virtual aperture receiving array on the along-track and reserves the multi-element manifold on the across-track. This helps to improve the imaging quality of the MBES and achieves high-resolution 3D detection with no gaps. Simulation and tank experimental results demonstrate that the proposed scheme can significantly improve the detection ability of the MBES, especially for small 3D target detection, thus making it suitable for 3D high-resolution underwater detection applications.

Focusing Bistatic Forward-looking Synthetic Aperture Radar Based on An Improved Hyperbolic Range Model and A Modified Omega-K algorithm.

For parallel bistatic forward-looking synthetic aperture radar (SAR) imaging, the instantaneous slant range is a double-square-root expression due to the separate transmitter-receiver system form. The hyperbolic approximation provides a feasible solution to convert the dual square-root expression into a single-square-root expression. However, some high-order terms of the range Taylor expansion have not been considered during the slant range approximation procedure in existing methods, and therefore, inaccurate phase compensation occurs. To obtain a more accurate compensation result, an improved hyperbolic approximation range form with high-order terms is proposed. Then, a modified omega-K algorithm based on the new slant range form is adopted for parallel bistatic forward-looking SAR imaging. Several simulation results validate the effectiveness of the proposed imaging algorithm.

Imaging algorithm for multi‐channel scan synthetic aperture radar with channel error estimation and compensation

The wide swath synthetic aperture radar (SAR) imaging with a fine resolution is fundamentally a contradiction in conventional SAR imaging. To meet the demand of high-resolution width swath (HRWS) imaging, multi-channel scan mode has received a great deal of attention as a promising concept to realise HRWS observation. In this article, based on extended Chirp Scaling algorithm, an imaging algorithm for multi-channel scan SAR is proposed. Furthermore, channel error estimation and compensation are the key point in multi-channel scan imaging and are integrated into the proposed imaging algorithm. The computer simulation is shown to verify the presented imaging algorithm.

Object detectability enhancement under weak signals for passive GNSS-based SAR

Passive Global Navigation Satellite System (GNSS)-based Synthetic Aperture Radar (SAR), known as GNSS-SAR, is a recently developing SAR imaging system. Due to the restrictions of transmission power and long distance transmission between GNSS satellites and earth surface, the received signals can be very weak after reflections, in which a noisy GNSS-SAR image can be resulted in. In this study, a new imaging algorithm for GNSS-SAR objects signal detectability enhancement is proposed. The main idea of the proposed algorithm is to apply joint coherent and non-coherent integrations for azimuth compression processing for each scattering point. In the proposed algorithm, at first, each azimuth resolution cell is partitioned into multiple non-overlapped consecutive mini-slots. To both effectively average out the remaining noise from range compression and reduce azimuth samples for correlation operation, the azimuthally distributed range-compressed signals with migration corrected in each partitioned mini-slot are added together. Then azimuth correlation for the compression per azimuth cell is carried out based on the result obtained from performing the addition scheme. Both theoretical analysis and experimental study show that the proposed algorithm can result in obviously enhanced imaging signal detectability for object identification. Meanwhile, computation with the proposed imaging algorithm is significantly more efficient than with the conventional GNSS-SAR imaging algorithm.

An algebraic reconstruction imaging approach for corrosion damage monitoring of pipelines

In this paper, an algebraic reconstruction technique is presented for corrosion damage monitoring in steel pipes by using helical guided ultrasonic waves. To validate the proposed approach, numerical simulations as well as experimental tests were carried out. For the numerical simulations, a finite element model was created using ABAQUS software to simulate wave propagation in a steel pipe. The model simulates corrosion damage by reducing the thickness of the pipe in a selected location. Experimental tests were conducted on a steel pipe instrumented with a network of six low-profile piezoelectric transducers. Reconstructed images from both the numerical simulations and experimental tests accurately localized the simulated damages. These results suggest that the proposed imaging algorithm has the potential to be effectively used for continuous monitoring of pipelines.

Cross Validation Based Distributed Greedy Sparse Recovery for Multiview Through-the-Wall Radar Imaging

Multiview through-the-wall radar imaging (TWRI) can improve the imaging quality and target detection by exploiting the measurement data acquired from various views. Based on the established joint sparsity signal model for multiview TWRI, a cross validation (CV) based distributed greedy sparse recovery algorithm which combines the strengths of the CV technique and censored simultaneous orthogonal matching pursuit algorithm (CSOMP) is proposed in this paper. The developed imaging algorithm named by CV-CSOMP which separates the total measurements into reconstruction measurements and CV measurements is able to achieve the accurate imaging reconstruction and estimation of recovery error tolerance by the iterative CSOMP calculation. The proposed CV-CSOMP imaging algorithm not only can reduce the communication costs among radar units, but also can provide the desirable imaging performance without the prior information such as the sparsity or noise level. The experimental results have verified the validity and effectiveness of the proposed imaging algorithm.

A Modified Fixed-Point Chirp Scaling Algorithm Based on Updating Phase Factors Regionally for Spaceborne SAR Real-Time Imaging

To realize real-time imaging for the spaceborne synthetic aperture radar (SAR) using the chirp scaling (CS) algorithm, high generation rates of phase factors and heavy computation loads are unavoidable. For example, updating phase factors continuously in the 2-D time or frequency space is difficult for generating phase factors for real-time imaging. To solve this problem, a modified CS algorithm based on updating phase factors regionally is proposed. Moreover, conventional floating-point arithmetic is unaffordable for real-time imaging onboard. Therefore, fixed-point processing is adopted. In the proposed imaging algorithm, the phase factors are updated only at some specified times and frequencies, and fixed-point operation is adopted. Furthermore, based on the paired echo theory and the finite word length error mathematical model, the effect of the proposed imaging algorithm on the SAR image quality is studied. Moreover, based on the above analysis and the experiments of Chinese HJ-1C and GF-3 spaceborne SAR practical measured data, a hardware system including digital signal processor and field-programmable gate array boards is constructed.

Pars interarticularis injury in elite athletes – The role of imaging in diagnosis and management

Injuries of the lumbar neural arch, in particular the pars interarticularis, are widely considered to be due to abnormal bone stress secondary to repetitive loading/shearing, and are a common pathology and a main cause of lower back pain in elite athletes across a range of sports. Medical imaging plays a pivotal role in the diagnosis, monitoring and prognostication of neural arch injury. Early detection is highly desirable in the young elite athlete, as early injuries have been shown to require shorter recovery time and have a higher potential of full healing, whilst accurate grading of injury allows appropriate rehabilitation planning. Various imaging modalities are used in the diagnosis and management of pars stress injury, each with their strengths and weaknesses. There is currently a lack of general consensus over the most appropriate imaging pathway for neural arch injury in this specific group of patients. In this review article, we present an overview of neural arch injury, the various imaging modalities used and their imaging appearances with a brief pictorial review, and a proposed imaging algorithm with special considerations in the young elite athletes.

Fast imaging of scattering obstacles from phaseless far-field measurements at a fixed frequency

This paper is concerned with the inverse obstacle scattering problem with phaseless far-field data at a fixed frequency. The main difficulty of this problem is the so-called translation invariance property of the modulus of the far-field pattern or the phaseless far-field pattern generated by one plane wave as the incident field, which means that the location of the obstacle cannot be recovered from such phaseless far-field data at a fixed frequency. It was recently proved in our previous work Xu et al 2018 (SIAM J. Appl. Math. 78 1737–53) that the obstacle can be uniquely determined by the phaseless far-field patterns generated by infinitely many sets of superpositions of two plane waves with different directions at a fixed frequency if the obstacle is a priori known to be a sound-soft or an impedance obstacle with real-valued impedance function. The purpose of this paper is to develop a direct imaging algorithm to reconstruct the location and shape of the obstacle from the phaseless far-field data corresponding to infinitely many sets of superpositions of two plane waves with a fixed frequency as the incident fields. Our imaging algorithm only involves the calculation of the products of the measurement data with two exponential functions at each sampling point and is thus fast and easy to implement. Further, the proposed imaging algorithm does not need to know the type of boundary conditions on the obstacle in advance and is capable to reconstruct multiple obstacles with different boundary conditions. Numerical experiments are also carried out to illustrate that our imaging method is stable, accurate and robust to noise.

Computationally efficient image reconstruction algorithm for electrical capacitance tomography

The electrical capacitance tomography (ECT) is a visualization measurement method and can reconstruct the spatial permittivity distribution information in a measurement domain based on given capacitance values, in which the effectiveness of the image reconstruction algorithm plays a vital role in real-world engineering applications. Unlike common imaging methods, within the framework of the Tikhonov regularization methodology and the transform-domain sparsity method, a new cost function encapsulating the wavelet-based sparsity constraint is proposed to model the ECT imaging problem. An iteration scheme that integrates the superiorities of the alternating direction method of multipliers algorithm and splits a complicated optimization problem into several simpler sub-problems is developed to seek for the optimal solution of the proposed cost function. Numerical experiments validate that the proposed imaging algorithm is practicable and effective, and can improve the reconstruction accuracy and robustness.

Cyclic Prefix-Based OFDM ISAR Imaging

In recent years, orthogonal frequency-division multiplexing (OFDM) synthetic aperture radar (SAR) imaging has been introduced and an inter-range-cell interference (IRCI) free OFDM SAR has also been proposed. In this paper, we use this idea and propose an IRCI free cyclic prefix (CP)-based OFDM inverse synthetic aperture radar (ISAR) imaging which provides a high range resolution ISAR image. However, in order to use this algorithm, the motion parameters of target should be determined. Thus, before applying the algorithm, the motion parameters are estimated through proposing an iterative entropy-based method (IEBM). The proposed imaging algorithm and the IEBM are verified by simulations for CP-based OFDM ISAR.

Velocity Error Correction Based Tomographic Imaging for Stress Wave Nondestructive Evaluation of Wood

Stress wave testing has been applied in the nondestructive evaluation of wood for many years. However, the anisotropy property of wood and the limited number of sensors prevent an accurate stress wave velocity measurement and the high resolution of tomographic inversion. This paper proposes a tomographic imaging algorithm (IABLE) with a velocity error correction mechanism. The proposed algorithm computed the wave velocity distribution of the grid cells of wood cross-sections by the least square QR decomposition (LSQR) iterative inversion, and then optimized the tomography with a velocity error correction mechanism (ECM). To evaluate the performance of the proposed algorithm, several healthy and defective logs and live trees were selected as the experimental samples, and the nondestructive testing procedures were finished. With the stress wave velocity data sets measured via a PiCUS 3 stress wave testing instrument, the IABLE algorithm was implemented, and the tomographic images of the log samples and live trees were generated. The experimental results demonstrated the effectiveness of the proposed imaging algorithm for the nondestructive evaluation of wood.

Fast 2D super resolution ISAR imaging method under low signal‐to‐noise ratio

Sparse representation (SR)-based inverse synthetic aperture radar (ISAR) imaging method can achieve super-resolution image of a target. However, it is computationally expensive and sensitive to noise. To overcome these two drawbacks, the authors propose the coupled Nesterov linearised Bregman iteration algorithm based on two-dimensional (2D) real-valued dictionaries (CNLBI-TRD) for ISAR imaging. First, the ISAR echoes are taken as a 2D joint SR model in the range frequency-azimuth Doppler domain. Then the 2D complex-valued dictionaries are converted into real-valued ones through unitary transformations. The computational complexity is thus decreased by a factor of at least four. Finally, the CNLBI algorithm is proposed to reconstruct the 2D SR model directly. It combines the Nesterov's accelerated gradient method with the condition number optimisation of sensing matrices. An adaptive-adjustment strategy of the soft threshold parameter is presented. Thus the total iteration numbers can be greatly reduced. The simulation results and real data experiments verify the effectiveness of the proposed imaging algorithm.

A numerical study concerning brain stroke detection by microwave imaging systems

In this paper, a numerical study devoted to evaluate the application of a microwave imaging method for brain stroke detection is described. First of all, suitable operating conditions for the imaging system are defined by solving the forward electromagnetic scattering problem with respect to simplified configurations and analyzing the interactions between an illuminating electromagnetic wave at microwave frequencies and the biological tissues inside the head. Then, preliminary inversion results are obtained by applying an imaging procedure based on an iterative Gauss-Newton scheme to a realistic model of the human head. The proposed imaging algorithm is able to deal with the nonlinear and ill-posed problem associated to the integral equations describing the inverse scattering problem. The aim of the inversion procedure is related to the determination of the presence of a hemorrhagic brain stroke by retrieving the distributions of the dielectric parameters of the human tissues inside a slice of the head model.