Efficient Inverse Scattering Algorithm by Incorporating RPM Method for Microwave Non-destructive Imaging

Electro-magnetic computational techniques are indispensable tools for advanced radar imaging techniques, especially for inverse scattering topics. As one of the most attractive topics in recent years, UWB (Ultra-wide Band) short range radar has a great potential for higher resolution imaging for short range scale, due to holding higher range resolution or favorable penetrating ability for dielectric object. There are so many demands for establishing super-resolution imaging algorithm for robotic sensor in disaster area, non-destructive testing or non-invasive biomedical imaging issues, where the spatial resolution must be remarkably enhanced for competing other internal sensing approaches, such as X-rays or ultra sound imaging. For these demands, we have already developed super-resolution and highly accurate 3-dimensional imaging methods, called as range points migration (RPM) method. The RPM method has a several important advantages comparing with the other imaging method, as synthetic aperture radar, time-reversal approach or other waveform focusing techniques. This method has been verified numerically or experimentally that it provides us super-resolution and 1/100 wavelength reconstruction accuracy by using frequency domain interference (FDI) approach, as Capon method, which is extended to correspond to the scattering waveform deformation. This paper introduces this super-resolution imaging methodologies and clarify the role of accurate EM computational technique as FDTD method for achieving 1/100 wavelength accuracy. This paper also introduces a novel inverse scattering method, namely, the reconstruction of dielectric constant distribution, by combining the unique feature of RPM boundary extraction and the efficient EM computational method. Both numerical and experimental investigations are assessed for proving the effectiveness of the proposed method.

With its consideration for nonlinear scattering phenomena, the distorted Born iterative method (DBIM) is known to provide images superior to those of linear tomographic methods. However, the complexity involved with the production of superior images has prevented DBIM from overtaking simpler imaging schemes in commercial applications. The iterative process and need to solve the forward-scattering problem multiple times make DBIM a slow algorithm compared to diffraction tomography. Fortunately, as computer prices continue to decline, it is becoming easier to assemble large, distributed computer clusters from low-cost personal computer systems. These are well-suited to DBIM inversions, and offer great promise in accelerating the method. Traditional frequency-domain DBIM formulations produce an image by inverting the Fréchet derivative. If the derivative is treated as a matrix, it is costly to construct and awkward to invert on distributed computer systems. This paper presents an interpretation of the Fréchet derivative that is ideal for parallel-computing applications. As a bonus, this formulation reduces the storage requirements of DBIM implementations, making it possible to invert larger problems on a fixed system.

Revised range point migration is a fast, noise-tolerant technology for ultra wide-band imaging. One important application of this technology is security systems to detect concealed weapons in security-controlled places such as train stations or airports. Such systems require higher resolution to distinguish weapons from benign objects or artifacts. In this paper, we propose a modified weighting function for the range point migration method that suppresses sharp amplitude changes due to the interference of multiple scattering waveforms. We apply the proposed method to numerical data to investigate its resolution and performance. We then apply the method to measurement data, which were obtained from a mannequin to demonstrate its imaging capability in a realistic scenario. By introducing this weighting function, results show remarkable improvement in resolution and image quality.

Distorted Born Diffraction Tomography Applied to Inverting Ultrasonic Field Scattered by Noncircular Infinite Elastic Tube

Purpose This paper aims a new implementation of the popular distorted Born iterative method (DBIM) that could effectively solve the inverse scattering problem in microwave breast cancer imaging systems. Design/methodology/approach Instead of a tomographic reconstruction of the unknown permittivity profile of the breast, the proposed algorithm adopts a 3D approach. The algorithm iteratively solves the complex permittivity profile of 3D scatterers with method of moment-based volume integral equation solver. Instead of the surface meshing approach based on Rao−Wilton−Glisson basis function, a volume meshing is adopted here, and implemented on Schaubert−Wilton−Glisson (SWG) basis function. The imaging configuration consists of a group of Hertzian dipole antennas arranged on a hemispherical surface covering the breast. By applying the inverse solver algorithm on these scattered data sets, the complex permittivity profile of the 3D object under test is faithfully reconstructed. Findings The proposed algorithm is tested on a small cube scatterer, and a realistic human breast model. A noise analysis is also performed to assess the robustness of the algorithm under measurement noise. The electromagnetic radiation used in this method has a frequency of 2 GHz. The soft tissues in human breast guarantee the sufficient penetration depth required by the electromagnetic radiation for the complex permittivity reconstruction. Originality/value The novelty of this research is in tailoring DBIM onto a 3D full wave solver with SWG basis function to implement an accurate inverse solver.

Ultrasound imaging offers a safe, non-invasive, non-ionizing tool for clinical diagnosis. Ultrasound tomography would allow ultrasound imaging to reach its full potential, compared to X-ray, MRI, etc. It is based on inverse scattering and has the capability to resolve structures which are smaller than the wavelength of the incident wave, as opposed to conventional ultrasound imaging using echo method. Some material properties such as sound contrast are very useful to detect small objects. The distorted Born iterative method based on first-order Born approximation has been introduced as an efficient diffraction tomography approach. In this paper, we propose to replace linear measurements by random ones in the detection geometry configuration. Reconstructions of circular cylindrical objects including 4.5% of speed of sound in a grid size of 32 × 32 for full 360 degree arrangement of transmitters and detectors are presented. The quality of reconstruction is improved and the complexity is reduced.

This paper presents an efficient three-dimensional nonlinear electromagnetic inversion method in a multilayered medium for geophysical exploration. The stabilized biconjugate-gradient fast Fourier transform algorithm is used for forward modeling, whereas the distorted Born iterative method is used in the inversion. The conjugate-gradient method is applied at each inversion iteration to minimize the cost function. The usage of an iterative solver based on the FFT algorithm and the developed recursive matrix method combined with an interpolation technique to evaluate the layered medium Green's functions rapidly, make this method highly efficient, with the requirement of very low time and memory, O (N log N ) and O (N) respectively. Numerical experiments about the 3D forward modeling with application in Borehole to Surface Electromagnetic Method will be presented in this paper; in addition, we will show the examples about 3D inversion applied in geophysical exploration. All these results will demonstrate the high efficiency and accuracy of the proposed method.

This paper presents a microwave tomography system for ischemic stroke. The developed device consists of a measurement prototype, a matrix switch and a vector network analyzer(VNA). and it equipped with an efficient imaging method for the reconstruction of the distribution of the relative permittivity of the human body under test. It has been preliminarily verified by experimental data. The results show that the system can effectively detect inclusions with different sizes and positions in the head model under consideration.

R wave is short for Rayleigh wave. R wave,on which the effect of gravity is generally inevitable,has received considerably extensive attention and been widely used in many fields,such as geological exploration,nondestructive test,etc. The effects of gravity on R wave velocity for elastic parameters were further analyzed based on available R wave velocity function,and the applicable range of the approximate one was given as well. Wave propagation through quasi-half-space isotropic linear elastic medium with gravity,under the excitation of differentiated Gaussian pulse( DGP) was studied by finite difference time domain( FDTD) method with staggered grid discretization,and extended boundary conditions were introduced.Velocity values obtained were closer to the theoretical ones. Meanwhile,the influence of gravity on time domain,frequency domain response was discussed. By analyzing the theoretical and numerical results,it is necessary to take the effect of gravity into account in simulation for more accurate prediction.

Nondestructive Testing (NDT) assessment of materials’ health condition is useful for classifying healthy from unhealthy structures or detecting flaws in metallic or dielectric structures. Performing structural health testing for coated/uncoated metallic or dielectric materials with the same testing equipment requires a testing method that can work on metallics and dielectrics such as microwave testing. Reducing complexity and expenses associated with current diagnostic practices of microwave NDT of structural health requires an effective and intelligent approach based on feature selection and classification techniques of machine learning. Current microwave NDT methods in general based on measuring variation in the S-matrix over the entire operating frequency ranges of the sensors. For instance, assessing the health of metallic structures using a microwave sensor depends on the reflection or/and transmission coefficient measurements as a function of the sweeping frequencies of the operating band. The aim of this work is reducing sweeping frequencies using machine learning feature selection techniques. By treating sweeping frequencies as features, the number of top important features can be identified, then only the most influential features (frequencies) are considered when building the microwave NDT equipment. The proposed method of reducing sweeping frequencies was validated experimentally using a waveguide sensor and a metallic plate with different cracks. Among the investigated feature selection techniques are information gain, gain ratio, relief, chi-squared. The effectiveness of the selected features were validated through performance evaluations of various classification models; namely, Nearest Neighbor, Neural Networks, Random Forest, and Support Vector Machine. Results showed good crack classification accuracy rates after employing feature selection algorithms.

The integration of antennas in composite structures is gaining popularity with advances in wireless communications and the ever-increasing demands for efficient smart structures. Efforts are ongoing to ensure that antenna-embedded composite structures are robust and resilient to inevitable impacts, loading and other external factors that threaten the structural integrity of these structures. Undoubtedly, the in situ inspection of such structures to identify anomalies and predict failures is required. In this paper, the microwave non-destructive testing (NDT) of antenna-embedded composite structures is introduced for the first time. The objective is accomplished using a planar resonator probe operating in the UHF frequency range (~525 MHz). High-resolution images of a C-band patch antenna fabricated on an aramid paper-based honeycomb substrate and covered with a glass fiber reinforced polymer (GFRP) sheet are presented. The imaging prowess of microwave NDT and its distinct advantages in inspecting such structures are highlighted. The qualitative as well as the quantitative evaluation of the images produced by the planar resonator probe and a conventional K-band rectangular aperture probe are included. Overall, the potential utility of microwave NDT for the inspection of smart structures is demonstrated.

Detection of surface cracks on slanted surfaces and through thick irregular insulation layers using polarimetric microwave imaging

Ceramic based coatings such as Thermal Barrier Coatings (TBCs) are widely deposited onto gas turbine engine components to protect their metal substrate against high temperature. However, improper adhesive application, thermal stress and moisture penetration through the insulation materials can cause defects such as delamination to grow between the insulation and the metal surface. Such defects could lead to catastrophic failure if not immediately detected and repaired. Hence, it is imperative to non-destructively inspect these interfaces for delamination to avoid such an event. In this paper, a novel microwave non-destructive testing technique for ceramic-based coatings is proposed. This technique is based on scanning the surface of the coating with an open-ended rectangular waveguide and analyzing the reflections in the time domain. The proposed time-domain technique appears to reveal more about the depth of any existing delamination than any other microwave non-destructive testing (NDT) technique. Here, a ceramic sample with machined delamination is scanned using rectangular waveguides operating from 26.5 to 40 GHz. After applying Fourier analysis to the frequency domain waveguide reflections, time-domain results showed the delamination with significantly better depth resolution compared to frequency domain analysis. The results reported in this paper prove the advantages of the time-domain technique to forecast the relative delamination depth, which is important for many industrial applications. The depth information of delamination can be used in a condition-based predictive maintenance schedule to plan the inspection intervals efficiently, minimize the unnecessary replacements and provide practical guidance for future design.

The capability of microwave signals to penetrate inside composites and interact with the inner structure makes them a very attractive candidate for composite inspection. Various techniques of microwave nondestructive testing (NDT) are used for detecting disbonds in composites. Despite their promising results, these techniques suffer from poor spatial resolution due to the given features that do not significantly distinguish between the defect and defect-free regions. In this paper, a hybrid signal processing based on a refinement feature extraction method is employed to enhance the imaging efficiency of the disbond detection in composite material. This technique is based on scanning the composite material with an open-ended rectangular waveguide operating from 18 to 26.5 GHz and analyzing its reflections using the proposed hybrid signal processing method. Maximal overlap discrete wavelet packet transform is employed to provide significant informative features of each frequency point. The Bi-directional long short-term memory (Bi-LSTM) network approach is used to distinguish the significant features and the outliers. The Bi-LSTM classifies each inspected location into a defect or defect-free location. The findings presented in this paper show the advantages of the refinement method-aided microwave technique in detecting disbonds down to 1 mm with an accuracy rate of 88.84%, a significant advantage over any current disbond inspection technique.

Glass fiber-reinforced polymer (GFRP) is widely used in engineering fields involving aerospace, energy, transportation, etc. If internal buried defects occur due to hostile environments during fabrication and practical service, the structural integrity and safety of GFRP structures would be severely undermined. Therefore, it is indispensable to carry out effective quantitative nondestructive testing (NDT) of internal defects buried within GFRP structures. Along with the development of composite materials, microwave NDT is promising in non-intrusive inspection of defects in GFRPs. In this paper, quantitative screening of the subsurface impact damage and air void in a unidirectional GFRP via microwave reflectometry was intensively investigated. The influence of the microwave polarization direction with respect to the GFRP fiber direction on the reflection coefficient was investigated by using the equivalent relative permittivity calculated with theoretical analysis. Following this, a microwave NDT system was built up for further investigation regarding the imaging and quantitative evaluation of buried defects in GFRPs. A direct-wave suppression method based on singular-value decomposition was proposed to obtain high-quality defect images. The defect in-plane area was subsequently assessed via a proposed defect-edge identification method. The simulation and experimental results revealed that (1) the testing sensitivity to buried defects was the highest when the electric-field polarization direction is parallel to the GFRP fiber direction; and (2) the averaged evaluation accuracy regarding the in-plane area of the buried defect reached approximately 90% by applying the microwave reflectometry together with the proposed processing methods.