Abstract—Auto-registration video synthetic aperture radar (ViSAR), which requires real time pixel index unifying and resolution matching, is of great significance due to its applicability in multi-aspect observation and continuous monitoring. The phase error induced by the wavefront planar assumption, however, varies with different ViSAR frames, which limits the size of auto-registration imaging scene. To enlarge the auto-registration imaging swath, a parameter-adjusting auto-registration overlapped subaperture algorithm (PAAR-OSA) is proposed in this paper. By collaboratively designing the subapertures within each frame and among different frames in the stabilized-scene coordinate and cooperatively compensating the phase error of all frames, auto-registration with larger imaging swath can be achieved. Both the point targets and distributed targets validation results verify the superiority of the proposed method compared with existing algorithms.

Imaging objects with high relative permittivity and large electrical size remains a challenging problem in the field of inverse scattering. In this work, we present a phaseless inverse scattering method that can accurately reconstruct objects even with these attributes. The novelty of the approach is that it uses a high-frequency approximation for waves passing through lossy media to provide corrections to the conventional Rytov approximation (RA). We refer to this technique as the extended phaseless Rytov approximation for low-loss media (xPRA-LM). Simulation and experimental results are provided for RF indoor imaging using phaseless measurements acquired from 2.4-GHz-based WiFi nodes. We demonstrate that the approach provides accurate reconstruction of objects up to relative permittivities of$15+1.5j$for object sizes greater than 30 wavelengths. Even at higher relative permittivities of up to$\epsilon _{r}=77+ 7j$, object shape reconstruction remains accurate; however, the reconstruction amplitude is less accurate. To the best of our knowledge, xPRA-LM is the first linear phaseless inverse scattering approximation with such a large validity range and can be used to achieve the potential of RF and microwave imaging in applications such as indoor RF imaging.

In this paper, an unrolling algorithm of the iterative subspace-based optimization method (SOM) is proposed for solving full-wave inverse scattering problems (ISPs). The unrolling network, named SOM-Net, inherently embeds the Lippmann-Schwinger physical model into the design of network structures. The SOM-Net takes the deterministic induced current and the raw permittivity image obtained from back-propagation (BP) as the input. It then updates the induced current and the permittivity successively in sub-network blocks of the SOM-Net by imitating iterations of the SOM. The final output of the SOM-Net is the full predicted induced current, from which the scattered field and the permittivity image can also be deduced analytically. The parameters of the SOM-Net are optimized in a supervised manner with the total loss to simultaneously ensure the consistency of the induced current, the scattered field, and the permittivity in the governing equations. Numerical tests on both synthetic and experimental data verify the superior performance of the proposed SOM-Net over typical ones. The results on challenging examples like scatterers with tough profiles or high permittivity demonstrate the good generalization ability of the SOM-Net. With the use of deep unrolling technology, this work builds a bridge between traditional iterative methods and deep learning methods for solving ISPs.

Due to the strong nonlinearity, it is always a challenge to reconstruct strong scatterers with high contrasts and/or large dimensions. This article proposes an iterative domain decomposition technique (IDDT) based on the framework of the subspace-based optimization method (SOM) to solve highly nonlinear inverse scattering problems (ISPs). This method takes advantage of the fact that the reduction of unknowns can reduce the nonlinearity of ISPs and different parts of scatterers have different effects on scattered fields. In the inversion procedure, the domain of scatterers (DoS) is obtained by refining the domain of interests (DoI) first. Then, the DoS is divided into two subdomains according to their contributions to scattered fields: the dominant subdomain and the subordinate subdomain. The induced current of the subordinate subdomain is approximated by its deterministic part. Therefore, only the induced current of the dominant subdomain needs to be reconstructed, greatly reducing the dimensions of the solution domain. Then the properties of the entire DoS are retrieved with the properties of the dominant subdomain as initial guesses. This technique can be used repeatedly to improve the reconstruction quality. Compared with the original SOM, this method can reduce the nonlinearity of ISPs and reconstruct stronger scatterers with better reconstruction qualities and less computation loads. The feasibility and efficiency of IDDT-SOM are discussed from the perspective of the relative distribution of induced current by numerical and experimental examples.

Electrical impedance tomography (EIT) is an attractive technique that aims to reconstruct the unknown electrical property in a domain from the surface electrical measurements. In this work, the induced-current learning method (ICLM) is proposed to solve nonlinear electrical impedance tomography (EIT) problems. Specifically, the cascaded end-to-end convolutional neural network (CEE-CNN) architecture is designed to implement the ICLM. The CEE-CNN greatly decreases the nonlinearities in EIT problems by designing a combined objective function and introducing multiple labels. A noticeable characteristic of the proposed CNN scheme is that the input parameters are chosen as both induced contrast current (ICC) and the updated electrical field from a spectral analysis and the output is chosen as ICC, which is fundamentally different from prevailing CNN schemes. Further, several skip connections are introduced to focus on learning only the unknown part of ICC. ICLM is verified with both numerical and experimental tests on typical EIT problems, and it is found that ICLM is able to solve typical EIT problems in less than 1 second with high image qualities. More importantly, it is also highly robust to measurement noises and modeling errors, such as inaccurate boundary data.

This paper is devoted to solving a full-wave inverse scattering problem (ISP), which is aimed at retrieving permittivities of dielectric scatterers from the knowledge of measured scattering data. ISPs are highly nonlinear due to multiple scattering, and iterative algorithms with regularizations are often used to solve such problems. However, they are associated with heavy computational cost, and consequently, they are often time-consuming. This paper proposes the convolutional neural network (CNN) technique to solve full-wave ISPs. We introduce and compare three training schemes based on U-Net CNN, including direct inversion, backpropagation, and dominant current schemes (DCS). Several representative tests are carried out, including both synthetic and experimental data, to evaluate the performances of the proposed methods. It is demonstrated that the proposed DCS outperforms the other two schemes in terms of accuracy and is able to solve typical ISPs quickly within 1 s. The proposed deep-learning inversion scheme is promising in providing quantitative images in real time.

In this paper, we combine two techniques together, i.e., the fast Fourier transform-twofold subspace-based optimization method (FFT-TSOM) and multiplicative regularization (MR) to solve inverse scattering problems. When applying MR to the objective function in the FFT-TSOM, the new method is referred to as MR-FFT-TSOM. In MR-FFT-TSOM, a new stable and effective strategy of regularization has been proposed. MR-FFT-TSOM inherits not only the advantages of the FFT-TSOM, i.e., lower computational complexity than the TSOM, better stability of the inversion procedure, and better robustness against noise compared with the SOM, but also the edge-preserving ability from the MR. In addition, a more relaxed condition of choosing the number of current bases being used in the optimization can be obtained compared with the FFT-TSOM. Particularly, MR-FFT-TSOM has even better robustness against noise compared with the FFT-TSOM and multiplicative regularized contrast source inversion (MR-CSI). Numerical simulations including both inversion of synthetic data and experimental data from the Fresnel data set validate the efficacy of the proposed algorithm.

Subspace techniques have been introduced in the framework of contrast source (CS) extended born (CSEB) model, for improving its reconstruction capabilities. Two techniques are demonstrated. First, a scheme for generating a good initial guess of the scatterer profile is shown. Second, subspace-based optimization method is used for optimization. Using the suggested techniques, CSEB model can be applied for solving inverse electromagnetic scattering problem with an extended range of application with respect to previous contributions, particularly for very high contrast lossy scatterers.

This paper presents a novel variation of the subspace-based optimization method (SOM) to reconstruct the scatterer's permittivity profile by utilizing only phaseless measurements (i.e., intensity data of the total field with no phase information). Based on spectrum analysis, the contrast source is partitioned into two orthogonally complementary portions (viz., deterministic and ambiguous portions). The original SOM's procedure to obtain the deterministic portion has to be modified in order to accommodate the lack of phase information while the ambiguous portion is determined by another nonlinear optimization. The numerical results presented for the two examples of scatterers under transverse-electric incidence have demonstrated that the proposed method is capable of reconstructing complicated patterns with rapid rate of convergence and robust immunity to noise.

This paper investigates a modified version of the subspace-based optimization method for solving inverse-scattering problems. The method is found to share several properties with the contrast-source-inversion method. The essence of the subspace-based optimization method is that part of the contrast source is determined from the spectrum analysis without using any optimization, whereas the rest is determined by optimization method. This feature significantly speeds up the convergence of the algorithm. There is a great flexibility in partitioning the space of induced current into two orthogonal complementary subspaces: the signal subspace and the noise subspace. This flexibility enables the algorithm to perform robustly against noise. Numerical simulations validate the efficacy of the proposed method: fast convergent and robust against noise.

This paper proposes an improved subspace-based optimization method (SOM) by using a new construction method for the ambiguous part of the induced current. The new current construction method reduces not only the computational complexity of the current construction in every iteration of the optimization but also the computational complexity of the singular-value decomposition of the mapping from the induced current to scattered fields. Thus, the improved SOM is able to deal with the 3-D inverse-scattering problems. Numerical tests validate the algorithm.

Electromagnetic inverse techniques are non-destructive techniques to investigate an unknown region. These techniques use the principle of scattering to determine the number of objects present in the domain, their properties and shapes. However, the scattered field is non-linear function of the objects’ parameters. Direct search methods prove beneficial in solving such problems. In this paper, we study a two-dimensional domain having dielectric elliptic cylinders of infinite length. We try to estimate the location, contour and relative permittivity of the each of the cylinders. The previous works have majorly contributed to optimization of shapes of cylinders made of perfect electric conductor. Here, we investigate cases of domain having single dielectric elliptic cylinder in different orientations and in noisy/noise-free scenarios. We also present results for a noise-free domain containing two dielectric elliptic cylinders. We use Multiple Signal Classification algorithm to find the exact number of cylinders in the domain and their locations. Then, Differential Evolution is used to estimate the relative permittivities and contours of the cylinders.

We propose a spheroidal mode approach to characterize the electromagnetic induction (EMI) response of buried objects, assumed to be much more conductive than their environment. Both the excitation and the response are formulated as the linear superpositions of basic spheroidal modes. The scattering coefficients characterize objects, regardless of their geometrical complexity and material inhomogeneity, due to the orthogonality of the spheroidal modes. The ill-conditioning encountered in retrieving the scattering coefficients is dealt with by mode truncation and Tikhonov regularization. The approach is tested for both simulated and measured data, and the retrieval results show encouragingly that only few excitation and response modes effectively represent the EMI response of the objects. The proposed approach is therefore promising in the detection and classification of buried objects

We use a hybrid model including asymptotic expressions of the spheroidal wave functions (SWFs) to obtain a reliable broadband solution for the electromagnetic induction (EMI) response from a conducting and permeable spheroid. We obtain this broadband response, valid in the magnetoquasistatic regime from zero to hundreds of kilohertz, by combining three different techniques, each applicable over a different frequency range. At low frequencies, the exact analytical solution is used. At midrange frequencies, asymptotic expressions for the angular and radial SWFs are incorporated into the exact solution in order to maintain a stable solution for the induced magnetic field. At higher frequencies, a small penetration approximation (SPA) solution is used when the SPA solution approaches the asymptotically assisted solution to within some predefined tolerance. Validation of this combined technique is accomplished through the comparison of the induced magnetic field predicted by our model to both a finite element/boundary integral (FE-BI) numerical solution and experimental data from various spheroids taken by an ultrawideband EMI instrument.