Target High-resolution Image Research Articles

MIMO Radar Sparse Recovery Imaging with Wideband Interference Prediction

Multiple-input multiple-output (MIMO) radar three-dimensional (3D) imaging is widely applied in military and civil fields. However, MIMO is easily affected by wideband interference (WBI). To solve this problem, in this study, we propose a sparse recovery imaging method with WBI prediction based on the predictive recurrent neural network (PredRNN) and the tensor-based smooth L0 (TSL0) algorithm. Firstly, we extract the time-frequency (TF) feature of the historical measured WBI via the short-time Fourier transform (STFT) operation. In this way, we can use PredRNN to exploit the spatiotemporal correlation of the WBI in the TF domain to predict the TF feature of the WBI in the future. Then, we adaptively design the random sparse stepped frequency waveform by selecting non-overlapped frequencies with the WBI according to the predicted WBI TF feature. Finally, we apply the TSL0 algorithm to reconstruct the 3D high-resolution target image from the sparse signal cube. Simulation results show the high performance and robustness of the proposed imaging method in the presence of different WBIs.

Composite Electromagnetic Scattering and High-Resolution SAR Imaging of Multiple Targets above Rough Surface

Aiming at the high efficiency of composite electromagnetic scattering analysis and radar target detection and recognition utilizing high-range resolution profile (HRRP) characteristics and high-resolution synthetic aperture radar (SAR) images, a near-field modified iterative physical optics and facet-based two-scale model for analysis of composite electromagnetic scattering from multiple targets above rough surface have been presented. In this method, the coupling scattering of multiple targets is calculated by near-field iterative physical optics and the far-field scattering is calculated by the physical optics method. For the evaluation of the scattering of an electrically large sea surface, a slope cutoff probability distribution function is introduced in the two-scale model. Moreover, a fast imaging method is introduced based on the proposed hybrid electromagnetic scattering method. The numerical results show the effectiveness of the proposed method, which can generate backscattering data accurately and obtain high-resolution SAR images. It is concluded that the proposed method has the advantages of accurate computation and good recognition performance.

Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy.

Volumetric imaging by fluorescence microscopy is often limited by anisotropic spatial resolution, in which the axial resolution is inferior to the lateral resolution. To address this problem, we present a deep-learning-enabled unsupervised super-resolution technique that enhances anisotropic images in volumetric fluorescence microscopy. In contrast to the existing deep learning approaches that require matched high-resolution target images, our method greatly reduces the effort to be put into practice as the training of a network requires only a single 3D image stack, without a priori knowledge of the image formation process, registration of training data, or separate acquisition of target data. This is achieved based on the optimal transport-driven cycle-consistent generative adversarial network that learns from an unpaired matching between high-resolution 2D images in the lateral image plane and low-resolution 2D images in other planes. Using fluorescence confocal microscopy and light-sheet microscopy, we demonstrate that the trained network not only enhances axial resolution but also restores suppressed visual details between the imaging planes and removes imaging artifacts.

Design of a new algorithm MFCFT using ISAR imaging to improve the Cross-Range Resolution and Computation Time

SAR imaging is a 2D Radar imaging that produces high resolution images of targets like Ships and Airplane. It uses RD, RID and RIC methods for smoothly rotating, highly rotating and maneuvering targets respectively to generate the images. In this paper, the imaging is done using a new algorithm MFCFT to improve the Cross range resolution and computation time and comparison with different algorithms is shown through the simulated results. Keywords: MFCFT, RD, RID, RIC

Fourier single pixel imaging reconstruction method based on the U-net and attention mechanism at a low sampling rate.

There exists the contradiction between imaging efficiency and imaging quality for Fourier single-pixel imaging (FSI). Although the deep learning approaches have solved this problem to some extent, the reconstruction quality at low sampling rate is still not enough to meet the practical requirements. To solve this problem, inspired by the idea of super-resolution, this paper proposes the paralleled fusing of the U-net and attention mechanism to improve the quality of FSI reconstruction at a low sampling rate. This paper builds a generative adversarial network structure to achieve recovery of high-resolution target images from low-resolution FSI reconstruction results under low sampling rate conditions. Compared with conventional FSI and other deep learning methods based on FSI, the proposed method can get better quality and higher resolution results at low sampling rates in simulation and experiments. This approach is particularly important to high-speed Fourier single pixel imaging applications.

Reconstructing subwavelength resolution terahertz holographic images.

Computer-generated holography typically generates terahertz (THz) holographic images with a pixel size larger than wavelength. We propose a multi-foci metalens model to reconstruct THz holographic images with subwavelength resolution. The designed devices are realized based on dielectric metasurfaces consisting of silicon micropillars with spatially variant orientations. By exploiting quasi-continuous profile of focal points as the pixels of a holographic image, a metalens can reconstruct a high-resolution target image on its focal plane. The effects of size and pitch of each sub-diffraction focal point on imaging quality and pixel resolution are discussed. The intensity distribution at each focal point indicates that the reconstructed images have subwavelength resolution. In comparison with conventional hologram designs, this design method can be used to reconstruct THz holographic images with subwavelength resolution, which have potential applications in THz communication, information security and anti-counterfeiting.

High-Resolution Radar Imaging of Off-Grid Maneuvering Targets Based on Parametric Sparse Bayesian Learning

In high-resolution radar imaging, the time-varying Doppler induced by maneuvering targets generally invalidates traditional methods. Although sparse signal reconstruction methods can be applied to achieve better performance, the off-grid problem embedded in these methods still prohibits well-focused imaging. In this article, we propose to perform high-resolution radar imaging of maneuvering targets with off-grid scattering centers in a Bayesian framework. First, the statistical model with a parametric dictionary is established, in which the Doppler frequencies of scattering centers are treated as unknown model parameters instead of being discretized into the grid. To be consistent with the physical characteristics, the posterior of the Doppler is approximated by the von Mises distribution. Then, the model parameters and rotation parameters are estimated iteratively by variational inference (VI) and the Newton method. Experiments have demonstrated that the proposed method provides an effective way for high-resolution and well-focused radar imaging of maneuvering targets with off-grid scattering centers in complex scenarios, such as incomplete data and low signal-to-noise ratio.

A Method for Signal Leakage Cancellation in Multistatic Subsurface SAR Imaging System

Multi-static subsurface synthetic aperture radar (SAR) is a promising tool capable of providing 3-D high resolution images of targets buried underground. The high detection capability of the system stems from the fact that the multi-static configuration allows for viewing the targets from many directions. However, a primal limiting factor for such configuration is the dominance of the direct and ground reflection signals over the scattered target signal hindering the system sensitivity. This letter presents a simple technique to overcome such drawback. By an appropriate utilization of SAR data, each adjacent sampling points are co-processed together as a two-element array in such a way as to have a null along the direction of the direct and ground reflection signals suppressing such signals and making the target signals more discernible. The technique is described in details and the potential problems arising from array processing are discussed. Additionally, a modified version of back projection imaging algorithm suitable for the array is provided. The performance of the proposed technique is assessed by simulation data of buried metallic spheres as well as measurement data for a buried metallic pipeline. Both simulation and measurements show a significant improvement in target detection and image quality.

EMRNet: End-to-End Electrical Model Restoration Network

The traditional method to improve the resolution in electromagnetic inversion is increasing the number of iterations, which displays poor non-linear mapping and strong non-uniqueness. To meet this challenge, a new strategy is proposed via reconstructing the geoelectric model for traitional inversion results through a deep neural networks (DNN). DNN possesses the advantage on establishing an uncertain mapping between low-resolution images and high-resolution target images. In order to recover the high-precision geoelectric model, we propose an end-to-end electromagnetic recovery network (EMRNet) with novel components to adequately utilize the geoelectric model data of traditional inversion. Specifically, EMRNet uses the codec structure from U-Net, whereby a cross-scale feature attention module (CSFA Block) is incorporated into the decoding process to make full use of feature information of different scales. The superiority of EMRNet are validated on both synthetic and measured data, The predicted geoelectric models of EMRNet are more consistent with the target from the aspects of resistivity values, overall structure, and resolution. In addition, the geoelectric model predicted by EMRNet agree well with the real geological background data and corresponding response data is closer to the measured data.

Integration of Super-Resolution ISAR Imaging and Fine Motion Compensation for Complex Maneuvering Ship Targets Under High Sea State

Under high sea state, ship targets make complex maneuvering motions due to strong disturbances such as sea waves and sea winds. Selecting the optimal imaging time interval to shorten the coherent processing interval (CPI) can reduce the complexity of motion errors, but the imaging resolution is affected as well, i.e., the motion error complexity and imaging resolution are constrained by each other. To address this problem, this article proposes a novel inverse synthetic aperture radar (ISAR) imaging framework for complex maneuvering ship targets to achieve the integration of super-resolution (SR) ISAR imaging and fine motion compensation (ISRFMC) under high sea state. With regard to the complex maneuvering motion of ship targets, we analyze the motion errors caused by the time-variant rotational velocity and imaging projection plane (IPP) on the echo signals, respectively, and a fine phase error model is established to uniformly represent the dual time-variant characteristic (DTVC) of complex maneuvering ship targets. Moreover, a deformed Akaike information criterion (DAIC) is developed to realize the adaptive selection of the phase error model with the image sharpness as the objective function. Underpinned by the Bayesian compressive sensing (BCS) theory, the SR ISAR imaging can be realized by solving a sparsity-driven optimization problem via a modified quasi-Newton solver. Particularly, the fine phase errors are constructed as the model errors of image reconstruction, and the particle swarm optimization algorithm (PSO) is utilized to solve the maximum image sharpness optimization problem in order to perform the joint fine motion compensation and azimuth scaling (JFMCAS). ISRFMC or the integration of SR ISAR imaging and fine motion compensation can be achieved through alternate iteration, so as to obtain well-focused and scaled high-resolution ISAR images of complex maneuvering ship targets under high sea state. Extensive experiments based on both simulated and real data verify that the proposed algorithm is capable of addressing the conflict between imaging resolution and motion error complexity under high sea state.

ISAR Imaging of Target Exhibiting Micro-Motion With Sparse Aperture via Model-Driven Deep Network

This study proposes a model-driven deep network based on the linear alternating direction method of multipliers (L-ADMM), to solve the problem whereby the inverse synthetic aperture radar (ISAR) generates defocused images of targets exhibiting micro-motion with sparse aperture. The network unfolds the operation process of L-ADMM into a model-driven deep network, and automatically optimizes the parameters of the network through learning instead of manually adjusting the parameters, which can better obtain images. Analyses of data acquired through simulations and experimental measurements were used to compare the results of imaging obtained by L-ADMM-net with those of the range Doppler (R-D) algorithm, chirplet algorithm, and L-ADMM. The entropy of images obtained by L-ADMM-net was the lowest, and their image contrast and resolution were the highest. Moreover, L-ADMM-net can generate high-resolution images of targets exhibiting micro-motion with sparse aperture at a low signal-to-noise ratio (SNR), which verifies its robustness. It can also automatically update and adjust parameters more stably than L-ADMM. The proposed method significantly improves the resolution, robustness, and stability of images of targets exhibiting micro-motion in different situations compared with traditional methods, and can provide technical support for target recognition in the future.

A Robust VideoSAR Single Target Tracker by Integrating Correlation Filter and IMM-PDAF

Video synthetic aperture radar (VideoSAR) provides the potential of high-resolution target images with also high frame rate. Different from the target signal in VideoSAR, target shadow straightforwardly indicates its location and track without suffering from the Doppler shift and smearing induced by the motion modulation. The target shadow can be exploited to realize the real-time tracking of the moving target with VideoSAR. In this letter, we propose a robust single target shadow tracker, which integrates the discriminative correlation filter(DCF) and interacting multiple model probabilistic data association filter(IMM-PDAF) to simultaneously overcome the interference of around clutter and target maneuvers. Considering the dependence between target shadow appearance and its dynamic motion, a filtering template updating strategy based on the estimated mode probability was proposed. VideoSAR tracking experiment shows that the proposed algorithm outperforms conventional visual tracking techniques in the robustness aspects.

MDLI-Net: Model-Driven Learning Imaging Network for High-Resolution Microwave Imaging With Large Rotating Angle and Sparse Sampling

Microwave imaging with large rotating angle and sparse sampling is an attractive approach to obtain the high-resolution target image with reduced radar resource. However, the popular imaging methods, e.g., Range-Doppler (RD), back projection (BP), and sparse recovery (SR), are difficult to deal with large rotating angle and sparse sampling simultaneously. In recent years, deep learning (DL) has been widely studied and been successfully used to handle the problems in computer vision. However, since most existing DL networks are put forward for the real visual image and a large amount of data is essential for network training, DL cannot be directly used to process the complex and sparse target echo for microwave imaging. In this article, a new learning imaging framework is proposed and a model-driven learning imaging network (MDLI-Net) is built for high-resolution microwave imaging with large rotating angle and sparse sampling. In the proposed framework, the electromagnetic scattering model is used to generate the training data efficiently, and the sparse microwave imaging theory is applied to guide the design of the deep imaging network. By inputting the 2-D sparse complex-valued target echo, the trained MDLI-Net can output the high-resolution and focused target image efficiently. The effectiveness of the proposed learning imaging method is validated by experiment results with both simulated and real data.

Arbitrary Scale Super-Resolution Neural Network Based on Residual Channel-Spatial Attention

In recent years, the performance of convolutional neural networks in single-image super-resolution has improved significantly. However, most state-of-the-art models address the super-resolution problem for specific scale factors. In this paper, we propose a convolutional neural network for arbitrary scale super-resolution. Specifically, given a range of scale factors, the proposed model can generate super-resolution images with non-integer scale factors within the range. The proposed model incorporates a channel-spatial attention block in which the scale factor is also provided. This module recovers the most relevant information from the low-resolution image given the scale factor and enhances the upsampled image before generating the high-resolution target image. This channel attention block allows learning the channel and spatial dependencies. Additionally, we incorporate global residual learning so that the model recovers the details of an upsampled low-resolution image by interpolation. We evaluated the proposed method through extensive experiments on widely used benchmark datasets for single-image super-resolution. In order to assess the performance of the model, we used the peak signal-to-noise ratio and the structural similarity index measure. The proposed model achieves an average of 35.36, 31.78, 29.62 for peak signal-to-noise ratio, and 0.9410, 0.8828, 0.8334 for structural similarity index measure for the standard evaluation scale factors ×2, ×3, ×4, respectively. The experimental results show a better performance of the proposed model over other state-of-the-art models for arbitrary scale super-resolution, and are competitive with models trained for specific scale factors.

Hybrid SAR-ISAR Image Formation via Joint FrFT-WVD Processing for BFSAR Ship Target High-Resolution Imaging

Bistatic forward-looking synthetic aperture radar (BFSAR) is a kind of bistatic SAR system that can image forward-looking terrain in the flight direction of the receiver. Current literature and reports about BFSAR mainly concentrate on the stationary scene and ground-moving target imaging. Unlike stationary and ground-moving targets, the translational and rotational movements of ship targets usually lead to complicated range cell migration (RCM) and Doppler frequency migration (DFM). Moreover, the characteristics of RCM and DFM for different scattering points of the ship target are significantly different, i.e., the characteristics of the RCM and DFM are 2-D spatial variation, ultimately leading to severe defocusing of ship target in the SAR image. To solve these problems, a kind of hybrid SAR-ISAR imaging formation is proposed for BFSAR ship target imaging. First, to solve the problem of the Doppler ambiguity caused by the forward-looking mode of the receiver, an efficient ambiguity estimation method based on the minimum entropy criterion is presented. Then, keystone transform and range alignment processing can be applied to correct the spatial variant range walk and higher order RCM, respectively. Moreover, in order to obtain a high-resolution and well-focused image after translational compensation, a new method based on the fractional Fourier transform (FrFT) and the Wigner–Ville distribution (WVD) is proposed, where FrFT is applied to separate the multiple main scattering points in each range cell, and WVD is applied to obtain the high-resolution time–frequency distribution of each scattering point. Compared with the conventional ISAR range-Doppler (RD) algorithm and time–frequency estimation-based imaging methods, this method not only has no cross terms but also has high processing accuracy and better antinoise performance.

High-resolution imaging of a high-speed target based on a reconfigurable photonic fractional Fourier transformer.

The previously reported photonics-based radar working with a large bandwidth has the advantages of realizing high-resolution imaging of targets with low velocity. However, the high velocity of a target will introduce Doppler dispersion to the echo signals, which severely deteriorates the imaging resolution. This problem becomes more noticeable as the bandwidth increases. In this paper, we propose a radar receiver based on a reconfigurable photonic fractional Fourier transformer (PFrFTer). The order of the PFrFTer can be reconstructed flexibly by changing the optical transform kernel. When the transform order matches the velocity of the target, the chirp echo signals behave as narrow impulses in the fractional Fourier domain, showing the range information with a high resolution. In the experiment, a PFrFTer is established and applied to process the echo signals with a bandwidth of 12 GHz. A lossless range resolution of 1.4 cm is obtained in range profiles and inverse synthetic aperture radar imaging for high-speed targets. This range resolution is much higher than that in the classical optical de-chirping receiver. These results demonstrate the PFrFTer is immune to the Doppler dispersion effect and is excellent for high-resolution imaging of high-speed target. The introduced technique would be of practical interest in the detection and recognition of targets.

Inverse synthetic aperture radar imaging method of complex maneuvering target based on instantaneous autocorrelation function – chirp Fourier transform

As the maneuverability of aerial targets increases, in the process of using traditional imaging algorithms to image complex maneuvering targets, the results obtained will inevitably appear defocus and blurring. In order to achieve high-resolution imaging of complex maneuvering targets, the inverse synthetic aperture radar (ISAR) imaging method for complex maneuvering targets based on instantaneous autocorrelation function-chirp Fourier transform (IAF-CFT) is proposed in this work. The method first uses IAF to reduce the order of echo, and then chirp Fourier Transform is used to estimate the echo parameters. Finally, the CLEAN technology is adopted to reconstruct the echo and get the result of ISAR imaging. The simulation results prove the effectiveness of this method.

Fast ISAR imaging method for complex manoeuvring target based on local polynomial transform‐fast chirp Fourier transform

In ISAR imaging, echo signals of complex manoeuvring targets have to be modelled as multi-component cubic phase signals (m-CPS). In this condition, the Doppler diffusion will seriously affect the quality of ISAR imaging. Herein, a complex manoeuvring target imaging method based on local polynomial transform-fast chirp Fourier transform (LPT-FCFT) is proposed. In this method, the instantaneous frequency and the quadratic chirp rate of the echo signal are estimated by local polynomial transformation, then the chirp rate and the amplitude are estimated by fast chirp Fourier transform, and finally the high-resolution ISAR image of the complex manoeuvring target is obtained by CLEAN technology. Compared with the traditional method, the proposed method has the advantages of less computational complexity, clearer imaging results and better anti-noise performance. Simulation results indicate the effectiveness and superiority of the proposed method.

Compact long-range single-photon imager with dynamic imaging capability.

Single-photon light detection and ranging (LiDAR) has emerged as a strong candidate technology for active imaging applications. Benefiting from the single-photon sensitivity in detection, long-range active imaging can be realized with a low-power laser and a small-aperture transceiver. However, existing kilometer-range active imagers are bulky and have a long data acquisition time. Here we present a compact co-axial single-photon LiDAR system for kilometer-range 3D imaging. A fiber-based transceiver with a 2.5 cm effective aperture was employed to realize a robust and compact architecture, while a tailored temporal filtering approach guaranteed the high signal-to-noise level. Moreover, a micro-electro-mechanical system scanning mirror was adopted to achieve fast beam scanning. In experiment, high-resolution 3D images of different targets up to 12.8 km were acquired to demonstrate the long-range imaging capability. Furthermore, it exhibits the ability to achieve dynamic imaging at five frames per second over a distance of ∼1km. The results indicate potential in a variety of applications such as remote sensing and long-range target detection.

SDR-Implemented Ground-Based Interferometric Radar for Displacement Measurement

In this article, we demonstrate the use of general software-defined radio (SDR) hardware for ground-based interferometric radar (GBIR) system development for static target imaging and displacement estimation purposes. First, a system synchronization approach is proposed within the free and open-source framework GNU Radio, followed by a frequency-domain bandwidth synthesis method used to improve the range resolution with three different waveforms. Second, data preprocessing and target imaging methods are proposed to reduce the negative influences of practical nonideal factors and get high-quality target images. Finally, various experiments are conducted to show the performance of the developed SDR-GBIR system and the proposed methods. It is shown that high-resolution target image and high-accuracy displacement measurement can be obtained by our SDR-GBIR systems.