Super-resolution Inverse Synthetic Aperture Radar Research Articles

Optimized complex object classification model: reconstructing the ISAR image of a hypersonic vehicle covered with a plasma sheath using a U-WGAN-GP framework

ABSTRACT This study aims to apply generative adversarial networks (GANs) to the effective classification of high/low resolution (HR/LR) image pairs obtained via inverse synthetic aperture radar (ISAR) for hypersonic objects covered with a plasma sheath. We propose a classification training model based on a Wasserstein GAN with a gradient penalty (U-WGAN-GP) framework, wherein a U-Net with an excellent jump connection structure is used as a generator, and a VGG-Net with high robustness is used as a discriminator, to support the reliable classification of HR/LR ISAR image pairs for the enhancement of ISAR image resolution. The WGAN-GP provides a shortcut for the gradient propagation of network parameters during the training stage through the stable encoder and decoder structure of U-Net. It inherits the echo intensity variation and position distribution characteristics of the scattering points between hypersonic objects in LR images and effectively transplants them into the generated super-resolution ISAR images, and it establishes end-to-end mapping between the HR/LR ISAR images. Moreover, the VGG-Net ensures that the generated super-resolution images are stable, controllable, and undistorted. In addition, the WGAN-GP structure combines the content and adversarial losses, optimizes the generator loss function, and uses the GP method to provide a stable and continuous training process. Finally, a traditional VGG-16 network classifier is added at the end of the training model. The proposed model is applied to an HR/LR image pair dataset of hypersonic objects. Compared with existing methods, the proposed method improved the classification and recognition accuracy from 54.4% to 81.8%.

A novel super-resolution ISAR imaging method based on the EM-turbo technique

ABSTRACT The traditional inverse synthetic aperture radar (ISAR) imaging method, namely, range-Doppler (RD) method, becomes invalid when it comes to the sparse aperture (SA) situation. In this letter, we developed a novel super-resolution SA-ISAR imaging method via the Turbo technique to deal with this problem, which has satisfactory recovery performance and excellent imaging results. To enhance the robustness of the proposed method, we further utilized the expectation maximization (EM) strategy to update the relative model parameters automatically. Experiments on the simulated and real measured data validate the effectiveness of the proposed method.

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.

Real-Time Super-Resolution ISAR Imaging Using Unsupervised Learning

Compressive sensing (CS) enables high-resolution inverse synthetic aperture radar (ISAR) imaging with limited measurements. However, these methods reconstruct images via iterative optimization, resulting in a high computational load. Recently, convolutional neural networks (CNNs) have been used to perform super-resolution ISAR imaging in real time, where high-resolution images are necessarily used as ground truth. However, the desired high-resolution images are not reliable in practice. This letter presents an unsupervised CNN-based framework for super-resolution ISAR imaging. The well-trained CNN can directly produce high-resolution ISAR images in real time. Moreover, the network is trained in an unsupervised manner, which is suitable for practical applications. Furthermore, a pseudo <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula> -norm has been used as the sparse constraint for the exact image reconstruction. The proposed approach has been used to process the real ISAR data, and the experimental results are convincing.

Orbital-angular-momentum-based super-resolution ISAR imaging for maneuvering targets: Modeling and performance analysis

Vortex electromagnetic (EM) waves carrying orbital angular momentum (OAM) wave, whose infinitely helical wavefront and orthogonal eigenvalues of unique physical properties introduce additional rotational degrees of freedom, which has demonstrated superior performance in target detection and azimuthal imaging. In order to enhance the imaging performance for maneuvering targets, this paper offers a solution to develop the vortex inverse synthetic aperture radar (ISAR) technique based on OAM beams design. Firstly, by the space geometry of radar and maneuvering targets, the vortex echo signal model is derived, and the characteristics are analyzed as well. Secondly, pre-processing approach is developed for initial phase compensation by the design of the transmitted OAM beams. Thirdly, the imaging method, based on fractional Fourier transform (FrFT) and Doppler centroid tracking (DCT), is proposed to achieve high-precision range compression and motion compensation to accomplish the range alignment while removing the phases caused by residual azimuth displacements. Finally, after the slow-time pulse compression, the time-invariant Doppler shift is generated, and the well-focused ISAR image can be obtained. The simulation results demonstrate that the proposed vortex ISAR imaging can achieve a higher azimuth resolution than the conventional planar EM ISAR paradigm with the same synthetic aperture length. This discovery provides an approach for OAM-based microwave imaging in a rather efficient manner, which has no harsh requirements for complicated and heavily computational post-processing.

Super-Resolution ISAR Imaging for Maneuvering Target Based on Deep-Learning-Assisted Time–Frequency Analysis

Traditional range-instantaneous Doppler (RID) methods for maneuvering target imaging suffer from the problems of low resolution and poor noise suppression. We propose a new super-resolution inverse synthetic aperture radar (ISAR) imaging method based on deep-learning-assisted time–frequency analysis (TFA). Our deep neural network resembles the basic structure of a U-net with two additional convolutional-upsampling layers and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> -norm loss function for super-resolution generation and noise suppression. The neural network is trained in advance to learn the mapping function between the low-resolution time–frequency spectrum inputs and their high-resolution references. Then, the linear TFA assisted by the pretrained network is integrated into the RID-based ISAR imaging system and is found to achieve sharply focused and denoised target image with super-resolution. Both the simulated and real radar data are used to evaluate the performance of the proposed method. Numerical experimental results demonstrate the superiority of the proposed ISAR imaging method over traditional ones.

Two-Dimension Joint Super-Resolution ISAR Imaging With Joint Motion Compensation and Azimuth Scaling

The quality of inverse synthetic aperture radar (ISAR) images suffers seriously from the two-dimension (2-D) resolution and noise. The motion errors arising from translational and rotational motion further aggravate the image defocusing. For the limited bandwidth and short aperture (LB-SA) signal, this letter proposes a novel 2-D joint super-resolution (2D-JSR) ISAR imaging with joint motion compensation and azimuth scaling (JMCAS) algorithm. In this technique, a 2D-JSR signal model is established, enabling the 2-D high-resolution ISAR image to be generated by solving a sparsity-driven optimization problem with a modified quasi-Newton solver. In addition, a new JMCAS algorithm is developed to enhance the focusing performance of image. Not only can this algorithm jointly correct the range shift and phase error caused by translational motion, it can also complete the azimuth scaling and range spatial-variant phase error (RSVPE) compensation simultaneously. Through the iterative processing of 2D-JSR reconstruction and JMCAS, the well-focused and scaled high-resolution ISAR image can be obtained. Both simulated and real data experiments are provided to verify the effectiveness of the proposed algorithm.

Novel sparse apertures ISAR imaging algorithm via the TLS‐ESPRIT technique

The prevalent imaging method in inverse synthetic aperture radar (ISAR) system is range-Doppler (RD) method, which is implemented by the fast Fourier transformation (FFT). FFT is computationally efficient, but it comes with a price – the problems of wide main-lobes and high side-lobes, especially under the sparse apertures condition. In addition, the resolution of RD method is determined by the radar parameters, which poses limitation to super-resolution imaging, multi-tasking and cognitive reconfigurable applications. In this study, the authors propose a novel super-resolution ISAR imaging method based on the total least squares-estimation of signal parameters via rotational invariance technique (TLS-ESPRIT). The locations and intensities of the scatterers are obtained by employing the ESPRIT algorithm and the least squares technique. Then the ISAR image is formulated, having circumvented all of the above-mentioned limitations. Experiments based on simulated and real measured data validate the effectiveness of the proposed method.

Spectral‐factorisation Root‐MUSIC algorithm for super‐resolution ISAR imaging

Inverse synthetic aperture radar (ISAR) is widely used in both military and civil fields recently. The fast Fourier transformation is the conventional method to obtain high-resolution ISAR images, but it is Rayleigh limited and has high sidelobes. Root-MUSIC served as a super-resolution method can break the Rayleigh limit. Whereas Root-MUSIC has computational redundancy as its roots are presented as conjugated pairs. Here, a spectral factorisation Root-MUSIC (SF-Root-MUSIC) is proposed for super-resolution ISAR imaging. SF-Root-MUSIC efficiently reduces the order of rooting-polynomial of Root-MUSIC into half and has a similar resolution ability as Root-MUSIC. The computational complexity of SF-Root-MUSIC is significantly reduced compared to Root-MUSIC. Simulated signal, MIG-25 data, and real measured Yak-42 data are employed to validate the validity of the proposal.

Super-resolution ISAR imaging based on the modified iterative reweighted approach

High-resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. Targets’ fast rotational motion always introduces migration through range cells (MTRC) when we use the conventional range Doppler algorithm. We mainly focus on the ISAR imaging of a fast rotating target. In that case, the samples in the cross-range dimension are insufficient, which is the undersampling case. Compressed sensing-based ISAR imaging methods are generally faced with the problem of basis mismatch, which may degrade the ISAR image. A two-dimensional iterative reweighted super-resolution algorithm is proposed by iteratively decreasing a surrogate function. We also compared the performance of the proposed method with other state-of-the-art sparse recovery methods. Simulation results show that the proposed method can achieve the high-resolution ISAR imaging of fast rotating targets. Moreover, the MTRC and the basis mismatch problems can be effectively solved.

High-resolution ISAR imaging of fast rotating targets based on pattern-coupled Bayesian strategy for multiple measurement vectors

Very high resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. There may be insufficient pulses or may introduce migration through range cells (MTRC) during the coherent processing interval (CPI) when we use the conventional range Doppler (RD) ISAR technique. With compressed sensing (CS) technique, we can achieve the high-resolution ISAR imaging of a target with limited number of pulses. Sparse representation based method can achieve the super resolution ISAR imaging of a target with a short CPI, during which the target rotates only a small angle and the range migration of the scatterers is small. However, traditional CS-based ISAR imaging method generally faced with the problem of basis mismatch, which may degrade the ISAR image. To achieve the high resolution ISAR imaging of fast rotating targets, this paper proposed a pattern-coupled sparse Bayesian learning method for multiple measurement vectors, i.e. the PC-MSBL algorithm. A multi-channel pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring range cells and correct the MTRC problem. The expectation-maximization (EM) algorithm is used to infer the maximum a posterior (MAP) estimate of the hyperparameters. Simulation results validate the effectiveness and superiority of the proposed algorithm.

Superresolution radar imaging based on fast inverse-free sparse Bayesian learning for multiple measurement vectors

Compressive sensing has been successfully applied to inverse synthetic aperture radar (ISAR) imaging of moving targets. By exploiting the block sparse structure of the target image, sparse solution for multiple measurement vectors (MMV) can be applied in ISAR imaging and a substantial performance improvement can be achieved. As an effective sparse recovery method, sparse Bayesian learning (SBL) for MMV involves a matrix inverse at each iteration. Its associated computational complexity grows significantly with the problem size. To address this problem, we develop a fast inverse-free (IF) SBL method for MMV. A relaxed evidence lower bound (ELBO), which is computationally more amiable than the traditional ELBO used by SBL, is obtained by invoking fundamental property for smooth functions. A variational expectation–maximization scheme is then employed to maximize the relaxed ELBO, and a computationally efficient IF-MSBL algorithm is proposed. Numerical results based on simulated and real data show that the proposed method can reconstruct row sparse signal accurately and obtain clear superresolution ISAR images. Moreover, the running time and computational complexity are reduced to a great extent compared with traditional SBL methods.

2D Superresolution ISAR Imaging via Temporally Correlated Multiple Sparse Bayesian Learning

In inverse synthetic aperture radar (ISAR) imaging, the image resolution is always limited by the bandwidth and the observation time. Sparse recovery (SR) is recently proposed to improve the range resolution or cross-range resolution effectively. However, for the two dimensional superresolution case, a SR-induced range cell migration (RCM) occurs among the High-Resolution Range Profiles (HRRPs) and definitely degrades the ISAR image. After that translational motion compensation is completed, the common sparsity of HRRPs is exploited to suppress the RCM in this paper. Furthermore, by taking the temporal correlation of HRRPs into account, an ISAR imaging method based on temporally correlated Multiple Sparse Bayesian Learning is proposed to improve the imaging quality. Simulated data and real data results demonstrate the effectiveness of the proposed method.

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 fast two dimensional joint linearized bregman iteration algorithm for super-resolution inverse synthetic aperture radar imaging at low signal-to-noise ratios

In practical inverse synthetic aperture radar (ISAR), the traditional imaging algorithms have low range and low cross-range resolutions while the echoes have limited bandwidth and sparse azimuth aperture in small coherent processing interval. To obtain super-resolution ISAR imaging at low signal-to-noise (SNR) ratios, this paper puts forward a novel fast two-dimensional joint linearized Bregman iteration (2D-JLBI) algorithm based on compressive sensing theory. Firstly, the radar echoes are established as a two-dimensional joint sparse representation model in the range frequency-azimuth Doppler domain. Consequently, the original two-dimensional super resolution imaging problem is converted into a two-dimensional jointly compressive reconstruction problem. Secondly, to avoid the reconstruction complexity from the vectorization of the echoes, the two-dimensional joint linearized Bregman iterative algorithm is proposed. Meanwhile, three strategies, namely the weighted residual iteration, estimation of the stagnation step, and optimizing the condition numbers of sensing matrices, are combined to improve the convergence speed. Both the ISAR imaging ability at low SNR and its speed are improved obviously. Finally, simulation experiments show that the proposed algorithm can shorten the imaging time and have better noise robustness under the condition of sub-Nyquist sampling rate and low SNR.

Fast Marginalized Sparse Bayesian Learning for 3-D Interferometric ISAR Image Formation Via Super-Resolution ISAR Imaging

Compressed sensing (CS) has been successfully applied to inverse synthetic aperture radar (ISAR) imaging of moving targets. In existing CS-based ISAR imaging algorithms, Laplace distribution is widely adopted to enforce sparseness on signal recovery. However, this kind of CS method using Laplace prior encounters the problems of determining optimum regularization factor and heavy computation load. In this paper, a fast marginalized sparse Bayesian learning (MSBL) method is proposed for three-dimensional (3-D) interferometric super-resolution ISAR imaging. After deriving the target sparsity-driven imaging model, a fast MSBL approach is applied to obtain super-resolution ISAR image, and then a high-quality 3-D view of the target is achieved via the interferometry technique using an ISAR imagery pair. Experiments on simulated and real data are provided to validate the effectiveness of the proposed method.

Efficient Cross-Range Scaling Method via Two-Dimensional Unitary ESPRIT Scattering Center Extraction Algorithm

For a better understanding of the inverse synthetic aperture radar (ISAR) image, cross-range scaling is an essential step. To deal with the scaling problem of ISAR image, this letter proposes a novel cross-range scaling algorithm for ISAR image. This method consists of three steps. In the first step, joint time-frequency method is employed to compensate the rotational motion and generate 2-D ISAR images at two selected times. Theoretically, after rotating the angular shift between the two images, they present the same appearance. However, conventional image rotation is computationally inefficient and complex for implementation. Thus, in the second step, we propose a 2-D unitary estimation of parameters via rotation invariance technique (ESPRIT) superresolution ISAR imaging technique to extract isolated scatterers instead of conventional ISAR image. In the last step, we search rotation velocity (RV) and rotate the image by angular shift to find the right RV that makes the two selected images match each other best. Finally, simulated and real ISAR data are provided to demonstrate the effectiveness and correctness of the proposed cross-range scaling algorithm.

Superresolution ISAR Imaging Based on Sparse Bayesian Learning

Recently, compressive sensing (CS) has been successfully used in inverse synthetic aperture radar (ISAR) imaging. Since the exact sparse reconstruction, i.e., l0-norm constraint, is NP hard, l1-norm relaxation is widely used at the cost of performance degradation in the sparseness of the solution. The performance of existing CS-based ISAR imaging algorithms is sensitive to the regularized factor, which should be adjusted manually. This makes the existing algorithms inconvenient to be used in practice. It is well known that sparse Bayesian learning (SBL) acts as an effective tool in regression and classification, which is closely related to the CS. Furthermore, all the necessary parameters can be estimated using an efficient evidence maximization procedure in SBL, which retains a preferable property of the l0-norm diversity measure and can give more sparse solution. Motivated by that, a fully automated ISAR imaging algorithm based on SBL is proposed in this paper. Experimental results based on simulated and measured data show that the proposed algorithm keeps a better balance between the computation load and the sparsity of the reconstruction signal than the existing algorithms.

Two-Dimensional Superresolution Inverse Synthetic Aperture Radar Imaging Using Hybridized SVSV Algorithm

This communication presents the hybridized version of recently proposed high resolution “Two-stage separated virtual steering vector-based (SVSV) rank reduction propagator method” (HSVSV) for inverse synthetic aperture radar imaging. HSVSV rearranges the 2D data on virtual array and makes use of SVSV method twice. HSVSV outperformed the SVSV in terms of estimating the positions of target scattering centers lying on horizontal and/or vertical line in target space for signal to noise ratios 0 ≤ SNR ≤ 17 dB, at the cost of doubling the computational load. The superiority of HSVSV to SVSV algorithm is demonstrated using both simulated and experimental ISAR data.