High-resolution Inverse Synthetic Aperture Radar Imaging Research Articles

Fast ADMM-based approach for high-resolution ISAR imaging

Due to the sparse nature of inverse synthetic aperture radar (ISAR) images, sparsity-driven algorithms have been developed for high-resolution ISAR imaging. Recently, alternating direction method of multipliers (ADMMs) has been introduced for sparse signal recovery. In this Letter, based on ADMM, a fast algorithm for ISAR imaging is proposed which has lower computational complexity than that of conventional sparse representation-based methods and achieves good robustness in low-signal-to-noise ratio conditions. Experimental results based on both simulations and real data of the Yak-42 airplane show the superiority of the proposed method.

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.

High-Resolution ISAR Imaging and Motion Compensation With 2-D Joint Sparse Reconstruction

With regard to the multifunction radar transmitting sparse stepped-frequency-modulation (SSFM) signal for inverse synthetic aperture radar (ISAR) imaging, the received echo signal is usually sparse in two dimensions, i.e., sparse stepped-frequency-modulation and sparse aperture waveforms (SSFM-SAWs), and there are translational and rotational motion errors between subpulses. The two problems seriously challenge the feasibility of conventional 1-D sparse reconstruction algorithms. This article proposes a novel high-resolution ISAR imaging and motion compensation with the 2-D joint sparse reconstruction (2D-JSR) algorithm. In this technique, a 2D-JSR dictionary is established according to the SSFM-SAW signal model. Based on the Bayesian compressive sensing (BCS) theory, the 2D-JSR is then transformed into solving a sparsity-driven optimization problem with ${l_{1}}$ -norm constraint. With the accommodation of a modified quasi-Newton solver, the exact recovery of SSFM-SAW can be achieved. In addition, a new algorithm, named joint translational motion compensation and range spatial-variant autofocus (JTSVA) algorithm, is also developed to realize motion parameters by a two-step estimation. Integrating with 2-D coupling information of echo signal and the efficient and robust motion compensation algorithm, the accurate motion parameters together with well-focused and scaled high-resolution ISAR images can be obtained. Extensive experiments based on both simulated and real data demonstrate that the proposed algorithm is capable of the precise reconstruction of ISAR images and the effective suppression of both motion errors and noise.

Enhancing ISAR Resolution by a Generative Adversarial Network

Recent studies have shown the superiority of neural networks on imaging equality and efficiency in inverse synthetic aperture radar (ISAR) resolution enhancement, but a central problem remains largely unsolved: all recent studies based on neural networks focused on minimizing the mean-squared reconstruction error (MSE), causing limited enhancing factors and inaccurate recovery of weak point scatters. In order to address this problem, a framework based on a generative adversarial network (GAN) using a combined loss composed of the absolute loss and the adversarial loss is proposed in this letter. The absolute loss ensures that reconstructed high-resolution ISAR images achieve higher enhancing factors and lower sidelobes. The adversarial loss pushes this framework to recover accurate amplitude and position of weak point scatters by a discriminator that is trained to differentiate reconstructed high-resolution ISAR images and real high-resolution ISAR images. Compared to some state-of-the-art methods, our GAN-based framework provides superior reconstruction with higher enhancing factors and more target details.

ISAR Imaging of Multiple Targets With Time-Delay Receive Array

For inverse synthetic aperture radar (ISAR), it is desirable to perform simultaneously imaging of multi-target within a wide spatial angle region. Besides, it is likely that multiple targets are covered by several beams, or sometimes within a single beam, which results in imaging challenge for each single target. To alleviate this problem, this paper devises a new approach by introducing time delay across different receive array elements, realizing multi-target separation and independent imaging. By using the time-delay receive array with linear frequency modulation (LFM) waveform, the equivalent receive beampattern is range-angle-dependent and it is possible to perform beamforming in joint range and angle domain. In the time-delay receive array, an element compensation method is presented to cope with the variation of the equivalent spatial frequency. After receive beamforming, the conventional high resolution ISAR imaging method can be performed. With the proposed method, the multi-target echo can be separated under low signal noise ratio (SNR). Simulation results have verified the effectiveness of the proposed approach.

Iterative Optimization-Based ISAR Imaging With Sparse Aperture and Its Application in Interferometric ISAR Imaging

Recently, high-resolution inverse synthetic aperture radar (ISAR) imaging with sparse aperture (SA) data has attracted increasing attention. The theory of compressive sensing (CS) suggests that an unknown sparse signal can be accurately recovered by taking advantage of very limited samples. For ISAR images, the number of the resolution cells occupied by the strong scattering points of the target is usually much smaller than that of the resolution cells of the image plane, revealing the strong sparsity trait of the ISAR signal. This trait of ISAR signal creates the conditions for incorporating CS into high-resolution ISAR imaging. In this paper, a novel iterative optimization-based SA-ISAR imaging approach is proposed. First, the SA-ISAR signal model is established and the envelope alignment is executed on the one-dimensional range profiles of the SA data. Next, the gradient-based algorithm is exploited to recover the complete signals. Then, by iteratively performing the procedures of the envelope alignment and the signal recovery, the accuracy of signal recovery can be significantly improved and a high-quality ISAR image can be obtained. Ultimately, the extension of the iterative optimization-based SA-ISAR imaging to the three-dimensional (3-D) interferometric ISAR (InISAR) imaging is successfully implemented via the traditional ISAR imagery pair interferometric method. The experiments based on the measured and simulated data are carried out to validate the superiority of the novel algorithm.

Sparse Frequency Waveform Optimization for High-Resolution ISAR Imaging

The stepped-frequency waveform is usually used to synthesize a wideband signal in the radar imaging system. To reduce the amount of data and coherent pulse intervals (CPIs), as well as to improve antijamming abilities, the sparse stepped frequency is employed in the area of inverse synthetic aperture radar (ISAR) imaging. Nevertheless, the traditional sparse stepped linear frequency modulation waveform (SSLFMW) has a shortage of high grating lobes caused by missing frequency bands, resulting in a degradation of the ISAR imaging quality. Many methods have been proposed to reduce the effect of grating lobes by echo signal processing. However, the method of grating lobe reduction is rarely studied from the aspect of waveform optimization. In this article, a novel SSLFMW with the low grating lobes in the ISAR imaging system is proposed. By deriving the autocorrelation function (ACF), the relation between grating lobes and waveform parameters, including stepped-frequency and phase-coded elements, is established. An optimization method based on alternate iteration is designed to optimize waveform parameters and reduce grating lobes. Based on this optimized SSLFMW, we establish an ISAR imaging framework with the compressive sensing (CS) theory. Finally, the experiments are designed to show that the optimized SSLFMW has lower grating lobes. Both simulated and real measured data are used to prove that the optimized waveform has a better performance in the high-resolution range profile (HRRP) synthesis and ISAR imaging compared with the traditional SSLFMW.

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.

Sparse aperture wide‐angle inverse synthetic aperture radar imaging based on compressive sensing

In high-resolution inverse synthetic aperture radar (ISAR) imaging, if the authors enlarge observation angle, range migration would appear and that can cause defocus problem. For full aperture data, the authors can use keystone transform to mitigate the range migration. However the keystone transform is difficult to directly applied in azimuth sparse sampled data. In this article, for azimuth sparse sampled data, the authors analyse the echo model and devise an algorithm based on compressive sensing theory to reconstruct scatters in high-resolution ISAR image. Simulation results demonstrate the validity of the method proposed in the article.

High‐resolution distributed ISAR imaging by OMP method

The distributed inverse synthetic aperture radar (ISAR) technique can form multiple virtual sensors and has the potential to increase the cross-range resolution by exploiting the multi-channel echoes. In the existing imaging approaches, the echoes acquired from different virtual sensors are rearranged into an equivalent single-channel ISAR signal firstly. Then, the missing data between the observation angles of any two adjacent sensors is restored by interpolation. Thus, the echo with long observation time can be acquired. After the fast Fourier transform on this echo, the improved cross-range resolution image can be acquired. However, when the gaps are large, the recovered missing data is very inaccurate. Secondly, if the target rotates non-uniformly and the change of the observation angle is not a linear function with the slow time, the equivalent single-channel ISAR echo cannot be obtained by interpolation. Therefore, this study aims to avoid these drawbacks and get desired distributed ISAR imaging results. After sparse representation of the distributed ISAR echoes, the orthogonal matching pursuit algorithm is used to position the scatterers in every range bin and the final high-resolution ISAR image can be obtained. Simulations show the effectiveness of the proposed method to deal with aforementioned problems.

BSBL‐based multiband fusion ISAR imaging

Multiband fusion imaging can effectively improve the range resolution of inverse synthetic aperture radar (ISAR) imaging. In this study, the block sparse Bayesian learning (BSBL) method is applied to multiband fusion imaging to achieve high-resolution ISAR imaging of a block-structured target. The BSBL method is suitable for the ISAR imaging of numerous and continuous scatterers because it considers the block structure characteristics of the signal. The validity of the proposed method is verified by the simulation and real-data experimental results.

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.

High-Resolution ISAR Imaging With Sparse Aperture VFM Waveforms Under Low SNR Condition

In recent years, the compressed sensing theory has been widely used in sparse aperture radar imaging. The inverse synthetic aperture radar (ISAR) imaging based on the sparse aperture echo of V-style frequency modulation (V-FM) waveform, which can mitigate the ambiguity appeared in range and velocity, has been proposed in this paper. After analyzing and interpreting the reason why the VFM signal pulse compression needs to use dual channels, we built the VFM waveform sparse echo model and analyze the causes of echo sparseness. A modified weighted compressive sensing (MWCS) algorithm is proposed to obtain high-resolution images under strong noise environment. The innovation of this paper lies in the new weighting method and the iterative reconstruction algorithm. The experimental results are shown to demonstrate the validity of the proposed method.

An Efficient ISAR Imaging of Targets with Complex Motions Based on a Quasi-Time-Frequency Analysis Bilinear Coherent Algorithm.

The inverse synthetic aperture radar (ISAR) imaging for targets with complex motions has always been a challenging task due to the time-varying Doppler parameter, especially at the low signal-to-noise ratio (SNR) condition. In this paper, an efficient ISAR imaging algorithm for maneuvering targets based on a noise-resistance bilinear coherent integration is developed without the parameter estimation. First, the received signals of the ISAR in a range bin are modelled as a multicomponent quadratic frequency-modulated (QFM) signal after the translational motion compensation. Second, a novel quasi-time-frequency representation noise-resistance bilinear Radon-cubic phase function (CPF)-Fourier transform (RCFT) is proposed, which is based on the coherent integration of the energy of auto-terms along the slope line trajectory. In doing so, the RCFT also effectively suppresses the cross-terms and spurious peaks interference at no expense of the time-frequency resolution loss. Third, the cross-range positions of target’s scatters in ISAR image are obtained via a simple maximization projection from the RCFT result to the Doppler centroid axis, and the final high-resolution ISAR image is thus produced by regrouping all the range-Doppler frequency centroids. Compared with the existing time-frequency analysis-based and parameter estimation-based ISAR imaging algorithms, the proposed method presents the following features: (1) Better cross-term interference suppression at no time-frequency resolution loss; (2) computationally efficient without estimating the parameters of each scatters; (3) higher signal processing gain because of 2-D coherent integration realization and its bilinear function feature. The simulation results are provided to demonstrate the performance of the proposed method.

ISAR HRRP Super Resolution Reconstruction Based on Couple Dictionary Learning

It is important for Inverse Synthetic Aperture Radar (ISAR) to improve its resolution, which can get more critical information. In this paper, an ISAR super-resolution imaging algorithm based on joint dictionary learning is proposed, which means we find two special sets of sparse signals from numerous high and low resolution ISAR images respectively, this two sets of sparse elements which are also called dictionaries can represent all the original signal linearly. Through the learned dictionaries, we can achieve the super resolution of ISAR images. A coupled dictionary learning algorithm based on Restricted Boltzmann Machine (RBM) is designed to learn high and lowresolution dictionaries. Finally, low-resolution ISAR images are sparsely reconstructed based on dictionaries. The suggested approach has lower computational complexity and higher anti-noise performance.

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.

High-resolution W-band ISAR imaging system utilizing a logic-operation-based photonic digital-to-analog converter.

W-band inverse synthetic aperture radar (ISAR) imaging systems are very useful for automatic target recognition and classification due to their high spatial resolution, high penetration and small antenna size. Broadband linear frequency modulated wave (LFMW) is usually applied to this system for its de-chirping characteristic. However, nearly all of the LFMW generated in electronic W-band ISAR system are based on multipliers and mixers, suffering seriously from electromagnetic interference (EMI) and timing jitter. And photonic-assisted LFMW generator reported before is always limited by bandwidth or time aperture. In this paper, for the first time, we propose and experimentally demonstrate a high-resolution W-band ISAR imaging system utilizing a novel logic-operation-based photonic digital-to-analog converter (LOPDAC). The equivalent sampling rate of the LOPDAC is twice as large as the rate of the digital driving signal. Thus, a broadband LFMW with a large time aperture can be generated by the LOPDAC. This LFMW is up-converted to W band with an optical frequency comb. After photonic-assisted de-chirping processing and data processing to the echo, a high-resolution two-dimension image can be obtained. Experimentally, W-band radar with a time-bandwidth product (TBWP) as large as 79200 (bandwidth 8 GHz; temporal duration 9.9 us) is established and investigated. Results show that the two-dimension (range and cross-range) imaging resolution is ~1.9 cm × ~1.6 cm with a sampling rate of 100 MSa/s in the receiver.

High resolution inverse synthetic aperture radar imaging of three-axis-stabilized space target by exploiting orbital and sparse priors**Project supported by the National Natural Science Foundation of China (Grant Nos. 61601496 and 61401024).

The development of inverse synthetic aperture radar (ISAR) imaging techniques is of notable significance for monitoring, tracking and identifying space targets in orbit. Usually, a well-focused ISAR image of a space target can be obtained in a deliberately selected imaging segment in which the target moves with only uniform planar rotation. However, in some imaging segments, the nonlinear range migration through resolution cells (MTRCs) and time-varying Doppler caused by the three-dimensional rotation of the target would degrade the ISAR imaging performance, and it is troublesome to realize accurate motion compensation with conventional methods. Especially in the case of low signal-to-noise ratio (SNR), the estimation of motion parameters is more difficult. In this paper, a novel algorithm for high-resolution ISAR imaging of a space target by using its precise ephemeris and orbital motion model is proposed. The innovative contributions are as follows. 1) The change of a scatterer projection position is described with the spatial-variant angles of imaging plane calculated based on the orbital motion model of the three-axis-stabilized space target. 2) A correction method of MTRC in slant- and cross-range dimensions for arbitrarily imaging segment is proposed. 3) Coarse compensation for translational motion using the precise ephemeris and the fine compensation for residual phase errors by using sparsity-driven autofocus method are introduced to achieve a high-resolution ISAR image. Simulation results confirm the effectiveness of the proposed method.

High-Resolution Imaging and 3-D Reconstruction of Precession Targets by Exploiting Sparse Apertures

Inverse synthetic aperture radar (ISAR) imaging of a precessing target, which is a kind of fast spinning target, is faced with migration through range cell when using traditional imaging algorithms. Theory of compressed sensing (CS) suggests that exact recovery of an unknown sparse signal with an overwhelming probability can be achieved from very limited number of samples. A cycle shift smoothed L0 algorithm based on CS is proposed in this paper for high-resolution ISAR imaging of precessing targets by exploiting sparse apertures. A precessing cone-shaped target model is built and a 3-D reconstruction method based on multistatic ISAR is proposed. Simulations and electromagnetic computation verify the validity of the proposed method.