Inverse Synthetic Aperture Radar Research Articles

Multilook Polarimetric 3-D Interferometric ISAR Imaging

This article introduces polarimetric 3-D interferometric inverse synthetic aperture radar (ISAR) imaging process using multiple phase centers via a spatio-sensor multilook algorithm. This approach enables one to take effective advantage of polarimetric scattering mechanisms in 3-D target representations, which may improve target classification and identification. In addition, the multilook algorithm enhances the accuracy of height estimation in noisy conditions. The 3-D interferometric ISAR (InISAR) imaging process is validated using the backhoe synthetic data released by the Air Force Research Laboratory.

A reweighted matrix completion algorithm for sparse inverse synthetic aperture radar imaging

Sparse inverse synthetic aperture radar (ISAR) imaging can be generally achieved by compressed sensing (CS) methods because sparse sampling disables the approach of conventional range Doppler. However, the CS-based methods have to convert the matrix into a vector when the random sampling is adopted, which results in huge memory usage and high computational complexity. Note that matrix completion (MC) is suitable for matrix operations, which can recover random sparse data directly based on the low-rank property of the echo matrix. In order to improve the performance of the conventional MC approaches, a novel reweighted matrix completion method for sparse ISAR imaging is proposed in this paper. To avoid using the same singular value thresholding of the traditional MC method to achieve a low-rank solution, the reweighted scheme for singular values is applied to restrain the rank. Furthermore, the weights of the current iteration are updated with their singular values obtained in the former iteration, rather than using a fixed value. Then, the alternating direction method of multipliers is utilised to alternatively optimise the proposed model, which has an improved computational efficiency. Once the full data is recovered, the ISAR image can be achieved via the conventional 2D inverse fast Fourier transform. Experimental results based on measured data validate the proposed approach that can result in improving imaging performance within several seconds, which is tens of times faster than some reported low-rank-based methods.

Space Target Dynamic Identification by Exploiting Geometrical Feature Flow From ISAR Image Sequences

Dynamic identification is crucial for space target investigation and awareness, which is especially challenging for uncooperative and uncontrolled targets. This article presents a dynamic identification algorithm using the time-variant geometric feature flow extracted from an inverse synthetic aperture radar (ISAR) image sequence. The range-Doppler (RD) projection, along with the sequential radar light of slight of the target component, intrinsically relies on the targets’ dynamic states. With the accommodation of a long short-time memory (LSTM) network, reliable dynamic identification of space targets can be implemented by exploiting the geometrical feature flow from the ISAR sequence. The superiority of the proposed method is demonstrated through comparison experiments.

3D inverse synthetic aperture radar image quality improvement using sparse signal representation

AbstractGeneralisation of one‐dimensional dictionary learning (1DDL) algorithms to Multidimensional (MD) mode and its utilisation in MD data applications, increases the speed and reduces the computational complexity. An example of such an application is 3D inverse synthetic aperture radar (ISAR) image reconstruction and noise reduction. In this study, in addition to MD mode generalisation, the formulation structure of the multidimensional dictionary learning (MDDL) problem is discussed followed by two novel algorithms to solve it. The first one is based on the K‐singular value decomposition algorithm for 1DDL, which uses alternating minimisation and singular value decomposition. The second algorithm is the extension of the sequential generalisation of K‐means 1DDL algorithm to the MD mode. Moreover, the MD tensor denoising method based on the MDDL algorithm (MDDL‐ALG) is proposed. As an application, the proposed method is used to denoise the 3D ISAR image. The numerical simulations reveal that the proposed methods, in addition to reducing the memory consumption and the computational complexity, also enjoy higher convergence rate in comparison to 1D algorithms. Specifically, convergence speed of MD algorithms, depending on the training data size, is up to at least 10 times faster than the equivalent 1D counterparts. As revealed through the simulations, the amount of signal to noise ratio recovered by the proposed methods is almost 2 dB higher than the case using a pre‐designed dictionary for denoising. Moreover, it outperforms about 10 dB over the case with the conventional 3D‐IFFT method for image construction.

A real‐time fine echo generation method of extended false target with radially high‐speed moving

The false-target echo generation to Inverse Synthetic Aperture Radar (ISAR) is significant in jamming the enemy ISAR and promoting ISAR development. Generally, it requires false-target echo coherent with the radar, real time and fine. However, conventional methods, such as digital image synthesizer (DIS), cannot meet those requirements. Moreover, existing methods do not consider the target's radial moving. To meet those demands, we propose an improved method in this study. We equivalently model echo formation as the synthesizer of two independent parts: (1) echo of remote target with radial moving and (2) echo of nearby extended target. In part one, accuracy is improved by utilising the Inner Pulse Motion (IPM) model and complexity is simplified by deducing it as a frequency offset modulation. In part two, the fine extended target echo is constructed by using convolution filtering whose resources consumption can be greatly reduced by separating it into an offline stage and a real-time stage. Our method is verified by algorithm simulations and actual experiments. The results indicate that it can build the fine false-target echo in real-time and can adapt to the target's radial velocity, different resolution and size. Compared with the conventional DIS method, our method reduces the computational complexity significantly and has more comprehensive functions.

Vortex-electromagnetic-wave-based ISAR imaging for high-speed maneuvering targets

Vortex electromagnetic wave (VEMW) carrying orbital angular momentum (OAM), which is expected to introduce additional degrees of freedom in inverse synthetic aperture radar(ISAR) imaging. However, the current research about maneuvering targets is based on the "stop go" hypothesis, which does not apply to high-speed motion scenarios due to the intrapulse movement of the target. To improve the imaging quality, this letter proposes a VEMW-based high-speed maneuvering targets imaging method. Firstly, the ISAR imaging scenario of high-speed target is established. According to the spatial geometric relationship between radar and maneuvering target, the vortex echo is deduced and its characteristics are analyzed. Subsequently, a frequency modulation rate estimation method considering both calculation efficiency and high precision is proposed to realize the accurate estimation of target speed. Then, an adaptive azimuth image compensation method based on minimum entropy is proposed. Through the setting of threshold, the number of component signals in linear frequency modulation (LFM) signal is determined and compensated successively. Finally, the range profile and azimuth profile are combined to reconstruct the three-dimensional information. The simulation results demonstrate that this work can effectively eliminate the influence of high-speed motion on range and azimuth profile, also benefit the development of ISAR imaging technique of high-speed maneuvering targets.

Unblurring ISAR Imaging for Maneuvering Target Based on UFGAN

Inverse synthetic aperture radar (ISAR) imaging for maneuvering targets suffers from a Doppler frequency time-varying problem, leading to the ISAR images blurred in the azimuth direction. Given that the traditional imaging methods have poor imaging performance or low efficiency, and the existing deep learning imaging methods cannot effectively reconstruct the deblurred ISAR images retaining rich details and textures, an unblurring ISAR imaging method based on an advanced Transformer structure for maneuvering targets is proposed. We first present a pseudo-measured data generation method based on the DeepLabv3+ network and Diamond-Square algorithm to acquire an ISAR dataset for training with good generalization to measured data. Next, with the locally-enhanced window Transformer block adopted to enhance the ability to capture local context as well as global dependencies, we construct a novel Uformer-based GAN (UFGAN) to restore the deblurred ISAR images with rich details and textures from blurred imaging results. The simulation and measured experiments show that the proposed method can achieve fast and high-quality imaging for maneuvering targets under the condition of a low signal-to-noise ratio (SNR) and sparse aperture.

Frame Selection Method for ISAR Imaging of 3-D Rotating Target Based on Time–Frequency Analysis and Radon Transform

When a target has 3-D rotation accompanying roll–pitch–yaw movement, it yields an additional phase component, which cannot be compensated for by any of the existing inverse synthetic aperture radar (ISAR) imaging methods. Under this condition, the resulting ISAR image will be severely distorted. Accordingly, selecting a proper imaging frame, where the target has only 2-D rotation, becomes the only solution, especially for range-Doppler (RD) algorithm. To do this, the conventional approach is to directly use the phase of the received signal, and however, this approach is vulnerable to noise and the cross-term interference of multiple scattering centers (SCs) in the range bin. To address these issues, we propose a new frame selection method using the range-instantaneous Doppler (RID) technique and Radon transform to detect the presence of 3-D rotation of the target within imaging frame. The proposed method comprises three steps: 1) obtain joint time–frequency distribution (JTFD) via the smoothed pseudo Wigner–Ville distribution (SPWVD); 2) estimate the orientation angle of the Radon image of the JTFD spectrum; and 3) select the suitable imaging frame using the estimated angle. Furthermore, to demonstrate the effectiveness of the proposed method, experimental results based on simulated and real measured data are provided.

Time-Varying Reflectivity Modulation on Inverse Synthetic Aperture Radar Image Using Active Frequency Selective Surface

Interrupted-sampling repeater modulation (ISRM) can generate multiple virtual target images by sampling and forwarding radar signals to confront the inverse synthetic aperture radar (ISAR). However, the repeating delay is unavoidable and the system is complex. Moreover, numerous works mainly discuss the ISAR image deception based on periodic modulation, and the modulation effect is relatively limited. Active frequency selective surface (AFSS) can perform intermittent amplitude modulation to the reflected signal by varying target reflectivity, thus, it can achieve the function of ISRM. In this paper, the possibility of AFSS applied in the multiple virtual target generation is discussed, and an ISAR image-modulation method is proposed. The radar signal is periodically modulated by varying target reflectivity, which is accomplished by the AFSS-loaded PIN diodes. On this basis, the AFSS modulating frequency is randomly varied in a slow time domain. When the modulation signal is processed by the ISAR system, the produced target image is unfocused and cannot be recognized by radar. Simulation results are utilized to demonstrate the effectiveness of the proposed method.

Photonic ADC-based scheme for joint wireless communication and radar by adopting a broadband OFDM shared signal.

We propose and demonstrate a novel, to the best of our knowledge, joint wireless communication and radar system based on a photonic analog-to-digital converter (PADC), which can receive broadband radio-frequency (RF) signals. Owing to this property, a broadband orthogonal frequency division multiplexing (OFDM) shared signal, which owns obvious advantages in communication applications, can be adopted to realize efficient data communication and high-performance target detection simultaneously. In the experiment, a communication rate of 6 Gbit/s is achieved. Inverse synthetic aperture radar (ISAR) imaging is demonstrated with a two-dimensional (2D) resolution of 3.97 cm × 2.94 cm. Finally, it is verified that high-accuracy radial resolution and high-speed communication can be maintained while increasing the pulse repetition period to detect remote target at around 374.6 m.

Study on the micro-Doppler separation method of bistatic inverse synthetic aperture radar targets with rotating parts based on complex variational mode decomposition

In bistatic inverse synthetic aperture radar (Bi-ISAR) imaging of targets with rotating components, the micro-Doppler effect generated by rotating components can produce interference bands in azimuth resolution, seriously affecting the imaging results and causing difficulties in target recognition. A unique Bi-ISAR target micro-Doppler separation method with rotating components is proposed by introducing the complex variational mode decomposition (CVMD) method. First, the echo signal is decomposed into different intrinsic mode functions (IMFs) by CVMD for the Bi-ISAR imaging target with rotating parts. Then, IMFs are divided into main and micromotion parts by setting the energy threshold. The IMFs belonging to the main part are extracted, and the imaging is performed using the range-Doppler algorithm. The simulation results show that CVMD outperforms the complex empirical mode decomposition algorithm and the complex local mean decomposition algorithm in the Doppler separation of Bi-ISAR targets with rotating components. In addition, CVMD exhibits good robustness and provides innovative ideas for future research on micro-Doppler separation technology.

Space-Based Sub-THz ISAR for Space Situational Awareness - Laboratory Validation

The advantages of sub-terahertz technology (sub-THz, 200–700 GHz) have already been advanced for imaging and recognition of a space object's state from a space-based sensor using inverse synthetic aperture radar (ISAR). The technology benefits from wide absolute signal bandwidths, giving high range resolution and from enhanced sensitivity to surface texture. An experimental validation of such a system in controlled laboratory conditions is presented. Possible image formation methods are proposed and evaluated experimentally. Images of real parts of satellites have been produced at sub-THz frequencies and compared to lower frequency images. Other results include sub-THz bistatic ISAR, and sub-THz cross-polarized images which highlight the scattering from rough surfaces while attenuating the very bright scatterers seen from conventional monostatic images.

Dynamic ISAR Imaging Method for Multiple Moving Vehicles Based on OMP-CADMM

Inverse synthetic aperture radar (ISAR) technology has attracted considerable attention in smart traffic monitoring, owing to the superiority of high-resolution imaging and tracking for moving targets in all-weather. However, in practical application cases, it is hard to obtain a globally-focused image of multiple vehicles by traditional ISAR imaging techniques due to the nonuniform motions of vehicles. Hence, a dynamic ISAR imaging method for multiple moving vehicles based on the joint of orthogonal matching pursuit (OMP) and consensus alternating direction method of multipliers (CADMM) is proposed in this paper. First, the preprocessing for raw data is performed in the wavenumber domain to simplify the imaging task into linear phase error estimation and distributed image recovery. Based on the sparsity of moving vehicles, the OMP method is utilized to estimate the dynamic motion phase error of consecutive frames data for different moving vehicles with less computational overhead. Then, the dynamic ISAR imaging problems of multiple moving vehicles can be transformed into the distributed imaging problem with variously estimated phases. Motivated by this fact, the CADMM optimization framework is exploited for the distributed ISAR imaging problem to obtain a globally-focused result according to the consensus constraint characteristic of multiple vehicles. Lastly, both the simulation and real data experiments are conducted to verify the performance and feasibility of the OMP-CADMM method. Furthermore, the experimental results show that the proposed method is capable of dynamic imaging and de-noising for multiple vehicles without signal separation.

Integrated Tracking and ISAR Imaging Using an Integrated Kalman Filter With Wideband Radar

Traditionally, inverse synthetic aperture radar (ISAR) imaging and target tracking are two separate and independent processes. ISAR imaging is usually achieved after data collecting and tracking for a long time, resulting in the problems of low imaging efficiency, information loss, and probing resource allocation difficulty. In this article, we proposed an integration method of tracking and imaging with wideband radar by constructing the relation among signal, data, and image domains, which can realize accurate tracking and high data-rate imaging at the same time. In this integrated imaging and tracking method, a complex-valued reference high-resolution range profile (HRRP) is generated and predicted by exploiting the relation among range-Doppler image, echo in time domain, and target’s motion state. Then, a Bayes estimation model is built to extract range phase error between the predicted HRRP and the real-measured HRRP, and the Kalman filter can update the motion state. In addition, in order to increase the efficiency, the decentralized Kalman filter is also introduced to realize the subaperture parallel processing and data fusion in this article. Finally, the sequential motion compensation is carried out to realize accurate motion compensation and sequential imaging. The simulated and real-measured data confirm the proposed algorithm’s robust imaging and tracking performance. Experimental results show that the proposed method has excellent efficiency and can simultaneously realize sequential imaging and accurate tracking.

Dynamic Estimation of Spin Satellite From the Single-Station ISAR Image Sequence With the Hidden Markov Model

With the increasing of on-orbit satellites, target dynamic monitoring becomes more and more important in space situation awareness applications. Currently, there are some exploratory methods monitoring attitude-stabilized targets using various high-resolution remote sensing technologies. However, it is still a big challenge to achieve dynamic estimation of spin satellite with single sensor. Inspired by the existing matching based works, this article presents a dynamic estimation method of spin satellite using single-station inverse synthetic aperture radar (ISAR) images. When the angle accumulation is set to be a constant in each ISAR imaging period, the projection change of target structures in the long-term observation sequence is described with the hidden Markov model to build the mathematic expression of target on-orbit state. In this condition, target sequential attitude can be solved with Viterbi decoding algorithm even the azimuth scaling of each image is difficulty due to target spin motion. Then, combined with the radar observation geometry, target sequential attitude parameters are substituted into a spin parameter optimization, which is solved by particle swarm optimization algorithm. In the end, target instantaneous attitude vectors and spin speed are used to express its dynamic during the observation period. Simulation experiments of a typical spin spacecraft, Tiangong-I, confirm the feasibility of the proposed algorithm, and its superiority is also investigated by comparison with the existing work.

다 대역 영상레이다장비의 유령영상 위치

In deramped synthetic aperture radar (SAR) equipment that utilizes two or more different bands (different center frequencies), if different pulse repetition frequencies (PRFs) are employed when using a single receiver, owing to its economic feasibility, images of other bands may appear and become ghost images. In this study, to determine the exact location of a ghost image illustrated in inverse SAR images, mathematical formulas are adopted to determine the location of the ghost image in the multiband. Even if the bandwidth and PRF are the same, if Tu (unprocessed time interval) is different, we can observe that a ghost image appears near the actual image. If a time delay is noted, the ghost image appears at the same position as that in the absence of a delay. Thus, the effect of ghost images should be minimized by changing Tu.

Joint low-rank and sparse representation for micro-Doppler effects removed inverse synthetic aperture radar imaging

In terms of the target with micromotion parts, the readability of inverse synthetic aperture radar (ISAR) image of the main body is influenced by micro-Doppler (m-D) effects. Some facts, such as radar working modes and electromagnetic environment, may lead to sparse aperture, which further induces high side lobes and makes the removal of m-D effects more difficult. To jointly eliminate m-D effects and the interference introduced by sparse aperture, a novel m-D effects removed ISAR imaging algorithm is proposed. In this technique, sparse ISAR imaging with the removal of m-D effects can be established as an optimization model of joint low-rank and sparse representation, which is a quadruple constraint optimization problem, including the low rank of the high-resolution range profile (HRRP) of the main body, the sparsity of the HRRP of micromotion parts, the sparsity of the imaging result of the main body, and the noise constraint of the echo signal. Furthermore, the matrix factorization method is theoretically presented to simplify the optimization process of the nuclear norm, and the alternating direction method of multipliers is utilized to solve all constructed models efficiently. Experimental results based on both simulated and measured data demonstrate the superiority of the proposed algorithm.

A bistatic inverse synthetic aperture radar sparse aperture high‐resolution imaging algorithm with migration compensation

In recent years, bistatic inverse synthetic aperture radar (Bi-ISAR) imaging of targets with sparse aperture has attracted more and more attention. One of the critical issues is that the Bi-ISAR imaging of some large targets with rotational motions is prone to migration through resolution cells, which increases the imaging difficulty. In order to enhance the compensation for through resolution cell migration in those scenarios and improve the accuracy and resolution of Bi-ISAR images under sparse aperture, that is, improving the target recognition of Bi-ISAR, the authors aimed at the high-order migration term and the high-order phase error term and proposed a Bi-ISAR sparse aperture high-resolution imaging algorithm based on complex Bayesian compressed sensing and frequency resampling. The algorithm was solved by ‘distributed’ iteration. The migration compensation under the sparse aperture conditions was completed, and the quality of the reconstructed images was improved. Simulation results verified the effectiveness and superiority of the proposed algorithm.

Autofocusing of Maneuvering Targets in Terahertz Inverse Synthetic Aperture Radar Imaging Based on Damped Newton Method.

Maneuvering target imaging based on inverse synthetic aperture radar (ISAR) imaging has recently drawn significant attention. Among the many autofocusing technologies which are crucial in ISAR imaging, minimum-entropy-based autofocusing (MEA) is highly robust. However, traditional MEA is not suitable for terahertz (THz) ISAR imaging. For one thing, the iterative process in traditional MEA is too complicated to be utilized for THz-ISAR imaging with tremendous data. For another, THz wavelengths are very short and extremely sensitive to phase errors, so the compensation accuracy of the traditional MEA method can hardly meet the requirements of THz radar high-resolution imaging. Therefore, in this paper, the MEA algorithm based on the damped Newton method is proposed, which improves computational efficiency by approximating the first- and second-order partial derivatives of the image entropy function with respect to the phase errors, as well as by the fast Fourier transform (FFT). The search step size factor is introduced to ensure that the algorithm can converge along the declination direction of the entropy function and obtain the globally optimal ISAR image. The experimental results validated the efficiency of the proposed algorithm, which is promising in THz-ISAR imaging of maneuvering targets.

Precise motion compensation method in terahertz ISAR imaging based on sharpness maximization

According to the characteristics of the manoeuvring targets and the superiorities of Terahertz Inverse Synthetic Aperture Radar (THz-ISAR), a precise motion compensation method based on image sharpness maximization for manoeuvring targets in the terahertz radar was proposed in this letter. Computational efficiency and compensation accuracy are improved by modified Newton's method. Then a 330-GHz imaging radar and an experiment on a corner reflector were introduced. The validity of the method was verified, by the experimental results, to be promising in THz-ISAR imaging of manoeuvring targets.