Inverse Synthetic Aperture Radar Imaging Research Articles

Three‐dimensional point cloud reconstruction of inverse synthetic aperture radar image sequences based on back projection and iterative closest point fusion

AbstractIn order to recover the three‐dimensional (3D) structure of the target from sequential inverse synthetic aperture radar (ISAR) images, the factorisation method is generally used. It requires a large number of high‐quality matched feature points from different ISAR images. If the number of extracted feature points is insufficient, the restored 3D structure is not obvious. Furthermore, the mismatching of feature points will greatly affect the quality of target reconstruction. However, the factorisation method only uses the information from the ISAR images, while that from imaging geometry is not sufficiently considered. ISAR imaging is a kind of projection, and the projection plane information could be taken into account for the 3D reconstruction. Hence, a new 3D reconstruction method for stable targets from sequential ISAR images is proposed in this paper. Firstly, the ISAR images are preprocessed with the CLEAN algorithm. The maximum between‐cluster variance method is applied to extract feature points from the processed images. Moreover, the image projection plane and projection equation corresponding to different ISAR images are analysed by using imaging geometry information. According to the projection equation, the feature points are back‐projected (BP) to the 3D space. Finally, the 3D point clouds obtained by the BP from multiple radar images are fused by the iterative closest point algorithm to restore the 3D structure of the target. The simulation and experiment results show the effectiveness and robustness of this method.

Noise-Robust ISAR Translational Motion Compensation via HLPT-GSCFT

Translational motion compensation is a prerequisite of inverse synthetic aperture radar (ISAR) imaging. Translational motion compensation for datasets with low signal-to-noise ratio (SNR) is important but challenging. In this work, we proposed a noise-robust translational motion compensation method based on high-order local polynomial transform–generalized scaled Fourier transform (HLPT-GSCFT). We first model the translational motion as a fourth-order polynomial according to order-of-magnitude analysis, and then design HLPT-GSCFT for translation parameter estimation and parametric translational motion compensation. Specifically, HLPT is designed to estimate the acceleration and third-order acceleration of the translational motion and GSCFT is introduced to estimate the second-order acceleration. Both HLPT and GSCFT have a strong ability for cross-term suppression. In addition, we use a minimum weighted entropy algorithm to estimate the velocity of the translational motion, which can improve the noise robustness of the parameter estimation. Experimental results based on a measured dataset prove that the proposed method is effective and noise-robust.

Complex Mutual Information-Based Least-Dependent Component Analysis for ISAR Imaging of Multiple Targets in a Formation Flight

This article proposes a signal decomposition-based approach for inverse synthetic aperture radar (ISAR) imaging of multiple targets in a formation. The procedure of the proposed framework is divided into three steps. The first step is to preprocess the radar echoes received by the spatial multichannel radar via pulse compression. The second step is signal decomposition using the complex-valued mutual information-based least-dependent component analysis proposed in this article. In this step, the range profiles (RPs) of multiple targets are separated into individual RPs to generate an ISAR image for each target. The third step is range-Doppler imaging using the separated RPs. As compared to the conventional methods, the proposed method decomposes the superimposed radar echoes at the raw signal level. Therefore, even if multiple targets overlap in the ISAR image, or RP histories are unaligned owing to the change in the relative positions between multiple targets, a well-focused ISAR image can still be generated. Simulation results using three targets composed of point scatterer centers verify that the proposed method can effectively segment three targets closely flying in a formation.

Efficient ISAR Imaging Approach for Nonuniformly Rotating Targets Based on ICPF-PPP

In this article, we introduce an improved algorithm for practical inverse synthetic aperture radar (ISAR) imaging, by utilizing the advantages of prominent point processing (PPP) and integrated cubic phase function (ICPF) in an efficient way. The proposed ICPF-PPP algorithm achieves a significant improvement compared to the conventional range-Doppler processing for ISAR imaging of a nonuniformly rotating target in terms of both image quality and computational efficiency. The experimental results using real datasets show that the proposed autofocus chain is highly efficient in forming ISAR images of a nonuniformly rotating target.

Extended Drones Tracking From ISAR Images With Doppler Effect and Orientation Based Robust Sub-Random Matrices Algorithm

The simultaneous imaging and tracking of drones and small UAVs is one of the most challenging problems in inverse synthetic aperture radar (ISAR) signal processing and has received significant attention recently. Wide-angle ISAR imaging may offer better resolution and richer geometrical features. Aiming at this issue, we propose a new algorithm for multiple extended drone targets (EDTs) detection and tracking from wide-angle ISAR images. Since a conventional tracking algorithm using a random matrices model (RMM) is not accurate enough to reconstruct a drone's extension by one ellipse, an appropriate technique needs to be developed for ISAR imaging and tracking of EDTs. This paper models the EDT's extension as multiple ellipses with a time-varying orientation angle and solves the resulting interference problem involving data association between the nonlinear measurements and sub-ellipses. Recently, the multi-Bernoulli (MB) filter for extended targets has been proposed with the RMM approach for linear ellipsoidal target tracking by additional state variables. However, the implementation of the MB-RMM filter for nonlinear non-Gaussian models and the Doppler effect has not been presented, especially for ISAR images. We consider linearizing the nonlinear observations to solve the nonlinearity due to the Doppler effect, target motion, and wide-angle ISAR images. We presented a new robust Sub-RMM-MB-TBD filter to obtain the estimated orientation of EDT's shape after using the range-only motion compensation that uses both range and Doppler information. Moreover, the authors applied the k-space algorithm to generate ISAR image. Simulation results demonstrate this remarkable performance.

Robust ISAR Target Recognition Based on ADRISAR-Net

Due to the inherent unknown image deformation among the training and test samples, performance of the deep convolutional neural network (CNN) will be degraded for Inverse Synthetic Aperture Radar (ISAR) automatic target recognition. Meanwhile, traditional CNN only captures the local spatial information due to small receptive fields, thus, neglects the global information useful for recognition. To tackle these issues, this article proposes the attention-augmented deformation robust ISAR image recognition network, dubbed as ADRISAR-Net. The model adopts the inverse compositional spatial transformer for automatic image deformation adjustment, and performs joint local and global feature extractions by the attention-augmented CNN. Finally, the softmax classifier outputs the recognition results. The proposed ADRISAR-Net is end-to-end trainable, and achieves higher recognition accuracy for the four-satellite and three-airplane ISAR image data sets generated by electromagnetic computing.

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.

ISAR Imaging Method for High-speed Maneuvering Target Based on MFT-UNET3+ Network

When inverse synthetic aperture radar (ISAR) images a high-speed maneuvering target, the imaging results of traditional methods are blurred and defocused in the range and azimuth directions because of the high speed and acceleration of the target. The existing imaging methods of maneuvering targets have poor imaging effects or low efficiency, and can’t eliminate the migration through range cells (MTRC) of far points on the target. Given that the existing methods can’t meet the imaging requirements of high-speed maneuvering targets, a new method by combing the UNET3+ convolution neural network (CNN) to machting Fourier transform (MFT) imaging process is proposed in this paper. Overcoming the limitations of the existing imaging methods, the proposed method can realize fast and accurate imaging for high-speed maneuvering targets. Experiments show that the novel MFT-UNET3+ method can achieve a good focus even under the condition of a low signal-to-noise ratio and down-sampling.

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.

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.

Field Trial of a Coherent, Widely Distributed, Dual-Band Photonics-Based MIMO Radar With ISAR Imaging Capabilities

Soon after its introduction in the communications domain, the novel concept of multiple input–multiple output (MIMO) has been making its way also into the radar world. A new generation of multistatic netted systems with unprecedented capabilities has been fully theorised, unveiling its potential advantages over its classical stand-alone, monostatic counterparts. However, MIMO radars mainly remained the object of abstract modeling, since electronic technology could hardly support the practical implementation of this new class of systems. With the development of microwave photonics, the realization of MIMO radar networks at the best of their capabilities has become a reality, thanks to the inherent coherence of photonics systems, and to the broad-band, low-distortion, and interference-immune optical signal distribution. This paper presents the results of a microwave photonics widely-distributed, dual-band MIMO radar network, deployed in a real freight port for maritime traffic monitoring, with inverse synthetic aperture radar imaging capabilities. It employs a central unit connected to multiple widely distributed remote radar peripherals thanks to optical fiber. The possibility to operate in dual-band mode, and the coherent management of the transmitted and received signals are demonstrated, exploiting geometric and frequency diversity in target detection. The system exhibits a spurious-free dynamic range of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\sim 82}\,{\mathbf{d}\mathbf{B}\cdot \mathbf{\mathbf{Hz}}^{\mathbf{2/3}}}$</tex-math></inline-formula> and a sensitivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf{-110}\,\mathbf{dBm}$</tex-math></inline-formula> . Under these premises, a small boat of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf \sim\! 1\,{\mathrm{\mathrm{m}}^{\mathrm{2}}}$</tex-math></inline-formula> radar cross section has been detected at more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {800}\,{\mathbf{m}}$</tex-math></inline-formula> , achieving a probability of detection of about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {0.85}$</tex-math></inline-formula> , for a false alarm rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {7.7\times 10^{-4}}$</tex-math></inline-formula> .

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.

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 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.