Inverse Synthetic Aperture Radar Data Research Articles

Few‐shot learning for satellite characterisation from synthetic inverse synthetic aperture radar images

AbstractSpace situational awareness systems primarily focus on detecting and tracking space objects, providing crucial positional data. However, understanding the complex space domain requires characterising satellites, often involving estimation of bus and solar panel sizes. While inverse synthetic aperture radar allows satellite visualisation, developing deep learning models for substructure segmentation in inverse synthetic aperture radar images is challenging due to the high costs and hardware requirements. The authors present a framework addressing the scarcity of inverse synthetic aperture radar data through synthetic training data. The authors approach utilises a few‐shot domain adaptation technique, leveraging thousands of rapidly simulated low‐fidelity inverse synthetic aperture radar images and a small set of inverse synthetic aperture radar images from the target domain. The authors validate their framework by simulating a real‐case scenario, fine‐tuning a deep learning‐based segmentation model using four inverse synthetic aperture radar images generated through the backprojection algorithm from simulated raw radar data (simulated at the analogue‐to‐digital converter level) as the target domain. The authors results demonstrate the effectiveness of the proposed framework, significantly improving inverse synthetic aperture radar image segmentation across diverse domains. This enhancement enables accurate characterisation of satellite bus and solar panel sizes as well as their orientation, even when the images are sourced from different domains.

MVFRnet: A Novel High-Accuracy Network for ISAR Air-Target Recognition via Multi-View Fusion

Inverse Synthetic Aperture Radar (ISAR) is a promising technique for air target imaging and recognition. However, the traditional monostatic ISAR only can provide partial features of the observed target, which is a challenge for high-accuracy recognition. In this paper, to improve the recognition accuracy of air targets, we propose a novel recognition network based on multi-view ISAR imaging and fusion, called Multi-View Fusion Recognition network (MVFRnet). The main structure of MVFRnet consists of two components, the image fusion module and the target recognition module. The fusion module is used for multi-view ISAR data and image preprocessing and mainly performs imaging spatial match, image registration, and weighted fusion. The recognition network consists of the Skip Connect Unit and the Gated Channel Transformation (GCT) attention module, where the Skip Connect Unit ensures the extraction of global depth features of the image and the attention module enhances the perception of shallow contour features of the image. In addition, MVFRnet has a strong perception of image details and suppresses the effect of noise. Finally, simulated and real data are used to verify the effectiveness of the proposed scheme. Multi-view ISAR echoes of six types of aircraft are produced by electromagnetic simulation software. In addition, we also build a millimeter wave ground-based bistatic ISAR experiment system and collect multi-view data from an aircraft model. The simulation and experiment results demonstrate that the proposed scheme can obtain a higher recognition accuracy compared to other state-of-the-art methods. The recognition accuracy can be improved by approximately 30% compared with traditional monostatic recognition.

Low-Rank and Patch-Based Method for Enhanced Sparse ISAR Imaging

Due to non-ideal conditions in actual applications of inverse synthetic aperture radar (ISAR), some measurements are lost or the received signal is invalid for some time periods. In addition, the received signal is often affected by measurement noise. Hence, it is usually difficult to obtain well-focused images for such sparse aperture ISAR data. To solve this issue, a novel low-rank and patch-based sparse ISAR imaging method called LRPB is proposed in this paper. In LRPB, the low-rank property and structure similarity of ISAR image in 3D space are explored to ensure high-quality image reconstruction. Simultaneously, the noise is also considered in the constraint to achieve better performance. Furthermore, a Lagrange multiplier-based technique is developed to tackle the optimization problem of ISAR imaging by combining the advantages of alternating direction multiplier method. The experiment results of simulation data and real measured data verify the effectiveness of the proposed method, especially at low SNR and small number of pulses.

A Fast ISAR Imaging Method Based on Strategy Weighted CAMP Algorithm

Complex approximate message passing (CAMP) algorithm is a promising compressed sensing (CS) method for inverse synthetic aperture radar (ISAR) imaging with the merits of fast convergence and good reconstruction. To improve the imaging performance and accelerate the convergence, a new strategy weighted CAMP based ISAR imaging method has been proposed. Rather than solving a sequence of weighted <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> minimization problems, the weighted CAMP problem is solved directly by using a new weighted threshold, which not only ensures the convergence of the algorithm but also improves the convergence speed. To accelerate the convergence furtherly, a new point is chosen smartly in the scalar threshold function, which borrows the idea of fast iterative shrinkage-thresholding algorithm (FISTA). As weighting adaptively to the signal and the noise is more appropriate to maintain the target information and suppress the noise, random forests (RF) algorithm is employed to classify the signal and the noise, and strategy weighting is used based on the classification result. The proposed algorithm was applied into complex sinusoids, quasi-real, and real ISAR data. It has got the best imaging result among the algorithms of CAMP class and need less imaging time than CAMP and weighted CAMP (WCAMP). Therefore, the proposed algorithm is an efficient ISAR imaging method with good performance.

Optimal Time Selection for ISAR Imaging of Ship Targets Based on Time-Frequency Analysis of Multiple Scatterers

The equivalent rotation vector synthesized by 3-D nonuniform rotation of ship target leads to the time-varying characteristic of Doppler frequency of ship echo data, which will cause the image to be defocused in azimuth. In order to obtain high-resolution inverse synthetic aperture radar (ISAR) images of ship targets, we developed an optimal time selection algorithm based on time-frequency analysis of multiple scatterers. In this letter, we addressed the challenges of the serious cross-term interference in time-frequency analysis and measuring the stability of Doppler frequency. The time interval with a minimum variation of Doppler frequency was determined by using time-frequency analysis of multiple isolated scatterers and root mean squared error (RMSE). The effectiveness and robustness of the proposed algorithm were evaluated by both simulated and real ISAR data.

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.

Sparse Inverse Synthetic Aperture Radar Imaging Using Structured Low-Rank Method

There has been an increasing interest in addressing the issue of high-resolution inverse synthetic aperture radar (ISAR) imaging from sparse sampling data. Traditional compressed sensing (CS) and matrix completion (MC) methods are based on sparse and low-rank constraints, respectively, which do not make full use of the structure of ISAR data. In this article, a sparse ISAR imaging algorithm using a structured low-rank approach is proposed for enhanced imaging performance. Based on the observation that the structured Hankel matrix has better low-rank property, the proposed algorithm can outperform the group of conventional MC methods in terms of accuracy to data quality and quantity. Rather than using the traditional singular value decomposition (SVD) solution of nuclear norm minimization, the proposed algorithm restates the nuclear norm via an equivalent reformulation that the structured Hankel matrix can be decomposed into two disjointed parts to avoid the dimensional expansion of the Hankel matrix. Meanwhile, the alternative direction method of multipliers (ADMMs) is applied to effectively reduce the computational complexity. Finally, the effectiveness of the proposed algorithm is further validated using the experiments on simulated and measured data.

An ISAR Imaging Method Based on Improved CAMP Algorithm

Compressed sensing (CS) provides a new idea for inverse synthetic aperture radar (ISAR) imaging. Complex approximate message passing (CAMP) algorithm, as a new CS reconstruction algorithm, has characteristics of fast convergence and good reconstruction, which is suitable for ISAR imaging. However, the off-grid problem is inevitable in ISAR imaging, which affects the reconstruction performance seriously and needs to be solved urgently. To solve the off-grid problem, a parametric CS ISAR imaging model is built in this paper, in which the off-grid is considered as an unknown parameter. To solve the joint optimization problem efficiently, first-order Taylor series approximation is used and the optimization problem is transformed into two least squares problems. To verify the effectiveness of the proposed method, it is applied into random sparse signals, complex sinusoids, quasi-real ISAR data and real ISAR data. Compared with the existing algorithms, the proposed method has got higher reconstruction signal-to-noise ratio (RSNR), smaller relative reconstruction error, smaller frequency position error and better imaging result. Therefore the proposed method is an effective CS ISAR imaging method which can solve the off-grid problem well.

Translational Motion Compensation for ISAR Images Through a Multicriteria Decision Using Surrogate-Based Optimization

Inverse synthetic aperture radar (ISAR) image is constructed using 2-D spatial distributions of the radar cross section of a target. ISAR data gathered from moving targets might include interphase error that causes a blurry effect in the ISAR image. In this article, an efficient motion compensation (MC) scheme depending on multicriteria decision using surrogate-based optimization (SbO) for minimizing the entropy and maximizing the sharpness of the images is proposed to remove the blur from the images. In order to provide a multicriteria decision, Pareto optimality is exploited to balance two criteria of the entropy and sharpness synchronously. A signal with an interphase error is input to the MC system for determining the global optimal motion parameters of the velocity and acceleration so as to focus on the ISAR image. The proposed scheme is implemented to four simulated ISAR scenarios reported elsewhere through two aircraft models for performance demonstration and comparison with artificial bee colony (ABC), differential evolution (DE), and particle swarm optimization (PSO) implemented in the literature. It is pointed out that the proposed scheme is more successful and efficient in view of the image focusing quality as well as the numerical results such as the motion parameters, the entropy and sharpness, and structural similarity (SSIM) index. The results also show that the SbO outperforms the other methods as the velocity and acceleration increase.

Integrating deep learning-based data driven and model-based approaches for inverse synthetic aperture radar target recognition

We explore the blending of model-based and deep learning approaches for target recognition in inverse synthetic aperture radar (ISAR) imagery. It evaluates five different approaches, namely, a model-based geometric hashing approach, a supervised deep learning approach, and three different blending models that fuse the model-based and deep learning approaches. The model-based approach extracts scattering centers as features and requires domain experts to identify and characterize important features of a target, which makes the training process very costly and hard to generalize when the image quality degrades in low signal-to-interference-plus-noise-ratio conditions. Next, a deep learning algorithm using a convolutional neural network is considered to extract the spatial features when raw ISAR data are used as input. This approach does not need an expert and only requires the labels of images for training. Finally, three model-based and deep learning approaches are blended together at the feature level and decision level to benefit from the advantages of both approaches, achieving a higher performance. The results show that the blending of the two approaches achieves a high performance while providing explainable inferences. The performance of the five different approaches is evaluated under varying conditions of occlusion, clutter, masking of the target, and adversarial attacks. It is empirically shown that the model-based and deep learning approaches are able to complement each other and can achieve better classification accuracy upon fusing the integrated approach.

Inverse Synthetic Aperture Radar Imaging Using a Fully Convolutional Neural Network

The traditional inverse synthetic aperture radar (ISAR) imaging uses the range-Doppler (RD) type of methods. The compressive sensing (CS)-based ISAR imaging is capable of obtaining good target images of high contrast and less sidelobe with much less downsampling data. However, the real application of CS ISAR imaging is limited by the time-consuming iteration-based image reconstruction. The image quality is also limited by the performance of sparse representation of the target scene. In recent years, deep learning methods, more specifically the convolutional neural network (CNN), has shown its capability in signal recovery with downsampling or noncomplete data. The well-trained CNN can extract high-level abstract feature representation from the input data autonomously and exploit it in the signal recovery. We are interested in exploiting the CNN to enhance the CS ISAR imaging capability. The successful training of CNN always requires many thousand annotated training samples. This limits the application of CNN to the radar imaging field where large amount of training data cannot be obtained as easy as in other fields, e.g., computer vision. We propose a fully CNN (FCNN) for ISAR imaging. The constructed FCNN has a multistage decomposition and multichannel filtering architecture and has no fully connected layers. It can work with very few training samples as compared to existing CNN-based imaging networks. The imaging results of real ISAR data show that the proposed FCNN-based ISAR imaging method outperforms the state-of-the-art CS ISAR imaging methods in both image quality and computational efficiency.

Collaborative Compressive Radar Imaging With Saliency Priors

Although several works have been done on high-resolution inverse synthetic aperture radar (ISAR) imaging via compressive sampling technology, they only explore sparse priors of targets in the scene and the image is recovered cell by cell separately. In order to potentially enhance targets and suppress clutters for fast and higher quality imaging, in this paper, we advance a collaborative compressive ISAR (CC-ISAR) imaging approach, by exploring both sparse priors and saliency priors of targets. First, a geometric saliency map is derived by performing pulse contourlet transform on a preliminary image. Then, a graph Laplacian is constructed to regularize a multiple measurement vector problem for collaborative compressive radar imaging. Third, targets are approximately separated from the background in the saliency map, and salient weights are defined for the target and background, respectively, to derive a saliency weighted $l_{1}$ -norm optimization algorithm. Some experiments are taken on real ISAR data to evaluate the performance of the proposed method, and both visual results and numerical guidelines prove that CC-ISAR imaging method can obtain more accurate targets and outperform its counterparts.

A Fast and Accurate Compressed Sensing Reconstruction Algorithm for ISAR Imaging

Compressed sensing (CS) has provided a novel way for inverse synthetic aperture radar (ISAR) imaging. In CS based ISAR imaging, the continuous range-Doppler plane is divided into grids, and the strong scattering points are assumed on the grids. However, the strong scattering points may not be on the grids, which will degrade the performance of CS greatly. This is the off-grid problem. To solve the problem, most of existing methods aim at estimating off-grid error and sparse solution jointly. But the computational cost is relatively high. To reduce the computational cost, a fast and accurate algorithm has been proposed in this paper. Interestingly, the joint optimization problem can be solved efficiently through two least squares problems based on first order Taylor approximation. When applied into simulated chirp signal and quasi real ISAR data, the proposed algorithm has got much better imaging results than existing algorithms. Therefore, it is a promising off-grid CS based ISAR imaging algorithm.

ISAR Image Reconstruction with Heavily Corrupted Data Based on Normal Inverse Gaussian Model

Inverse synthetic aperture radar (ISAR) is a potent radar system which generates two-dimensional signal on the range-Doppler domain by using target’s motion. ISAR imaging of targets is an important tool for automatic target recognition and classification in the defense and aerospace industry. In this paper, we focus upon the problem of ISAR imaging at low signal to noise ratio (SNR). Nonsubsampled directional filter bank (NSDFB) is a very useful tool in studying the directional features in two-dimensional signals. This paper offers a novel ISAR imaging approach by using NSDFB coefficients modeling. Bayesian maximum a posteriori is used where normal inverse Gaussian model is presumed for estimating ISAR image at low SNR. We applied NSDFB transform to ISAR image and implemented procedure to describe the characteristics of the algorithm. Both simulated and real ISAR data have been tested. The proposed technique keeps a balance between feature preservation and noise suppression. Finally, experimental results show that the offered technique outperforms other in terms of visual assessment and image evaluation parameters.

Inverse synthetic aperture radar image reconstruction with heavily corrupted data based on heavy-tailed Lévy model

Inverse synthetic aperture radar (ISAR) is a powerful radar-processing technique that uses target’s motion to generate images on the range-Doppler plane. In the defense industry, ISAR imaging of moving targets is an important tool for automatic target recognition. We focus on the problem of ISAR imaging at low signal-to-noise ratio (SNR). The nonsubsampled directional filter bank (NSDFB) is a very useful tool in analyzing the directional information in two-dimensional signals. This paper presents an ISAR Imaging algorithm using NSDFB coefficients modeling. Bayesian maximum a posteriori is used where the heavy-tailed Levy model is assumed for estimating an ISAR image at low SNR. We applied NSDFB transform to the ISAR image and developed a simulation procedure to describe the characteristics of the algorithm. Both simulated and real ISAR data have been tested. The proposed algorithm maintains a balance among noise suppression, feature preservation, and computational time. Finally, the experiments show that the proposed method outperforms others in terms of visual evaluation and image assessment parameters.

Fast off grid compressed sensing ISAR imaging algorithm

Abstract To solve the off grid problem in compressed sensing (CS) based inverse synthetic aperture radar (ISAR) imaging, a fast and accurate algorithm has been proposed in the paper. By jointly estimating the off grid error and the sparse solution, off grid ISAR imaging is transformed into a joint optimization problem. Interestingly, it can be solved efficiently through two least squares problems based on first order Taylor approximation. When applied to complex sinusoids and quasi real ISAR data, the proposed algorithm has got better results than the conventional algorithm. Therefore, it is a promising off grid CS based ISAR imaging algorithm.

ISAR Image Resolution Enhancement: Compressive Sensing Versus State-of-the-Art Super-Resolution Techniques

The applicability of compressive sensing (CS) to inverse synthetic aperture radar (ISAR) imagery has been widely discussed over the past few years. In particular, CS-based ISAR image-reconstruction algorithms have been developed and their effectiveness proven when dealing with incomplete ISAR data. Resolution enhancement has also been identified as a case for which CS can be effectively applied to ISAR imagery. In this case, the acquired signal can be interpreted as incomplete data in the frequency/slow-time domain and CS used to reconstruct the super-resolved ISAR image. In this paper, an exhaustive performance analysis is carried out along with a comparison between CS and conventional super-resolution techniques. Several concepts and methods have been introduced in order to effectively define the performance, which is not simply based on visual inspection.

Robust Reading Approach for Moving Chipless RFID Tags by Using ISAR Processing

A robust technique for reading moving chipless tags exploiting the inverse synthetic aperture radar data processing is presented. The procedure takes advantage from the known tag movement to improve the tag detection capability by integrating several received signals. The implicit advantage of the proposed detection scheme is that the background measurement, collected in the absence of the tag, can be easily performed before the tag passes in front of the reader antenna. The reading procedure allows for a significant increase of the interrogation distance and, more importantly, it greatly improves the detection probability in complex scenarios, where the tag is attached on arbitrary and highly scattering objects. A simplified model is used to demonstrate the capabilities of the presented procedure in detecting tags even in the presence of high clutter. Detection of tags with signal-to-noise ratio as low as −10 dB is experimentally demonstrated.

Generalised pareto distribution‐based Bayesian compressed sensing inverse synthetic aperture radar imaging

The widely used distributions in Bayesian compressed sensing (BCS) inverse synthetic aperture radar (ISAR) imaging do not support stable linear dimensionality reduction, and the compressibility of the recently proposed distributions is hard to be proved theoretically. However, generalised pareto distribution (GPD) can overcome these shortcomings. So a GPD-based ISAR imaging method has been proposed. Interestingly, shape parameter can be set to a small value directly based on mathematical proof in one kind of GPD-Meridian distribution, which is totally different from the existing methods. It not only simplifies the method but also improves the sparsity of the solution. It has been proved that the proposed method now is equal to the log-sum minimisation, which verifies the correctness of the parameter selecting algorithm. Compared with the conventional methods, the new method can recover signals with fewer measurements or in looser sparsity conditions or with smaller recovery errors. When applied into simulated and real ISAR data imaging, the proposed method has obtained better images than the conventional methods. Therefore, the proposed method is a promising imaging method in BCS ISAR imaging.

A Radar Imaging Method Using Nonperiodic Interrupted Sampling Linear Frequency Modulation Signal

Linear frequency modulation (LFM) signal is widely applied in imaging radar including synthetic aperture radar (SAR) and inverse SAR (ISAR). Jamming techniques against LFM imaging radar develop rapidly in the past decades, which make radar imaging with LFM signal to face a great challenge. In this paper, a novel radar imaging method using nonperiodic interrupted sampling-LFM (NIS-LFM) signal is proposed. The radar anti-jamming performance can greatly benefit due to the random property of the NIS-LFM signal. Besides, the compressive sensing-based algorithm is further proposed for radar image reconstruction by utilizing the piecewise sparsity of the NIS-LFM echo. Both measured ISAR data simulations and microwave anechoic chamber experiments demonstrate the validity of the proposed imaging method.