Sparse Inverse Synthetic Aperture Radar Imaging Research Articles

Joint 2-D Sparse ISAR Imaging and Autofocusing by Using 2-D-IADIANet

Compressive sensing (CS) based methods have been widely used for sparse inverse synthetic aperture radar (ISAR) imaging. However, many CS-based methods are sensitive to the selection of model parameters, and the residual phase error of the echo also causes trouble for imaging and autofocusing. To address these problems, a novel deep learning approach, named as 2D-IADIANet, is proposed to achieve 2D sparse ISAR imaging with 2D phase error estimation in this paper. First, a 2D ISAR sparse echo model with 2D phase error into account is established, and a 2D-ADMM frame-work-based method, dubbed as 2D-IADIA, is presented to solve this compound reconstruction problem. Second, 2D-IADIA is further unfolded and mapped into a deep network form by integrating with 2D phase error compensation network. Moreover, all adjustable parameters can be learned adaptively by training the network through back propagation algorithm in complex domain directly. At last, experiment results verify that the well-learned 2D-IADIANet, which is only trained by a small amount of simulation samples, can also be generalized to measured data application. Especially, owing to the good performance of the network, the proposal has a superior reconstruction performance than 2D-IADIA under the low 2D sample rate and/or signal-to-noise ratio scenarios.

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 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.

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.

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.

Fully Coherent Integration and Measurement of Optimized Frequency Agile Waveform for Weak Target High-Resolution ISAR Imaging

Frequency agile waveform effectively decreases interception probability and increases the anti-jamming ability for radar probing in the electromagnetic countermeasure environment. However, the transmitting agile frequency introduces jitter phases causing de-coherence of echoes, which decreases signal-to-noise ratio (SNR) accumulation gain and leads to difficult motion estimation. Therefore, stable target detection, motion parameters estimation, and inverse synthetic aperture radar (ISAR) imaging for frequency-agile radar are still intractable problems in the low SNR environment. Aiming at these problems, we proposed a fully coherent processing method for weak target detection and ISAR imaging for frequency agile waveform in this paper. The agile waveform with low range and Doppler side lobes is designed to improve motion parameters estimation and ISAR imaging performance. Then, the joint jitter phases and motion parameters estimation based on the generalized likelihood ratio test (GLRT) is proposed to realize robust target detection and motion parameters estimation in low SNR conditions. The translational motion compensation and sparse ISAR imaging methods are also presented based on the detection and motion parameters estimation results. Finally, both simulated and real-measured data are used to verify the remarkable detection, motion parameters estimation, and ISAR imaging performance compared with the traditional non-coherent accumulation and mixture of coherent and non-coherent accumulation methods.

Enhanced sparse ISAR imaging by jointly using sparsity and low-rank properties

The sparsity characteristic, which is the foundation of the compressive sensing (CS) theory, has been widely used in the inverse synthetic aperture radar (ISAR) imaging field. However, the performance of the traditional CS-based sparse aperture ISAR imaging method is limited in low signal to noise ratio (SNR) and/or sampling rate (SPR). Inspired by the low-rank property of the final ISAR image, a new sparse aperture ISAR imaging model by jointly using the sparse characteristic and low-rank property is proposed in this paper. Considering the effective solution of the proposed reconstruction model, the optimization problem is decomposed into several sub-problems under the framework of linearized alternating direction method with adaptive penalty (LADMAP) approach. Furthermore, in order to improve the computing efficiency of the proposed algorithm, a fast singular value decomposition (SVD) free algorithm is finally proposed. Both simulated and measured data experiments verify the effectiveness of the proposed algorithms, especially under the condition of low SNR and/or SPR.

A gridless sparse ISAR imaging method using atomic norm minimization based on multiple measurement vectors

ABSTRACT The atomic norm minimization (ANM) based gridless recovery approaches can completely obviate the grid mismatch problem of discrete compressed sensing methods by working directly on a continuous dictionary, which have attracted considerable interest in sparse inverse synthetic aperture radar (ISAR) imaging. In order to exploit the joint sparsity in the multiple measurement vectors (MMV), a MMV-ANM approach for sparse ISAR imaging with stepped frequency signal is proposed in this paper. By reformulating the sparse range frequency echoes into a MMV-ANM model, the expected full echo without off-grid can be recovered at a single step by solving a semidefinite programme (SDP). Finally, the further improved ISAR imaging results can be achieved via the standard fast Fourier transform methods. The real data experiments demonstrate performance of the proposed method compared to existing gridless approaches.

Progress in radar imaging for maneuvering targets

Radar imaging is an essential technique to acquire fine structural characteristics of a target. For a maneuvering target, however, the resolution of the traditional range-Doppler-based inverse synthetic-aperture radar (ISAR) imaging decreases on both the range and azimuth directions. Along the range direction, the resolution is determined by the bandwidth of the radar, which is limited by the hardware of the radar system. In addition, the high velocity of the target can result in a stretch of the range profile, thus decreasing the resolution. Along the azimuth direction, the complex target motion produces a time-varying nonlinear Doppler frequency, which destroys the imaging conditions for ISAR and causes a reduction of the resolution. In this paper, the effects of the target complex motion on ISAR imaging are firstly presented. The process of ISAR imaging for maneuvering targets is then elaborated from the dimensions of the range and Doppler. In this regard, high-velocity compensation, multiband fusion imaging, high-order phase-error compensation, and sparse ISAR imaging are mainly presented. Several techniques of radar imaging for maneuvering targets, which could overcome some shortcomings of ISAR imaging, are then discussed, including the wave front modulation and electromagnetic vortex imaging. Finally, the current works on radar imaging for maneuvering targets are summarized, and future perspectives are discussed.

A Cooperative Task Allocation Game for Multi-Target Imaging in Radar Networks

In radar networks, research on the task allocation strategy for multi-target imaging is essential, especially under limited radar resources. From the targets' perspective, the goal is to acquire enough resources to obtain high-quality inverse synthetic aperture radar (ISAR) images. Then there is competition among targets for radar resources. Therefore, the task allocation optimization problem is considered as a cooperative game, specifically a non-transferable coalitional game when the sparse ISAR imaging algorithm is adopted. A stable coalition structure, which allows cooperation among targets to achieve satisfactory image resolution using the least amount of time, represents an optimal task allocation scheme. Then, the corresponding algorithm based on coalition-forming rules is proposed. Through the framework of cooperative game theory, a stable network structure (i.e., the task allocation strategy) is formed while minimizing the radar time resource cost. Finally, experiments confirm that the proposed algorithm can achieve an optimal task allocation strategy with a lower computation time cost.

A RPCA-Based ISAR Imaging Method for Micromotion Targets.

Micro-Doppler generated by the micromotion of a target contaminates the inverse synthetic aperture radar (ISAR) image heavily. To acquire a clear ISAR image, removing the Micro-Doppler is an indispensable task. By exploiting the sparsity of the ISAR image and the low-rank of Micro-Doppler signal in the Range-Doppler (RD) domain, a novel Micro-Doppler removal method based on the robust principal component analysis (RPCA) framework is proposed. We formulate the model of sparse ISAR imaging for micromotion target in the framework of RPCA. Then, the imaging problem is decomposed into iterations between the sub-problem of sparse imaging and Micro-Doppler extraction. The alternative direction method of multipliers (ADMM) approach is utilized to seek for the solution of each sub-problem. Furthermore, to improve the computational efficiency and numerical robustness in the Micro-Doppler extraction, an SVD-free method is presented to further lessen the calculative burden. Experimental results with simulated data validate the effectiveness of the proposed method.

Time and Aperture Resource Allocation Strategy for Multitarget ISAR Imaging in a Radar Network

In modern high-dynamic battlefield environments, the time and aperture resources of multifunction radar systems are often viewed as important system resources. In this paper, a time and aperture resource allocation strategy for multitarget inverse synthetic aperture radar (ISAR) imaging that incorporates requirements for distributed network radar multitarget imaging tasks is proposed to improve the efficiency of a radar system. First, the resolution requirement of the target is determined based on the feature recognition of the target, and an allocation model of the aperture resource for the distributed networking conditions is established by analyzing the relationship between the resolution and the aperture resource. Second, by analyzing the requirements of target sparse ISAR imaging, time resources are incorporated into the resource allocation model. Third, a multiobjective function model for the multitarget imaging of networked radars with the least resource consumption and the largest number of imaging tasks is established. Finally, the objective function is sorted according to the degree of importance by a lexicographic strategy, and a lexicographic genetic algorithm is proposed to solve the optimization problem. The simulation results show that the proposed algorithm outperforms the traditional algorithm in terms of the effective utilization of radar resources and the radar system performance.

Three-Dimensional Reconstruction From a Multiview Sequence of Sparse ISAR Imaging of a Space Target

To monitor a space target, 3-D reconstruction from a multiview sequence of the inverse synthetic aperture radar (ISAR) imaging is developed. Scattering of a complex electric-large target, e.g., the ENVISAT satellite model, is numerically calculated, and multiview 2-D ISAR imaging can be simulated. Under the sparse sampling ISAR imaging via compressed sensing, the Kanade-Lucas–Tomasi feature tracker is applied to extraction of target feature points. Then, using the orthographic factorization method, 3-D reconstruction of those feature points is produced. A simple hexagonal frustum is first tested for the feasibility analysis. Two sequences of multiview ISAR imaging, one is the ENVISAT model and another real measurements of a space shuttle, are then presented for 3-D reconstruction. Furthermore, a complex multistructure model of the International Space Station is also studied from multiview ISAR imaging under different sparse sampling rates. All results demonstrate good feasibility of the 3-D reconstruction for those target components, e.g., solar panel and antenna.

Maneuvering target imaging and scaling by using sparse inverse synthetic aperture

In recent years, there have been increasing interests in addressing the issue of high-resolution inverse synthetic aperture radar (ISAR) imaging from sparse aperture (SA) data. The non-uniform rotation property of maneuvering target introduces non-stationary echo modulation to exhibit two-dimensional (2-D) migration through resolution cells (MTRC), which increases the difficulty of SA-ISAR imaging. In this paper, we focus on ISAR imaging and scaling of maneuvering target for effective MTRC correction and SA synthesis. Under a scaled non-uniform Fourier dictionary to include MTRC, SA maneuvering target imaging is formatted as sparse representation with maximum a posterior (MAP) estimation. Then, sparse ISAR imaging joint with parameter estimation is solved by using a two-step procedure. The estimated rational parameters are used to rescale the reconstructed ISAR image to extract the 2-D target geometry. Finally, the experiments based on simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.