Well-focused Inverse Synthetic Aperture Radar Images Research Articles

Distributed ISAR imaging based on convolution and total variation reweighted l1 regularization

ABSTRACT Distributed Inverse Synthetic Aperture Radar (ISAR) systems employ multiple spatially separated radars to observe a target, with the potential to enhance radar imaging azimuth resolution and gather more target information. Due to the existence of gaps in the observation angles of each radars, the coherence between pulses of radar echo is disrupted during fusion imaging, making it challenging for the Range-Doppler (RD) algorithm to achieve well-focused images. This paper proposes an imaging method based on sparse signal recovery (SSR), using the reweighted l 1 norm with convolution and total variation as regularization terms. This approach not only yields well-focused ISAR images but also better preserves the overall target information compared to other methods, while also exhibiting excellent noise resistance. In addition, the alternating direction method of multipliers (ADMM) algorithm is utilized for the solution to reduce computational complexity. Simulation and measured data verify the effectiveness of the proposed method.

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.

Noise Robust High-Speed Motion Compensation for ISAR Imaging Based on Parametric Minimum Entropy Optimization

When a target is moving at high-speed, its high-resolution range profile (HRRP) will be stretched by the high-order phase error caused by the high velocity. In this case, the inverse synthetic aperture radar (ISAR) image would be seriously blurred. To obtain a well-focused ISAR image, the phase error induced by target velocity should be compensated. This article exploits the variation continuity of a high-speed moving target’s velocity and proposes a noise-robust high-speed motion compensation algorithm for ISAR imaging. The target’s velocity within a coherent processing interval (CPI) is modeled as a high-order polynomial based on which a parametric high-speed motion compensation signal model is developed. The entropy of the ISAR image after high-speed motion compensation is treated as an evaluation metric, and a parametric minimum entropy optimization model is established to estimate the velocity and compensate it simultaneously. A gradient-based solver of this optimization is then adopted to iteratively find the optimal solution. Finally, the high-order phase error caused by the target’s high-speed motion can be iteratively compensated, and a well-focused ISAR image can be obtained. Extensive simulation experiments have verified the noise robustness and effectiveness of the proposed algorithm.

A Novel ISAR Imaging Approach for Maneuvering Targets With Satellite-Borne Platform

Inverse synthetic aperture radar (ISAR) imaging for maneuvering targets has always been a challenging task due to azimuth time-varying Doppler frequency modulation, especially under moving platform condition. In this case, the common assumption that the image projection plane (IPP) of the radar line-of-sight (LOS) direction is constant during coherent processing interval (CPI) is invalid. To address this issue, a novel ISAR imaging approach for maneuvering targets is proposed by exploiting nonstationary IPP in this article. First, considering time-varying LOS direction, the new geometric and signal models are developed, where 2-D spatial-variant phase error is mainly deduced. After that, a parametric image entropy minimum optimization combined with efficient particle swarm optimization (PSO) is used to obtain optimal motion parameters. In doing so, 2-D spatial-variant phase error terms are compensated accurately to produce well-focused ISAR image. Finally, the effectiveness and superiority of the proposed algorithm are verified by the simulation results and electromagnetic scattering data.

A Novel ISAR Imaging and Scaling Approach for Maneuvering Targets Based on High-Accuracy Phase Parameter Estimation Algorithm

For inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, the Doppler frequency induced by an arbitrary scatterer on the target is time-varying, and hence, the traditional range-Doppler (RD) algorithm is not appropriate for obtaining focusing ISAR images. In such a scenario, a novel ISAR technique, called the range instantaneous Doppler derivative (RIDD) algorithm, has been recently proposed, and two different kinds of well-focused ISAR images, named range-Doppler centroid (RDC) frequency image and range-Doppler frequency rate (RDR) image, can be obtained. In order to improve the accuracy of the target classification and recognition, the scaling approach for the RIDD algorithm must be taken into consideration. In this article, the point spread function (PSF) representations and cross-range scaling factors of the RDC image and the RDR image obtained by the RIDD algorithm are demonstrated analytically. On the basis of the PSFs and cross-range scaling factors, a novel imaging and scaling approach for the RIDD algorithm in the presence of a low SNR environment based on smoothed integrated high-resolution time–frequency-rate representation (SIHR) combined with coherent integrated smoothed generalized cubic phase function (CISCF) is proposed. As the proposed algorithm has high estimation accuracy and good antinoise capability, precise parameter estimation of the received signal can be achieved. Hence, high-quality scaled RDC and RDR images of the target can be reconstructed by the proposed approach. Experimental results of simulated data and real measured data verify the correctness of the PSF representations and demonstrate the effectiveness of the imaging and scaling approach proposed in this article.

Orbital-angular-momentum-based super-resolution ISAR imaging for maneuvering targets: Modeling and performance analysis

Vortex electromagnetic (EM) waves carrying orbital angular momentum (OAM) wave, whose infinitely helical wavefront and orthogonal eigenvalues of unique physical properties introduce additional rotational degrees of freedom, which has demonstrated superior performance in target detection and azimuthal imaging. In order to enhance the imaging performance for maneuvering targets, this paper offers a solution to develop the vortex inverse synthetic aperture radar (ISAR) technique based on OAM beams design. Firstly, by the space geometry of radar and maneuvering targets, the vortex echo signal model is derived, and the characteristics are analyzed as well. Secondly, pre-processing approach is developed for initial phase compensation by the design of the transmitted OAM beams. Thirdly, the imaging method, based on fractional Fourier transform (FrFT) and Doppler centroid tracking (DCT), is proposed to achieve high-precision range compression and motion compensation to accomplish the range alignment while removing the phases caused by residual azimuth displacements. Finally, after the slow-time pulse compression, the time-invariant Doppler shift is generated, and the well-focused ISAR image can be obtained. The simulation results demonstrate that the proposed vortex ISAR imaging can achieve a higher azimuth resolution than the conventional planar EM ISAR paradigm with the same synthetic aperture length. This discovery provides an approach for OAM-based microwave imaging in a rather efficient manner, which has no harsh requirements for complicated and heavily computational post-processing.

Removal of Micro-Doppler Effect of ISAR Image Based on Laplacian Regularized Nonconvex Low-Rank Representation.

The micro-Doppler (m-D) effect caused by micro-motion degrades the readability of the inverse synthetic aperture radar (ISAR) image. To achieve well-focused ISAR image of the target with the micro-motion part, this paper proposes a novel approach for the removal of m-D effect of ISAR image. Note that the range profiles of the rigid body are similar to each other, making the respective data matrix low-rank. Those of the micro-motion part, in contrary, generally fluctuate in different range cells, whose data matrix is sparse. Therefore, the removal of m-D effect can be naturally solved by the robust principal component analysis (RPCA)-a convenient convex program to decompose an auxiliary matrix into a low-rank matrix and a sparse one. In RPCA, the rank of a matrix is described by the nuclear norm, which is convex but leads to a suboptimal solution. To address it, we utilize a nonconvex surrogate, i.e., the summation of logistic function of the singular values of a matrix, to approximate the rank. Moreover, the range profiles of the rigid body are generally locally similar. To capture this geometric structured information, we further introduce a Laplacian regularization into the model. Then, the Laplacian regularized nonconvex low-rank (LRNL) model is solved efficiently by the linearized alternating direction method (ADM). Extensive experimental results based on both simulated and measured data demonstrate the effectiveness of the proposed approach on the removal of m-D effect of ISAR image.

Rotation Parameters Estimation and Cross-Range Scaling Research for Range Instantaneous Doppler ISAR Images

The maneuverability of targets cannot be neglected in many circumstances for Inverse Synthetic Aperture Radar (ISAR) imaging procedure. For these circumstances, the Range Instantaneous Doppler (RID) algorithm is required to form the well-focused ISAR images. Simultaneously, as the inevitable means of obtaining the target’s cross-range size, the cross-range scaling approach for the RID images also needs to be considered consequently. Since there are few cross-range scaling methods focused on the RID images, we propose a novel method in this paper based on the phase coefficients estimation and scatterers pairing process to solve the problem. Without the special requirements for the scatterers or complicated optimization process, the proposed approach is widely adaptive and efficient. Otherwise, besides the novel estimation method for the rotation parameters, we also propose the novel technique for calculating the cross-range scaling factor of the RID images, which is quite different from that of the Range Doppler (RD) images. Finally, by using the recommended RID imaging algorithm and the proposed cross-range scaling approach, the results of ISAR imaging and cross-range scaling experiments on the simulation and real-data experiment are represented, which demonstrate the effectiveness of the proposed approach in this paper.

A Novel 2-D Geometry Reconstruction Approach for Space Debris via Interpolation-Free Operation under Low SNR Conditions

Two unsolved key issues in inverse synthetic aperture radar (ISAR) imaging for non-cooperative rapidly spinning targets including high computational complexity and poor imaging performance in the case of low signal-to-noise ratio (SNR) are addressed in this work. In the strip-map imaging mode of SAR, it is well known that azimuth spatial invariant characteristics exist, and inspired by this, we propose a fast ISAR imaging approach for spinning targets. Our approach involves two steps. First, a precise analytic expression in the range-Doppler (RD) domain is produced using the principle of stationary phase (POSP). Second, a novel interpolation kernel function is designed to remove two-dimensional (2-D) spatial-variant phase errors, and the corresponding fast implementation steps that only require Fourier transform and multiplications are also presented. Finally, a well-focused ISAR image is obtained by compensating the azimuth high-order terms. Compared with current imaging methods, our approach avoids multi-dimensional search and interpolation operations and exploits the 2-D coherent integrated gain; the proposed method is of low computational cost and robustness in the low SNR condition. The effectiveness of the proposed approach is confirmed by numerically simulated experiments.

Computationally Efficient Sparse Aperture ISAR Autofocusing and Imaging Based on Fast ADMM

In the case of sparse aperture, the coherence between pulses of radar echo is destroyed, which challenges inverse synthetic aperture radar (ISAR) autofocusing and imaging. Mathematically, reconstructing the ISAR image from the sparse aperture radar echo is a linear underdetermined inverse problem, which, by nature, can be solved by the fast developed compressive sensing (CS) or sparse signal recovery theory. However, the CS-based sparse aperture ISAR imaging algorithms are generally computationally heavy, which becomes the bottleneck of preventing their applications to the real-time ISAR imaging system. In this article, we propose a novel and computationally efficient ISAR autofocusing and imaging algorithm for sparse aperture. We first consider a generalized CS model for ISAR imaging and autofocusing with sparse and entropy-minimization regularizations, and then utilize the alternating direction method of multipliers (ADMM) algorithm to optimize the model. To improve computational efficiency, the matrix inversion is translated to an elementwise division with the usage of a partial Fourier dictionary, and the 2-D ISAR image is updated as a whole instead of range cellwise. To achieve autofocusing for sparse aperture, the phase error is estimated by minimizing the entropy of the ISAR image reconstructed in each iterative loop. Experiments based on both simulated and measured data validate that the proposed algorithm can achieve well-focused ISAR images within a few seconds, which is ten times faster than the reported sparse aperture ISAR imaging algorithms.

Noise suppression method for geosynchronous satellite-based passive inverse synthetic aperture radar imaging under low signal-to-noise ratio conditions

In recent years, the geosynchronous satellite-based passive inverse synthetic aperture radar (ISAR) has been widely employed for target detection and automatic target recognition. However, the great distance between the satellite and the receiver causes low signal-to-noise ratio (SNR) of the target signal, which makes the system unable to obtain a well-focused image of the target. To obtain a well-focused ISAR image within a limited coherent processing interval, a method for improving the SNR is required. We propose a two-step noise suppression method to enhance the SNR by eliminating the noise components contained in the received data. The target windowing method is utilized first to reject the noise and clutter interference by windowing the target data from the range-Doppler map. Then, a denoising method is employed to further remove the noise components from the selected target data by threshold techniques. By using the two-step noise suppression, the SNR of the target signal is increased, and consequently a quality-improved ISAR image can be obtained. Simulation results validate the effectiveness of the proposed noise suppression method.

Compressive sensing approach for high‐resolution ISAR image reconstruction and autofocus

In this study, a novel autofocus method is presented to achieve high-resolution inverse synthetic aperture radar (ISAR) image reconstruction with limited measurements in the compressive sensing (CS) framework. In order to solve the CS reconstruction problem of ISAR images with sparsity, the CS coupled with Tikhonov-regularisation-based algorithm is devised as an effective focusing technique. The proposed method aims at compensating the phase errors induced by target motions, and can provide globally well-focused ISAR images. Simulation and experimental results verify the effectiveness of the proposed scheme.

A Novel ISAR Imaging Method for Maneuvering Target Based on AM-QFM Model Under Low SNR Environment

Inverse synthetic aperture radar (ISAR) imaging for long range maneuvering small/stealth target plays an important role in the field of national defense surveillance. Due to low signal-to-noise (SNR) of received echo signal and the nonstationary Doppler frequency modulated (DFM) during the coherent processing intervals (CPI), the conventional range-Doppler (RD) algorithm is unsuitable to generate a well-focused ISAR image. Furthermore, the dihedral or trihedral components of the maneuvering target or range cell migration caused by the target's complex motion generate the time-varying amplitudes for the received signal, which brings a new challenge for ISAR imaging algorithm design. To overcome those issues, in this work, we propose a new method to obtain the well-focused ISAR image of long range maneuvering small/stealth target. This method consists of two main steps. First, the received signal in a range bin is characterized as multicomponent amplitude modulated-quadratic frequency modulated (AM-QFM) signal with time-varying amplitudes after the translational compensation. After that, a novel coherently integrated general cubic phase function (CIGCPF) is developed to estimate the parameters of multi-component AM-QFM signal. Compared with the existing ISAR imaging methods based on the constant amplitude signal model during the CPI, the well-focused ISAR image can be obtained by the proposed method with time-varying amplitude in a low SNR environment. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm.

Wide-Angle, Ultra-Wideband ISAR Imaging of Vehicles and Drones.

In-situ, wide-angle, and ultra-wideband inverse synthetic aperture radar (ISAR) imaging of vehicles and drones is demonstrated using a portable ultra-wideband radar. In order to form well-focused ISAR images, motion compensation is performed before applying the k-space imaging algorithm. While the same basic motion compensation methodology is applied to both types of targets, a more complex motion model is needed to better capture the flight path of the drone. The resulting ISAR images clearly show the geometrical outline of the targets and highlight locations of prominent backscattering. The ISAR images are also assessed against images generated through instrumented targets or laboratory measurements, and the image quality is shown to be comparable.

Optimal Coherent Processing Interval Selection for Aerial Maneuvering Target Imaging Using Tracking Information

When a target is in complex three-dimensional (3-D) motions, inverse synthetic aperture radar (ISAR) images cannot be obtained with any motion compensation algorithms. In this case, selecting optimal coherent processing interval (CPI) (or frame) of the fixed rotational axis is the only way to obtain focused ISAR images. In this article, we focus on selecting optimal CPI for ISAR imaging of maneuvering targets, and a novel method using tracking information is proposed. Without complex computational procedures, the proposed method provides robust performance. The method includes three steps. First, target’s orientation (roll, pitch, and yaw) is estimated by using an extended Kalman filter. Second, the least-square method is adopted to find the best fitted lines of roll, pitch, and yaw angles in different time intervals, and the linearity degrees of the three rotation angles are measured over the imaging intervals. Third, the thresholds of the linearity degrees are set, according to which, optimal CPIs can be selected. The performance of proposed method is demonstrated using both simulated data and measured data. In both cases, optimal CPIs can be selected and well-focused ISAR images can be achieved.

Adaptive ISAR Imaging of Maneuvering Targets Based on a Modified Fourier Transform.

Focusing on the inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, this paper presents a new imaging method which works well when the target’s maneuvering is not too severe. After translational motion compensation, we describe the equivalent rotation of maneuvering targets by two variables—the relative chirp rate of the linear frequency modulated (LFM) signal and the Doppler focus shift. The first variable indicates the target’s motion status, and the second one represents the possible residual error of the translational motion compensation. With them, a modified Fourier transform matrix is constructed and then used for cross-range compression. Consequently, the imaging of maneuvering is converted into a two-dimensional parameter optimization problem in which a stable and clear ISAR image is guaranteed. A gradient descent optimization scheme is employed to obtain the accurate relative chirp rate and Doppler focus shift. Moreover, we designed an efficient and robust initialization process for the gradient descent method, thus, the well-focused ISAR images of maneuvering targets can be achieved adaptively. Human intervention is not needed, and it is quite convenient for practical ISAR imaging systems. Compared to precedent imaging methods, the new method achieves better imaging quality under reasonable computational cost. Simulation results are provided to validate the effectiveness and advantages of the proposed method.

Bayesian Bistatic ISAR Imaging for Targets with Complex Motion under Low SNR Condition.

This paper proposes a novel bistatic inverse synthetic aperture radar (ISAR) imaging algorithm for the target with complex motion under low signal to noise ratio (SNR) condition. Note the bistatic ISAR system generally suffers from a lower SNR than the monostatic one because of its non-mirror reflection geometry. A de-noising method, therefore, is proposed to improve SNR of range profiles, which accumulates the aligned range profiles non-coherently to obtain a window for noise suppression. Additionally, since the complex motion of target induces nonstationary Doppler, which is destructive to ISAR imaging, an optimal coherent processing interval (CPI) selection algorithm is further proposed to find out the interval where the Doppler is relatively stationary, so as to produce well-focused ISAR images. It utilizes the reassigned time-frequency (TF) method to obtain the high resolution instantaneous Doppler spectrum, and the minimum entropy criterion to select the optimal CPI, respectively. Note the selected CPI often contains too limited pulses to produce ISAR images with high resolution. A sparse aperture ISAR imaging method within the Bayesian framework is further proposed, which introduces the Laplacian scale mixture (LSM) model as the sparse prior, so as to reconstruct well-focused ISAR images with high resolution and low side lobes from the limited data. Compared with the traditional sparse Bayesian learning method, the proposed LSM based ISAR imaging performs superiorly on resolution improvement and noise reduction. Experimental results based on both simulated and measured data validate the effectiveness of the proposed algorithms.

ISAR 영상 형성을 위한 PPP 기반 회전운동 보상기법 연구

In order to form focused inverse synthetic aperture radar(ISAR) images of a non-uniformly rotating target, rotational motion compensation(RMC) should be performed. Prominent point processing(PPP), one of the most representative RMC methods, is used to compensate nonlinear rotation motion by exploiting the phase signals of scatterers. In this paper, we propose a new RMC method based on the integrated cubic phase function(ICPF). The ICPF requires only one-dimensional(1-D) maximization to estimate the phases of multi-component signals. Simulation results using a point scatterers model in the absence of dominant scatterers validate that PPP based on ICPF can achieve well-focused ISAR images in real time.

Efficient Autofocus Chain for ISAR Imaging of Non-Uniformly Rotating Target

Conventional approaches to inverse synthetic aperture radar (ISAR) imaging of a non-uniformly rotating target are not optimal in terms of reconstructed image quality and/or computation time. Motivated by the problems of conventional approaches, we introduce an efficient autofocus chain for ISAR imaging of a non-uniformly rotating target. The most distinct feature of the proposed autofocus chain is found in the processing sequence to compensate for the phase error induced by the translational and rotational motion (RM) of the target. In contrast to conventional approaches, RM compensation is first implemented in the proposed autofocus chain immediately after range-alignment. Next, further focusing of the ISAR image is performed by estimating the residual phase errors, providing globally well-focused ISAR images. From the experimental results using real data sets, we can conclude that the proposed autofocus chain is highly efficient in forming ISAR images of a non-uniformly rotating target in terms of both image quality and computational efficiency.

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

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