Inverse Synthetic Aperture Radar Imaging Of Targets Research Articles

Migration through Resolution Cell Correction and Sparse Aperture ISAR Imaging for Maneuvering Target Based on Whale Optimization Algorithm-Fast Iterative Shrinkage Thresholding Algorithm.

Targets faced by inverse synthetic aperture radar (ISAR) are often non-cooperative, with target maneuvering being the main manifestation of this non-cooperation. Maneuvers cause ISAR imaging results to be severely defocused, which can create huge difficulties in target identification. In addition, as the ISAR bandwidth continues to increase, the impact of migration through resolution cells (MTRC) on imaging results becomes more significant. Target non-cooperation may also result in sparse aperture, leading to the failure of traditional ISAR imaging algorithms. Therefore, this paper proposes an algorithm to realize MTRC correction and sparse aperture ISAR imaging for maneuvering targets simultaneously named whale optimization algorithm-fast iterative shrinkage thresholding algorithm (WOA-FISTA). In this algorithm, FISTA is used to perform MTRC correction and sparse aperture ISAR imaging efficiently and WOA is adopted to estimate the rotational parameter to eliminate the effects of maneuvering on imaging results. Experimental results based on simulation and measured datasets prove that the proposed algorithm implements sparse aperture ISAR imaging and MTRC correction for maneuvering targets simultaneously. The proposed algorithm achieves better results than traditional algorithms under different signal-to-noise ratio conditions.

Interferometric ISAR Imaging of Space Targets Using Pulse-Level Image Registration Method

Interferometric inverse synthetic aperture radar (InISAR) imaging is capable of retrieving three-dimensional target information from multi-channel inverse synthetic aperture radar (ISAR) images obtained by adjacent radars or antennas. The InISAR imaging of space targets is more difficult, compared with that observes short range targets with small radar systems. Considering the practical constraints when observing space targets with multiple large radars, this paper establishes a flexible InISAR imaging system which allows for non-perpendicular baselines and observation of targets under squint mode. Based on this, the generalized formulas for calculating scatterers' coordinates from measured interferometric phases are derived. Moreover, a pulse-level image registration method based on joint translational motion compensation is proposed to recover the coherence between multi-channel ISAR images, under condition of inconsistent pulse delays at different radar channels. With the proposed method, promising InISAR imaging results are obtained via two simulation experiments. The first experiment assumes ideal point scattering of the target, which verifies the high accuracy and computational efficiency of the proposed ISAR image registration method; the second experiment considers more practical target scattering using facet target model and physical optics algorithm, which demonstrates the possible deviation in InISAR imaging result caused by scatterer's anisotropic scattering and residual error of translational motion compensation. We believe that the derivation and simulation results in this paper would be helpful reference for practical InISAR imaging experiments.

Sparsity-Driven Inverse Synthetic Aperture Radar Imaging Using Accelerated Meta-Heuristic Optimization

When the inverse synthetic aperture radar (ISAR) system have sparse aperture (SA) dataset, which are non-uniform and less than Nyquist sampling rate along the cross-range axis, it can significantly blur the ISAR image of target having complex motion. To overcome this, we propose an improved ISAR imaging method based on dictionary estimation using parametric signal reconstruction (DEPSR) using accelerated meta-heuristic optimization (MHO). The proposed method reconstructs a motion-compensated and full-aperture (FA) dataset from a sparse-aperture (SA) dataset using MHO, which can be accelerated by a graphics processing unit (GPU). After then, we can obtain the motion-compensated FA dataset and by using this, we can exploit the conventional range-Doppler (R-D) processing. Simulation and real-data-based ISAR imaging results validate the superiority of the proposed method in terms of image quality in comparison with conventional methods, such as simple R-D processing, basis pursuit denoising, and DEPSR. Particularly, the proposed method exhibits a significantly lower computational burden than that of the traditional DEPSR approach.

ISAR Imaging of Precession Target Based on Joint Constraints of Low Rank and Sparsity of Tensor

Precession is a typical form of micro-motion that can bring about complex and time-varying Doppler modulation. The range instantaneous Doppler (RID) method, which uses time-frequency analysis instead of the Fourier transform to describe the time-varying Doppler, is typically used to obtain the high-resolution inverse synthetic aperture radar (ISAR) image of a precession target. However, the observation time of a specific target is often non-uniform due to various interference and channel switching among multi-channel radars, which will lead to a sparse aperture. Sparse aperture can cause sidelobe interference in the ISAR image obtained by the RID method, making it difficult to focus well. To solve the problem whereby the RID method fails to image a precession target with sparse aperture, this paper proposes a new method based on the joint constraints of low-rank and sparsity of tensor, and uses the alternating direction method of multipliers to solve the problem. The low-rank can constrain the correlation among consecutive ISAR images, and sparsity can remove the impact of sparse aperture. This effectively eliminates the micro-Doppler interference and sidelobe interference in ISAR image, and enables the reconstruction of precession target in a sequence of ISAR images with sparse aperture. Experimental results under both simulations and darkroom measurements verify that the proposed method performs well on ISAR images of conic precession target with sparse aperture.

ISAR imaging of targets exhibiting micro-motion under the joint constraints of low SNR and sparse rate

To solve the problem of the defocusing of inverse synthetic aperture radar (ISAR) image of targets exhibiting micro-motion under the joint constraints of low signal-to-noise ratio (SNR) and sparse rate, this paper proposes a method based on the joint constraints of noise and prior information of target. We use the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm to eliminate the noise, and constrain prior information of target (low rank and sparsity) by using nuclear norm and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm. On this basis, we deduce the solution of this problem according to the linear alternating direction method of multipliers. Finally, the proposed method is verified by simulated and measured data. The results show that the proposed method can achieve good focus on the ISAR image of targets exhibiting micro-motion under the joint constraints of low SNR and sparse rate, and is much more robust than the traditional methods.

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.

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.

Classification of Automotive Targets Using Inverse Synthetic Aperture Radar Images

Traditionally, point cloud representations or Doppler spectrograms have been generated from short-range automotive radars for dynamic object detection and classification. In this work, we propose using inverse synthetic aperture radar (ISAR) images obtained from range compensated turning targets for the classification of different types of vehicles. We experimentally demonstrate that ISAR images of automotive targets provide rich features such as the dimensions, trajectory, and the number of wheels of the vehicles for classification. Additionally, we present a simulation framework for generating large volumes of realistic ISAR images of automotive targets at millimeter-wave frequencies for training classifiers. The model incorporates radar scattering phenomenology of commonly found vehicles along with range-Doppler-based clutter and receiver noise. The model is experimentally validated with measurement data gathered from an automotive radar. The images from the simulation database are subsequently classified using traditional machine learning techniques and deep neural networks based on transfer learning. We show that the ISAR images offer a classification accuracy above 90% and are robust to both noise and clutter.

High-resolution ISAR imaging based on photonic receiving for high-accuracy automatic target recognition.

A scheme of high-resolution inverse synthetic aperture radar (ISAR) imaging based on photonic receiving is demonstrated. In the scheme, the linear frequency modulated (LFM) pulse echoes with 8 GHz bandwidth at the center frequency of 36 GHz are directly sampled with the photonic analog-to-digital converter (PADC). The ISAR images of complex targets can be constructed without detection range swath limitation due to the fidelity of the sampled results. The images of two pyramids demonstrate that the two-dimension (2D) resolution is 3.3 cm × 1.9 cm. Furthermore, the automatic target recognition (ATR) is employed based on the high-resolution experimental dataset under the assistance of deep learning. Despite of the small training dataset containing only 50 samples for each model, the ATR accuracy of three complex targets is still validated to be 95% on a test dataset with the equal number of samples.

Ultrawideband ISAR Imaging of Maneuvering Targets With Joint High-Order Motion Compensation and Azimuth Scaling

Ultrawideband (UWB) radar can achieve ultrahigh-resolution inverse synthetic aperture radar (ISAR) imaging of noncooperative targets by transmitting UWB signals. However, the spatial-variant (SV) high-order migration through range cell (MTRC) and phase errors produced by the UWB radar system have seriously challenged the feasibility of conventional ISAR imaging algorithms. Moreover, maneuvering targets has exacerbated this problem compared with the steady ones. In this article, a UWB ISAR imaging algorithm of maneuvering targets with joint high-order motion compensation and azimuth scaling (JHOMCAS) is proposed. For the azimuth SV linear MTRC and 2-D SV high-order MTRC caused by the maneuvering rotational motion of the targets, the cascaded generalized keystone transform (GKT) is adopted for precise correction. It is worth noting that, when eliminating the SV MTRC by the cascaded GKT, the 2-D SV high-order phase errors induced by the maneuvering rotational motion must be accurately compensated, or MTRC correction will fail. The traditional autofocus methods usually only address the phase errors shared by the total target without due attention to the fine SV property. In response to this problem, this article first develops a joint 2-D SV autofocus and azimuth scaling algorithm (JSVAAS) to achieve the integration of SV high-order phase error compensation and azimuth scaling. A JHOMCAS algorithm is proposed to perform the joint processing of GKT and JSVAAS, ``GKT-JSVAAS-GKT.'' This approach helps accomplish the high-precision UWB ISAR imaging of maneuvering targets, and the well-focused and scaled UWB ISAR images obtained will build a sound foundation for target classification and recognition. Extensive experiments based on both scattering point simulation data and electromagnetic calculation data verify that the proposed algorithm outperforms conventional ISAR imaging approaches in UWB ISAR imaging of maneuvering targets.

Precession Parameter Estimation From Wideband Measurements for 3-D ISAR Imaging of Cone-Shaped Targets

Precession of the cone-shaped warhead is the typical micromotion of ballistic targets, and 3-D inverse synthetic aperture radar (ISAR) imaging of the precession warhead is of great significance for ballistic target identification. This letter proposes a novel method for the precession parameter estimation and the 3-D ISAR imaging of the cone-shaped target. First, the imaging model of the precession cone-shaped target is developed, and the relationship between the radial-range curve on the range–slow-time plane and the precession parameters is derived. Subsequently, the wideband measurements from two-aspect radars are jointly processed to solve the target precession parameters implicitly involved in the radial-range curve of the scatterer at the cone top. Finally, based on the estimation of precession parameters, the 3-D images of the target can be reconstructed, which reveal the structure characteristics and spatial attitude of the target. Simulation results verify the effectiveness of the proposed method.

Integration of Super-Resolution ISAR Imaging and Fine Motion Compensation for Complex Maneuvering Ship Targets Under High Sea State

Under high sea state, ship targets make complex maneuvering motions due to strong disturbances such as sea waves and sea winds. Selecting the optimal imaging time interval to shorten the coherent processing interval (CPI) can reduce the complexity of motion errors, but the imaging resolution is affected as well, i.e., the motion error complexity and imaging resolution are constrained by each other. To address this problem, this article proposes a novel inverse synthetic aperture radar (ISAR) imaging framework for complex maneuvering ship targets to achieve the integration of super-resolution (SR) ISAR imaging and fine motion compensation (ISRFMC) under high sea state. With regard to the complex maneuvering motion of ship targets, we analyze the motion errors caused by the time-variant rotational velocity and imaging projection plane (IPP) on the echo signals, respectively, and a fine phase error model is established to uniformly represent the dual time-variant characteristic (DTVC) of complex maneuvering ship targets. Moreover, a deformed Akaike information criterion (DAIC) is developed to realize the adaptive selection of the phase error model with the image sharpness as the objective function. Underpinned by the Bayesian compressive sensing (BCS) theory, the SR ISAR imaging can be realized by solving a sparsity-driven optimization problem via a modified quasi-Newton solver. Particularly, the fine phase errors are constructed as the model errors of image reconstruction, and the particle swarm optimization algorithm (PSO) is utilized to solve the maximum image sharpness optimization problem in order to perform the joint fine motion compensation and azimuth scaling (JFMCAS). ISRFMC or the integration of SR ISAR imaging and fine motion compensation can be achieved through alternate iteration, so as to obtain well-focused and scaled high-resolution ISAR images of complex maneuvering ship targets under high sea state. Extensive experiments based on both simulated and real data verify that the proposed algorithm is capable of addressing the conflict between imaging resolution and motion error complexity under high sea state.

ISAR Imaging of Target Exhibiting Micro-Motion With Sparse Aperture via Model-Driven Deep Network

This study proposes a model-driven deep network based on the linear alternating direction method of multipliers (L-ADMM), to solve the problem whereby the inverse synthetic aperture radar (ISAR) generates defocused images of targets exhibiting micro-motion with sparse aperture. The network unfolds the operation process of L-ADMM into a model-driven deep network, and automatically optimizes the parameters of the network through learning instead of manually adjusting the parameters, which can better obtain images. Analyses of data acquired through simulations and experimental measurements were used to compare the results of imaging obtained by L-ADMM-net with those of the range Doppler (R-D) algorithm, chirplet algorithm, and L-ADMM. The entropy of images obtained by L-ADMM-net was the lowest, and their image contrast and resolution were the highest. Moreover, L-ADMM-net can generate high-resolution images of targets exhibiting micro-motion with sparse aperture at a low signal-to-noise ratio (SNR), which verifies its robustness. It can also automatically update and adjust parameters more stably than L-ADMM. The proposed method significantly improves the resolution, robustness, and stability of images of targets exhibiting micro-motion in different situations compared with traditional methods, and can provide technical support for target recognition in the future.

An Iterative Phase Autofocus Approach for ISAR Imaging of Maneuvering Targets

Translational motion compensation and azimuth compression are two essential processes in inverse synthetic aperture radar (ISAR) imaging. The anterior process recovers coherence between pulses, during which the phase autofocus algorithm is usually used. For ISAR imaging of maneuvering targets, conventional phase autofocus methods cannot effectively eliminate the phase error due to the adverse influence of the quadratic phase terms caused by the target’s maneuvering motion, which leads to the blurring of ISAR images. To address this problem, an iterative phase autofocus approach for ISAR imaging of maneuvering targets is proposed in this paper. Considering the coupling between translational phase errors and quadratic phase terms, minimum entropy-based autofocus (MEA) method and adaptive modified Fourier transform (MFT) are performed iteratively to realize better imaging results. In this way, both the translational phase error and quadratic phase terms induced by target’s maneuvering motion can be compensated effectively, and the globally optimal ISAR image is obtained. Comparison ISAR imaging results indicates that the new approach achieves stable and better ISAR image under a simple procedure. Experimental results show that the image entropy of the proposed approach is 0.2 smaller than the MEA method, which validates the effectiveness of the new approach.

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.

Estimation of the rotation velocity and scaling for ISAR imaging of near-field targets based on the integrated generalized cubic phase function and image interpolation

ABSTRACT Inverse synthetic aperture radar (ISAR) imaging of the near-field target is potentially significant with the development of radar technology. The near-field target cannot be mapped to the ISAR image correctly because it does not satisfy the far-field distance approximation. To efficiently solve the distortion problem, it is more desirable to estimate the rotation velocity for near-field ISAR imaging. In this article, we propose a method for estimating the rotation velocity of the near-field target based on integrated generalized cubic phase function. Different from the far-field target, the received signal of the near-field target in a range bin is considered as the cubic phase signal, and the rotation velocity can be determined by the coefficients of the cubic phase signal. Afterwards, we utilize the cubic convolution image interpolation to rescale the image on the homogeneous Cartesian domain. Experimental results of simulated data demonstrate the effectiveness of the proposed method on rotation velocity estimation and scaling of the near-field target.

Approach for ISAR imaging of near-field targets based on coordinate conversion and image interpolation

Inverse synthetic aperture radar (ISAR) imaging of near-field targets is potentially useful in some specific applications, which makes it very important to efficiently produce high-quality image of the near-field target. In this paper, the simplified target model with uniform linear motion is applied to the near-field target imaging, which overcomes the complexity of the traditional near-field imaging algorithm. According to this signal model, the method based on coordinate conversion and image interpolation combined with the range-Doppler (R-D) algorithm is proposed to correct the near-field distortion problem. Compared with the back-projection (BP) algorithm, the proposed method produces better focused ISAR images of the near-field target, and decreases the computation complexity significantly. Experimental results of the simulated data have demonstrated the effectiveness and robustness of the proposed method.

An approach of motion compensation and ISAR imaging for micro-motion targets

Inverse synthetic aperture radar (ISAR) imaging of the target with the non-rigid body is very important in the field of radar signal processing. In this paper, a motion compensation method combined with the preprocessing and global technique is proposed to reduce the influence of micro-motion components in the fast time domain, and the micro-Doppler (m-D) signal in the slow time domain is separated by the improved complex-valued empirical-mode decomposition (CEMD) algorithm, which makes the m-D signal more effectively distinguishable from the signal for the main body by translating the target to the Doppler center. Then, a better focused ISAR image of the target with the non-rigid body can be obtained consequently. Results of the simulated and raw data demonstrate the effectiveness of the algorithm.

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.

Ship Target Imaging in Airborne SAR System Based on Automatic Image Segmentation and ISAR Technique

The airborne synthetic aperture radar (SAR) image of ship target will be blurred for the complex motion of the target, which will influence the performance of feature extraction and target classification. In this article, a refocusing method of ship target in airborne SAR image though the inverse synthetic aperture radar (ISAR) technique is presented. The novel contributions of this article can be summarized as follows: 1) the residual phase of processed signal is analyzed in detail to illustrate the necessity of adopting ISAR technique; 2) a valid, labor-saving, and automatic image segmentation approach via clustering algorithm is put forward for automatic data extraction and inverse mapping. The clustering algorithm we choose is the agglomerative hierarchical cluster algorithm, which does not require the number of categories in advance; 3) the refocused individual ISAR image of individual ship target is yielded though the range-instantaneous Doppler algorithm. Finally, the simulated and real measured data are processed, and imaging results verify the effectiveness of the novel algorithm presented in this article.