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

Inverse synthetic aperture radar (ISAR) imaging is typically useful when there is a need to classify, recognize, or identify a moving target of interest. In fact, an ISAR image highlights two-dimensional (2-D) geometric features of a target, which can provide indications of target's type, size, and other salient information. Such information can be then used for target classification, recognition, and identification. In this chapter, we will provide five case studies on applications of ISAR imaging. Such case studies are chosen to diversify the type of application and to give additional indications about how to interpret ISAR images. Specifically, in the first case study (Section 12.1), we show an example of ground-based ISAR images of a noncooperative sailing ship. The ISAR techniques used to form the ISAR images are the range-Doppler (Chapter 3) and the ICBA (Chapter 5) techniques. Different time windows are chosen to show the effects of the coherent processing interval (CPI) length on the ISAR image. Since the radar is ground based, this represents a case where the ISAR image is formed by exploiting only the sea surface induced target's motions. The second case study (Section 12.2) refers to a scenario where the radar is carried by an aircraft and the target is a ship at sea. In this case, both the target and the platform motions concur to the ISAR image formation. Also in this case, the range-Doppler and ICBA are used to form the ISAR image sequence. The third case study (Section 12.3) concerns dual ground-based/satellite ISAR imaging of a noncooperative sailing ship. In this experiment two radars are used to image a sailing ship at the same time. Two techniques are here used to form the ISAR images: a refocus technique (see Chapter 8) to form the satellite ISAR image; and a range-Doppler with ICBA to form the ground-based ISAR image. In this case study, we show how a dual system is able to form ISAR images of the same target with different target's views and projections. This is a simple case of multiple-perspective ISAR images, which may be looked at as a way to improve a target's classification and recognition [1]. The fourth case study (Section 12.4) shows ISAR images of four aircrafts obtained by processing data acquired with a ground-base radar. This case study may be regarded as the classic ISAR imaging example. Four different airplanes have been selected to show how their characteristics are mapped onto ISAR images. Such characteristics can then be used by classifiers for recognition purposes.

In the current scenario of high-resolution inverse synthetic aperture radar (ISAR) imaging, the non-cooperative targets may have strong maneuverability, which tends to cause time-variant Doppler modulation and imaging plane in the echoed data. Furthermore, it is still a challenge to realize ISAR imaging of maneuvering targets from sparse aperture (SA) data. In this paper, we focus on the problem of 3D geometry and motion estimations of maneuvering targets for interferometric ISAR (InISAR) with SA. For a target of uniformly accelerated rotation, the rotational modulation in echo is formulated as chirp sensing code under a chirp-Fourier dictionary to represent the maneuverability. In particular, a joint multi-channel imaging approach is developed to incorporate the multi-channel data and treat the multi-channel ISAR image formation as a joint-sparsity constraint optimization. Then, a modified orthogonal matching pursuit (OMP) algorithm is employed to solve the optimization problem to produce high-resolution range-Doppler (RD) images and chirp parameter estimation. The 3D target geometry and the motion estimations are followed by using the acquired RD images and chirp parameters. Herein, a joint estimation approach of 3D geometry and rotation motion is presented to realize outlier removing and error reduction. In comparison with independent single-channel processing, the proposed joint multi-channel imaging approach performs better in 2D imaging, 3D imaging, and motion estimation. Finally, experiments using both simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.

ISAR imaging of target with complex motion associated with the fractional Fourier transform

In this paper, we propose a deep learning-based model to generate high-resolution inverse synthetic aperture radar (ISAR) images under a limited radar bandwidth. While traditional ISAR systems achieve a higher resolution with a wider bandwidth, this comes at the cost of increased system resource consumption. To address this issue, we propose a method to generate high-resolution images from low-resolution ISAR images using deep learning. The training data used in this study consist of ISAR image pairs generated by calculating the scattered field using physical optics. The images generated by the proposed model can achieve average performances of 0.08, 8.94, and 0.19 in terms of learned perceptual image patch similarity, natural image quality evaluator, and deep image structure and texture similarity, respectively, compared to the original images.

In this paper, performance efficiency analysis of compressive inverse synthetic aperture radar (ISAR) imaging for a complex, electrically large target is presented. In this context, physical optics (PO) is used as a high-frequency scattered field prediction technique and compressive sensing (CS) theory is applied to ISAR imaging by exploiting the sparse point scatterer structure of the target. Results prove that CS provides high resolution and accuracy in ISAR imaging even under the Nyquist sampling rate and linearly decreases computational time.

Inverse synthetic aperture radar (ISAR) images are frequently used in target classification and recognition applications. Nevertheless, the interpretation of ISAR images remains problematic for several reasons. One of these is the fact that the image plane cannot be defined by the user but instead depends on the target's own motions and on its relative position with respect to the radar. In order to overcome the problem of interpreting two-dimensional (2D) ISAR images, a method for three-dimensional (3D) reconstruction of moving targets is presented. This method is based on the use of a dual interferometric ISAR system. The interferometric phases measured from two orthogonal baselines are used to jointly estimate the target's effective rotation vector and the heights of the scattering centers with respect to the image plane. The scattering center extraction from the ISAR image is performed by applying a multichannel CLEAN technique. Finally, a 3D image of the moving target is reconstructed from the 3D spatial coordinates of the scattering centers. The effectiveness and robustness of the proposed algorithm is first proven theoretically and then tested against several radar-target scenarios as well as in the presence of noise.

Sub-Nyquist sampling is a common means of interfering with inverse synthetic aperture radar (ISAR) imaging, which has been validated for the feasibility of 1-D compressive sensing (CS) ISAR imaging. In order to overcome the high computational complexity of 1-D CS-based ISAR imaging, the coupled 2-D CS algorithms are utilized to achieve ISAR imaging directly which has been proposed recently. In this paper, sub-Nyquist sampling jamming signals are utilized to induce multiple false-target ISAR images by using the coupled 2-D-CS algorithm. The jamming signals, which are formed by the intercepted radar signals under the sub-Nyquist sampling theorem and scattered by moving targets, are collected by ISAR to establish a 2-D super resolution model, and the finally reconstructed ISAR images are induced and show that the deceptive false-target images retaining the real target information. Simulated trails and real data of a Yak-42 plane are used to validate the correctness of the analyses and the finally well-focused false-target images greatly support the effectiveness of the sub-Nyquist sampling idea in the countermeasures of ISAR with coupled 2-D CS.

Inverse synthetic aperture radar (ISAR) imaging for the target with micro-motion parts is influenced by the micro-Doppler (m-D) effects. In this case, the radar echo is generally decomposed into the components from the main body and micro-motion parts of target, respectively, to remove the m-D effects and derive a focused ISAR image of the main body. For the sparse aperture data, however, the radar echo is intentionally or occasionally under-sampled, which defocuses the ISAR image by introducing considerable interference, and deteriorates the performance of signal decomposition for the removal of m-D effects. To address this issue, this paper proposes a novel m-D effects removed sparse aperture ISAR (SA-ISAR) imaging algorithm. Note that during a short interval of ISAR imaging, the range profiles of the main body of target from different pulses are similar, resulting in a low-rank matrix of range profile sequence of main body. For the range profiles of the micro-motion parts, they either spread in different range cells or glint in a single range cell, which results in a sparse matrix of range profile sequence. From this perspective, the low-rank and sparse properties are utilized to decompose the range profiles of the main body and micro-motion parts, respectively. Moreover, the sparsity of ISAR image is also utilized as a constraint to eliminate the interference caused by sparse aperture. Hence, SA-ISAR imaging with the removal of m-D effects is modeled as a triply constrained underdetermined optimization problem. The alternating direction method of multipliers (ADMM) and linearized ADMM (L-ADMM) are further utilized to solve the problem with high efficiency. Experimental results based on both simulated and measured data validate the effectiveness of the proposed algorithm.

Inverse synthetic aperture radar (ISAR) imaging has the competence of generating two-dimensional (2-D) high-quality images of remote moving targets, while interferometric ISAR (InISAR) is a technique to reproduce three-dimensional (3-D) high-resolution shapes of targets by employing several ISAR images obtained from different antennas. For the target moving with an invariable acceleration, the Doppler frequency shift of its echoes is proved to be time variant; consequently, the Range-Doppler (RD) algorithm will not work properly for acquiring the clear ISAR image of it. To produce high-resolution ISAR/InISAR image of the maneuvering target, a novel algorithm via the peak extraction technique is proposed. The main contributions made by this paper can be listed as follows: 1) by analyzing ISAR imaging with the RD technique for the target moving with a constant acceleration, it is elaborately demonstrated that each scattering point still corresponds to a unique peak in the defocused ISAR image; 2) the expression of the peak location in the defocused ISAR image corresponding to each scattering point is precisely derived; 3) a novel peak extraction technique is developed for forming a high-resolution radar image of the maneuvering target; 4) due to the discovery that the ISAR images obtained in this way can completely preserve the phase information, a new InISAR imaging method via the peak extraction technique is presented consequently. The correctness of the theoretical derivation and the performance of the new algorithms are illustrated through several simulation experiments.

Most existing imaging studies are mainly used for a single target or a composite scene consisting of a target and a rough surface (TRS) below it, such as the target-ocean scene. This article focuses on inverse synthetic aperture radar (ISAR) imaging of a special composite scene consisting of a target and layered rough surfaces (TLRS) below it, such as a target and a snow-soil layered scene. Differently from previous imaging algorithms based on the frequency-domain (FD) method, this article combines the time-domain ray bouncing (TDRB) method with imaging algorithms to develop an accurate ISAR imaging framework to obtain composite images of the TLRS scene. To improve the simulation efficiency of the time-domain (TD) echo matrix for ISAR imaging, the multi-thread parallel is used to accelerate the TD scattering algorithm, then, the ISAR imaging formulas based on scattering field expressions in the TLRS scene are derived. Since the linear frequency modulation pulse used in traditional radar imaging has longer pulsewidth than the Gaussian, the matched filter is modified to realize pulse compression under modulating Gaussian pulse excitations to enhance the imaging efficiency. Simulation results show that the proposed ISAR imaging framework ensures the image quality and improves the simulation efficiency significantly. Finally, the ISAR images of the target-snow-soil scene with different parameters are simulated. This article addresses the correlation between the scattering mechanism and image features for ISAR images based on different scattering components, a topic that the literatures do lack.

The high orbit height leads to the ultralow signal-to-noise ratio (ULSNR) of geosynchronous (GEO) targets, so it is necessary to increase the coherent processing interval (CPI) to improve the coherent accumulation gain. The ultra-long CPI (ULCPI) causes wide rotational angles and complex signal modulation, which poses a serious challenge to conventional inverse synthetic aperture radar (ISAR) imaging algorithms. Moreover, the high orbit height and high-speed motion of GEO targets make the “stop-and-go” model no longer applicable, and intra-pulse motion errors must be taken into consideration. To address the problems, this article proposes a high-resolution ISAR imaging algorithm with joint intra-pulse and inter-pulse high-order motion compensation (JIPHOMC) and sub-aperture images fusion for GEO targets at ULCPI. In this technique, ULCPI is divided into several sub-apertures. Then, with respect to the motion compensation in sub-apertures, we innovatively propose a JIPHOMC algorithm with particle swarm optimization (PSO), which can correct the complex spatial-time-variant (STV) motion errors caused by wide rotational angles and high-speed motion, and eliminate the 2-D high-order coupling. By utilizing image overall constraints, JIPHOMC avoids signal source decomposition processing, thereby boosting algorithm efficiency and robustness. For the sub-aperture images obtained from different perspectives, we develop a sub-aperture image fusion algorithm (SAIF) based on non-negative matrix factorization (NMF) of structured weighted sparse enhancement, which can ensure the integrity of the target structure and enhance the image signal-to-noise ratio (SNR), so as to achieve high-resolution ISAR imaging of GEO targets at ULCPI. Extensive experiments based on both scattering point simulation data and electromagnetic calculation data corroborate that the proposed algorithm outperforms traditional ISAR imaging methods for GEO targets at ULCPI.

For the Inverse Synthetic Aperture Radar (ISAR) imaging, the ISAR image obtained by the Range-Doppler (RD) or time-frequency analysis methods can not display the target's real shape due to its azimuth relating to the target Doppler frequency, thus the cross-range scaling is required for ISAR image. In this paper, a fast cross-range scaling method for ISAR is proposed to estimate the Rotational Angular Velocity (RAV). Firstly, the proposed method utilizes efficient Pseudo Polar Fast Fourier Transform (PPFFT) to transform the rotational motion of two ISAR images from two different instant time into translation in the polar angle direction. Then, a new cost function called integrated correction is defined to obtain the RAV coarse estimation. Finally, the optimal RAV can be estimated using the Bisection method to realize the cross-range scaling. Compared with the available algorithms, the proposed method avoids the problems of precision loss and high computational complexity caused by interpolation operation. The results of computer simulation and real data experiments are provided to demonstrate the validity of the proposed method.

Multi-view inverse synthetic aperture radar (ISAR) image sequences provide multi-dimensional observation information about space targets, enabling precise attitude estimation that is fundamental to both non-cooperative target monitoring and critical space operations including active debris removal and space collision avoidance. However, directly utilizing all images within an ISAR sequence for attitude estimation can result in a substantial data preprocessing workload and reduced algorithm efficiency. Given the inherent overlap and redundancy in the target information provided by these ISAR images, this paper proposes a novel space target attitude estimation method based on the selection of multi-view ISAR image sequences. The proposed method begins by establishing an ISAR imaging projection model, then characterizing the target information differences through variations in imaging plane normal, and proposing an image selection method based on the uniform sampling across elevation and azimuth angles of the imaging plane normal. On this basis, the method utilizes a high-resolution network (HRNet) to extract the feature points of typical components of the space target. This method enables simultaneous feature point extraction and matching association within ISAR images. The attitude estimation problem is subsequently modeled as an unconstrained optimization problem. Finally, the particle swarm optimization (PSO) algorithm is employed to solve this optimization problem, thereby achieving accurate attitude estimation of the space target. Experimental results demonstrate that the proposed methodology effectively filters image data, significantly reducing the number of images required while maintaining high attitude estimation accuracy. The method provides a more informative sequence than conventional selection strategies, and the tailored HRNet + PSO estimator resists performance degradation in sparse-data conditions, thereby ensuring robust overall performance.

In this study, we propose an efficient strategy to exploit cross-range resolutions (CRRs) of inverse synthetic aperture radar (ISAR) images to improve classification performance. The proposed method preprocesses an ISAR image of an unknown target using both the CRR of an unknown ISAR image, which can be obtained using the cross-range scaling algorithm, and that of the ISAR images in a training database (DB). Specifically, the proposed method adjusts the unknown ISAR image so that the CRR of the adjusted unknown ISAR image is identical to that of the ISAR images in the training DB. The adjusted unknown ISAR image is then directly used for the subsequent classification task, thus considerably improving classification performance.

Segmentation is a critical step, preliminary to High-level processing such as classification, detection and object extraction from radar images. It provides an easier way to extract information in automatic target recognition (ATR) fields. However, extracting the shape of the target from this kind of images is very difficult task, so this paper presents a new approach for Radar image segmentation and how to be able to extract target shape from 3D Inverse Synthetic Aperture Radar (ISAR) Images by combining mathematical morphology watershed and thresholding methods with top-hat transformation.