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Aiming at the problems of low resolution and poor noise suppression for maneuvering targets with traditional inverse synthetic aperture radar (ISAR) imaging algorithms, we propose a motion compensation-driven high-resolution imaging algorithm based on Fourier ptychographic microscopy (FPM) technology. FPM is a phase retrieval technology that uses the concept of synthetic aperture to obtain the high-resolution image of the target. The process of FPM imaging is composed of the enhancement of the known target information in the spatial domain and some fixed restricted alternating iteration in the frequency domain. This article analyzes the mathematical model of maneuvering target echo and then studies the rationality of applying the FPM phase retrieval algorithm to ISAR maneuvering target imaging. The proposed algorithm mainly includes two steps. First, a series of low-resolution ISAR images are generated by sampling echo data through an aperture function. Second, the FPM phase retrieval technology is applied to splice and reconstruct the spectrum of the low-resolution images to achieve the focused ISAR image. The effectiveness of the proposed algorithm is verified by using multiple groups of simulation data and real radar data. [ABSTRACT FROM AUTHOR]

In the actual scene of interferometric inverse synthetic aperture radar (InISAR) imaging, the noncooperative targets may make a nonuniform 3-D rotational motion (3-D-RM), which contributes not only to the time-variant Doppler modulation but also to the spatial-variant wave path difference (SVWPD). This, in turn, seriously degrades the 3-D geometry reconstruction accuracy of the targets. Furthermore, it is an enormous challenge to realize InISAR imaging from sparse frequency band and sparse aperture (SFB-SA) signals. This article seeks to address the problems of fine image registration and 2-D joint sparse reconstruction (2-D-JSR) for InISAR imaging with SFB-SA signals. With regard to the maneuvering targets with 3-D-RM, a novel SVWPD signal model is established. Moreover, a new algorithm, named joint wave path difference compensation (JWPDC) algorithm, is developed to perform fine image registration. It can not only combine multiple channels to achieve image registration but also jointly compensate for the non-SVWPD (NSVWPD) and SVWPD. A joint multichannel 2-D-JSR (JMC-2-D-JSR) ISAR imaging algorithm is also proposed according to the SFB-SA signal model to produce high-resolution ISAR images. Underpinned by the Bayesian compressive sensing (BCS) theory, the JMC-2-D-JSR ISAR imaging can be realized by solving a sparsity-driven optimization problem via a modified quasi-Newton solver. Through iterative processing of JMC-2-D-JSR and JWPDC, the high-quality 3-D InISAR images of maneuvering targets with 3-D-RM can be obtained. Extensive experimental results based on both simulated and real data corroborate the effectiveness of the proposed algorithm that outperforms other available InISAR imaging frameworks in 2-D imaging, 3-D imaging, and motion compensation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The inverse synthetic aperture radar (ISAR) imaging technique of a moving target with sparse sampling data has attracted wide attention due to its ability to reduce the data collection burden. However, traditional low-rank or 2D compressive sensing (CS)-based ISAR imaging methods can handle the random sampling or the separable sampling data only. When the specific data collection condition cannot be satisfied, low-rank or 2D CS-based methods cannot provide satisfactory imaging results any more. To remedy this problem, in this paper, we proposed a joint low-rank and sparsity priors’ constrained model for ISAR imaging with various sparse data patterns. This model is inspired by the facts that the received radar data have a low-rank property and the ISAR image is sparse on the specific dictionary. Two reconstruction algorithms to solve the double priors’ constrained optimization problem are developed under the alternative direction method of multipliers (ADMM) framework with the help of augmented Lagrange multipliers (ALM). Results on simulation data and real data show that the proposed methods are quite effective in recovering missing samples and focused image and perform better than the matrix completion-based method and the sparse representation-based method when dealing with the various kinds of sparse sampling data. [ABSTRACT FROM AUTHOR]

The radar echoes of space debris sometimes have drawbacks of inadequate pulse samples, self-occlusion, and data missing, while the state-of-art radar imaging algorithms become unproductive. To tackle this problem, this paper proposes a novel method for three-dimensional (3-D) imaging of space debris. This iterative algorithm terminates automatically and can recover the intensity of space debris radar cross section, making it practical in real-world applications. Unlike the 3-D imaging methods based on multistatic inverse synthetic aperture radar, the proposed method is applicable to monostatic ISAR. [ABSTRACT FROM AUTHOR]

We present a method to generate three-dimensional (3D) inverse synthetic aperture radar (ISAR) images of a target at arbitrary aspect angles using the shooting and bouncing ray (SBR) method. We have derived a 3D-image-domain ray-tube integration formula based on the SBR technique. The imaging formula is in a form of convolution, then a nonuniform fast Fourier transform (NUFFT) is induced to achieve the convolution, which can be used to generate 3D ISAR images rapidly. Compared to the conventional algorithm where the ISAR images are obtained by inverse Fourier transforming the computed scattered fields over a range of frequencies and a range of aspect angles, the new formula needs only one-time ray tracing and physical optics (PO) integration over a small angle span. Hence the computational time is decreased tremendously. The ISAR images of a missile target and an armored car for several aspect angles are presented to demonstrate the efficiency and accuracy of the proposed method. [ABSTRACT FROM PUBLISHER]