Sparse aperture ISAR imaging for maneuvering targets is a relatively difficult task due to the complex form of observation model. In this letter, an approximated observation model based on CZT and NCS is proposed to accelerate the implementation of forward and backward operators. A structured sparse prior is introduced to establish a statistical framework for SA-ISAR imaging and VB-GAMP is utilized to implement a fast inference. A rotation parameters estimation based on image quality optimization is further plugged in the reconstruction procedure to achieve joint imaging and motion compensation. Experiments on simulated and measured data validate the effectiveness and efficiency of the proposed method.

For inverse synthetic aperture radar (ISAR) imaging under sparse aperture (SA) conditions, the rotation motion compensation is seldom considered. However, with the improvement of resolution, the migration through resolution cell (MTRC) cannot be ignored. Traditional methods for rotation motion compensation generally fail in SA cases. This article proposes a method to jointly implement sparse imaging and compensation of the MTRC in a structured sparse Bayesian learning (SBL) framework. Due to the coupling of fast time and slow time, the observation model is established in a vectorized form. To reduce the computational complexity, approximated inference methods are utilized to achieve fast inference for the posteriors. Maximum contrast (MC) criterion is adopted to estimate the rotation parameters. The approximated implementation for the forward operator and backward operator is discussed to further accelerate the algorithm. Experimental results based on simulated and measured data validate the effectiveness and efficiency of the proposed methods.

A multistatic ISAR system can observe a target from multiple observation angles. Compared with the monostatic ISAR system, the multistatic ISAR system can obtain more spatial sampling data, which provides the ability for high-resolution ISAR imaging. In some cases, the locations of radars are close. There are overlaps among observing angles, which brings little cross-range resolution improvement. However, such scenes are less considered in previous work. In the scene with overlaps, the image obtained by each radar may be similar due to the similar observation angles. In this letter, a novel multistatic ISAR imaging model is proposed by applying the similarity prior as a constraint. And an effecient image reconstruction algorithm is derived based on the orthogonality of observation matrix. Compared with existing CS based methods, the proposed method can be directly applied on multistatic ISAR echoes without pre-processing of rearranging, which is more convenient in practical applications. Experiment results of simulated and measured data show that the proposed method achieves better performance especially under low signal-to-noise (SNR) conditions.

Compared to 2D inverse synthetic aperture radar (ISAR) images of a space target, its 3D model can provide adequate details and accurate measurement parameters. However, it is challenging to tackle the problem of feature extraction and correlation during 3D reconstruction of space targets purely based on radar image sequences, due to their lack of clear evidence in imaging similarity compared to optical images. To address this problem, this paper proposes radar neural radiance fields (i.e. RaNeRF), which is a novel 3D reconstruction method using only observed ISAR image sequences. Firstly, the 3D structure of a target is represented as a continuous 6D function of space positions and viewing directions using a fully-connected deep network. Secondly, the relationship between the 3D structure and 2D ISAR images of the target is constructed to enable differential rendering of ISAR images. Our overall pipeline can thus be trained using the discrepancy between the modulus of rendered and observed ISAR images in a purely self-supervised manner without 3D supervision. Finally, the 3D mesh model of the target can be retrieved from the learned density field via marching cube. As a result, the proposed RaNeRF can directly reconstruct the 3D structure of targets without explicit feature extraction and correlation of ISAR image sequences. Both quantitative and qualitative results verify the effectiveness of the proposed method. Compared to conventional baseline methods using point clouds, our reconstructed structure is more complete and accurate. In addition, the optimized model can synthesize ISAR images at novel observation direction, which can be used for downstream tasks including data augmentation and target recognition.

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.

Sparse aperture inverse synthesis aperture radar (SA-ISAR) imaging is generally solved by compressed sensing (CS) methods or sparse signal recovery (SSR). Many SSR methods focus on the sparsity of radar images only, which achieves unsatisfactory results on structural data. In addition, most of the traditional CS algorithms suffer from a heavy computational burden. In this article, a new deep unfolding network called pattern-coupled sparse Bayesian learning (PCSBL)-generalized approximate message passing (GAMP)-Net is proposed. The proposed network structure can learn the model of block-sparse information from data to reconstruct images of better quality via fewer iteration steps. First, a complex-valued pattern-coupled hierarchical Gaussian prior model is established. Then, the GAMP algorithm is applied for computational Bayesian inference. Based on the previous PCSBL-GAMP framework, the iterative procedure is unrolled to be a deep network structure. A complex-valued backpropagation (BP) algorithm is derived for network training. Experiment results based on simulated and measured data validate the superiority of the proposed method over the traditional PCSBL-GAMP algorithm. Also, the proposed algorithm is ten times faster than the traditional PCSBL-GAMP algorithm.

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.

For sparse aperture (SA) radar imaging, the phase errors are difficult to be estimated, which challenges the traditional autofocusing for inverse synthetic aperture radar (ISAR) imaging. A novel Bayesian ISAR autofocusing algorithm for SA is proposed. We unfold the sparse Laplace prior to two layers so that the full variational Bayesian inference can be derived. To further exploit the prior knowledge on the structure of radar images, dependencies among adjacent pixels are considered to design a structured sparse prior. In addition, the minimum entropy criterion is utilized to estimate the phase error during the reconstruction of the ISAR image to achieve ISAR autofocusing. The superiority of the proposed method against the traditional sparsity-driven method is validated by the experimental results based on both simulated and measured data.