Analysis of the attitude and geometry of space targets with inverse synthetic aperture radar (ISAR) technique is a significant and difficult task. Most existing methods hardly consider the radar observation geometry in the determination of target attitude. This paper proposes a novel approach to estimating the three-dimensional (3D) attitude and reconstructing typical component geometry of space targets from an ISAR image sequence. The approach bridges range-Doppler (RD) images and target attitude parameters with the accommodation of target trajectory information and the ISAR geometric projection model. By exploring the shape feature within the ISAR sequence, the target attitude and rectangular component size are estimated through solving an optimization with prior shape constraints. Comparative experiments illustrate the advantages of the proposed method in both feature association and reconstruction feasibility. Moreover, considering practical circumstances, a further analysis is made of the robustness of the proposed algorithm after the attitude estimation experiment.

In general, conventional error correction for inverse synthetic aperture radarimaging consists of range alignment and phase adjustment, which compensate range shift and phase error, respectively. Minimum entropy-based methods have been proposed to realize range alignment and phase adjustment. However, it becomes challenging to align high-resolution profiles when strong noise presents, even using entropy minimization. Consequently, the subsequent phase adjustment fails to correct phase errors. In this article, we propose a novel method for translational motion correction, where entropy minimization is utilized to achieve range alignment and phase adjustment jointly. And, a coordinate descent algorithm is proposed to solve the optimization implemented by quasi-Newton algorithm. Moreover, a method for coarse motion estimation is also proposed for initialization in solving the optimization. Both simulated and real-measured datasets are used to confirm the effectiveness of the joint motion correction in low signal-to-noise ratio situations.

With regard to the multifunction radar transmitting sparse stepped-frequency-modulation (SSFM) signal for inverse synthetic aperture radar (ISAR) imaging, the received echo signal is usually sparse in two dimensions, i.e., sparse stepped-frequency-modulation and sparse aperture waveforms (SSFM-SAWs), and there are translational and rotational motion errors between subpulses. The two problems seriously challenge the feasibility of conventional 1-D sparse reconstruction algorithms. This article proposes a novel high-resolution ISAR imaging and motion compensation with the 2-D joint sparse reconstruction (2D-JSR) algorithm. In this technique, a 2D-JSR dictionary is established according to the SSFM-SAW signal model. Based on the Bayesian compressive sensing (BCS) theory, the 2D-JSR is then transformed into solving a sparsity-driven optimization problem with l₁-norm constraint. With the accommodation of a modified quasi-Newton solver, the exact recovery of SSFM-SAW can be achieved. In addition, a new algorithm, named joint translational motion compensation and range spatial-variant autofocus (JTSVA) algorithm, is also developed to realize motion parameters by a two-step estimation. Integrating with 2-D coupling information of echo signal and the efficient and robust motion compensation algorithm, the accurate motion parameters together with well-focused and scaled high-resolution ISAR images can be obtained. Extensive experiments based on both simulated and real data demonstrate that the proposed algorithm is capable of the precise reconstruction of ISAR images and the effective suppression of both motion errors and noise.

In recent years, deep convolutional neural networks (CNNs) pose superior synthetic aperture radar target recognition (SAR-TR) performance. However, CNN-based SAR classifiers would be vulnerable to adversarial attack (AA) when strong nonlinearity of CNN is contrapuntally utilized by AA. The AA can cause a CNN classifier to produce erroneous predictions with extremely high confidence by injecting a tiny adversarial perturbation to the input SAR images. In this letter, an accelerated SAR-TR AA algorithm is proposed named Fast C&W. We introduce a well-trained deep encoder network to replace the process of searching for the optimal perturbation of the input SAR image iteratively in the vanilla C&W algorithm. In this way, an adversarial perturbation can be generated much faster through the rapid forward mapping during an attack. Meanwhile, as a feature extraction network, the encoder network can learn the separable data region by optimizing the attack loss function. Through the encoder network, the added perturbation energy can be mainly concentrated on a region of target instead of background clutter area. This property would be of advantages in the perturbation location control in an SAR image. In the experiments, we use the proposed AA algorithm to interfere with the deep CNN-based high-accuracy SAR-TR model trained on the moving and stationary target acquisition and recognition (MSTAR) data set, which demonstrates its excellent effectiveness and thousands of times of efficiency improvement.