The registration of inverse synthetic aperture radar (ISAR) images is a key process in interferometric ISAR (InISAR) imaging. According to published literature, there are mainly two solutions for ISAR image registration, one is correlation coefficient based method and the other is parameter estimation of target angular motion. Though some simulation results are effective, defects of these two methods still need to be studied and solved. Different from precedent methods, the authors try new approach for ISAR image registration in this study and better results are obtained. The authors’ new approach is based on joint translational motion compensation, which consists of two steps namely joint range alignment and joint phase autofocus. The first step realises registration along range direction and the second one realises registration along cross‐range direction. The new method achieves ISAR image registration along with the translational motion compensation, thus no extra computation is needed. In addition to high computational efficiency, the new method is more precise compared with precedent methods and works well even under strong noise. Simulation results show the advantages of the proposed method in computing efficiency, precision, robustness and practicability.

Focusing on the inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, this paper presents a new imaging method which works well when the target’s maneuvering is not too severe. After translational motion compensation, we describe the equivalent rotation of maneuvering targets by two variables—the relative chirp rate of the linear frequency modulated (LFM) signal and the Doppler focus shift. The first variable indicates the target’s motion status, and the second one represents the possible residual error of the translational motion compensation. With them, a modified Fourier transform matrix is constructed and then used for cross-range compression. Consequently, the imaging of maneuvering is converted into a two-dimensional parameter optimization problem in which a stable and clear ISAR image is guaranteed. A gradient descent optimization scheme is employed to obtain the accurate relative chirp rate and Doppler focus shift. Moreover, we designed an efficient and robust initialization process for the gradient descent method, thus, the well-focused ISAR images of maneuvering targets can be achieved adaptively. Human intervention is not needed, and it is quite convenient for practical ISAR imaging systems. Compared to precedent imaging methods, the new method achieves better imaging quality under reasonable computational cost. Simulation results are provided to validate the effectiveness and advantages of the proposed 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.

Focusing on the inverse synthetic aperture radar (ISAR) imaging of targets with complex motion, this paper proposes a modified version of the Fourier transform, called non-uniform rotation transform, to achieve cross-range compression. After translational motion compensation, the target’s complex motion is converted into non-uniform rotation. We define two variables—relative angular acceleration (RAA) and relative angular jerk (RAJ)—to describe the rotational nonuniformity. With the estimated RAA and RAJ, rotational nonuniformity compensation is carried out in the non-uniform rotation transform matrix, after which, a focused ISAR image can be obtained. Moreover, considering the possible deviation of RAA and RAJ, we design an optimization scheme to obtain the optimal RAA and RAJ according to the optimal quality of the ISAR image. Consequently, the ISAR imaging of targets with complex motion is converted into a parameter optimization problem in which a stable and clear ISAR image is guaranteed. Compared to precedent imaging methods, the new method achieves better imaging results with a reasonable computational cost. Experimental results verify the effectiveness and advantages of the proposed algorithm.

The accurate classification of Low, Slow, and Small (LSS) targets in low-altitude airspace is a critical challenge for surveillance and safety systems. While radar provides essential data, methods relying on a single information source, such as micro-Doppler signatures or kinematic tracks, are often unreliable due to low radar cross-sections and complex clutter environments. Consequently, multimodal fusion has emerged as a promising direction. However, the performance of conventional fusion algorithms is often compromised by their static fusion strategies, where fixed weights assigned during training cannot adapt to real-world scenarios in which the quality of a data stream may degrade. To address these limitations, this paper proposes the Multimodal Adaptive Feature Fusion Network (MAFF-Net), a multilevel dual-branch architecture designed to overcome the vulnerabilities of both single-source methods and static fusion. At each hierarchical level, a Semantic Bridge Module (SBM) first aligns heterogeneous features from different modalities using contrastive learning. Crucially, an Adaptive Fusion Module (AFM) then dynamically arbitrates the contribution of each information stream, enabling the network to intelligently rely on higher-quality data and mitigate the effects of signal distortion or loss. Evaluated on a comprehensive dataset collected from five distinct geographical sites, MAFF-Net achieves 91.01% accuracy, outperforming other advanced fusion methods by up to 2.0 percentage points. The model's robustness and practical utility are further validated through extensive cross-site testing and a successful in-the-field deployment.