Infrared small target detection is crucial for the efficacy of infrared search and tracking systems. Current tensor decomposition methods emphasize representing small targets with sparsity but struggle to separate targets from complex backgrounds due to insufficient use of intrinsic directional information and reduced target visibility during decomposition. To address these challenges, this study introduces a Sparse Differential Directionality prior (SDD) framework. SDD leverages the distinct directional characteristics of targets to differentiate them from the background, applying mixed sparse constraints on the differential directional images and continuity difference matrix of the temporal component, both derived from Tucker decomposition. We further enhance target detectability with a saliency coherence strategy that intensifies target contrast against the background during hierarchical decomposition. A Proximal Alternating Minimizationbased (PAM) algorithm efficiently solves our proposed model. Experimental results on several real-world datasets validate our method’s effectiveness, outperforming ten state-of-the-art methods in target detection and clutter suppression. Our code is available at https://github.com/GrokCV/SDD.

Occurrence of periodical gapping in synthetic aperture radar (SAR) raw data poses a challenge in obtaining focused images. Traditional SAR imaging algorithms fail to process the periodically gapped raw data. A method is proposed to solve this problem. The presented method consists of phase compensation and recovering raw data in range Doppler domain via generalised orthogonal matching pursuit. Then, the recovered raw data is capable of being processed with the traditional SAR imaging algorithms. The Omega‐K algorithm is selected to focus the reconstructed raw data. The presented method is validated on performing better in comparison with the spectral method periodically gapped amplitude and phase estimation by point target simulation and analysis. In simulation, the raw data loses 50% by the form of azimuth periodical gapping. The resolution of point targets can reach 0.25 m in both azimuth and range direction. In addition, real SAR raw data is utilised to verify the effectiveness of the proposed method.

Synthetic Aperture Radar (SAR) raw data missing occurs when radar is interrupted by various influences. In order to cope with this problem, a new method is proposed to focus the azimuth missing SAR raw data via segmented recovery in this paper. A reference function in time domain is designed to make the missing raw data sparser in two dimensional frequency domain. Afterwards, greedy algorithms are available to recover the missing data in two dimensional frequency domain. In addition, in order to avoid range frequency aliasing problem caused by reference function multiplication in time domain, the missing raw data is split into several parts in range direction and is recovered with a segmented recovery strategy. Then, the recovered raw data is available to be focused with traditional SAR imaging algorithms. The range migration algorithm is chosen to deal with the recovered raw data in this paper. Point target and area target simulations are carried out to validate the effectiveness of the proposed method on azimuth missing SAR raw data. Moreover, the proposed method is implemented on real SAR data in order to further provide convincing demonstration.

Fine resolution spaceborne spotlight synthetic aperture radar (SAR) imaging is typically affected by the problems of curved orbit and azimuth spectral aliasing, especially when the resolution achieves the decimetre level. In order to overcome these difficulties, a method is proposed here, which consists of the modified singular value decomposition Stolt (SVDS) and the deramping‐based approach. The modified SVDS is designed to properly handle the influence induced by curved orbit, and the deramping‐based approach is utilised to solve the azimuth spectral aliasing problem. The modified SVDS is derived from the original SVDS by further taking the singular value into consideration, which is more concise and efficient. In addition, the deramping‐based approach is combined with numerical differentiation so as to accommodate the situation of curved orbit. The proposed method has smaller phase error and performs better than the generalised ωK method when resolution achieves decimetre level. Experiments carried out on point target simulation validate the effectiveness of the presented method. With focused scene size of 4 by 3 km, the range and azimuth resolutions can achieve 0.15 and 0.14 m, respectively.

The phenomenon of periodical gapping in Synthetic Aperture Radar (SAR), which is induced in various ways, creates challenges in focusing raw SAR data. To handle this problem, a novel method is proposed in this paper. Complex deconvolution is utilized to restore the azimuth spectrum of complete data from the gapped raw data in the proposed method. In other words, a new approach is provided by the proposed method to cope with periodically gapped raw SAR data via complex deconvolution. The proposed method provides a robust implementation of deconvolution for processing azimuth gapped raw data. The proposed method mainly consists of phase compensation and recovering the azimuth spectrum of raw data with complex deconvolution. The gapped data become sparser in the range of the Doppler domain after phase compensation. Then, it is feasible to recover the azimuth spectrum of the complete data from gapped raw data via complex deconvolution in the Doppler domain. Afterwards, the traditional SAR imaging algorithm is capable of focusing the reconstructed raw data in this paper. The effectiveness of the proposed method was validated via point target simulation and surface target simulation. Moreover, real SAR data were utilized to further demonstrate the validity of the proposed method.