Accurately and robustly detecting infrared (IR) small targets plays an essential role in infrared search and tracking (IRST) systems against complex backgrounds. However, the low signal-to-clutter ratio (SCR) feature target, also known as the dim target, is more similar to the local background region, making it prone to being misdetected as background or buried in clutter interferences, ultimately leading to detection failure. This has become a common and unavoidable challenge in the field. To alleviate this issue, an interpretation weighted sparse (IWS) method is proposed to detect IR small and dim targets while maintaining clutter suppression capability. The interpretability operation of the proposed method stems from an analysis of IR target characteristics, including dim target feature extraction and clutter suppression. Based on the multi-subspaces model, the relevant feature of the dim target is mined by a designed structure tensor, improving the target’s sparsity. Then, the contrast characteristic is introduced to eliminate the cost of dim target mine operation, namely residual clutter interference. Finally, the IWS map is obtained by fusing the target detection module and clutter suppression module, after which an adaptive threshold segmentation operation is adopted to extract the small targets. A series of experimental results and application discussion cases demonstrate that IWS outperforms other existing methods regarding detection performance while also exhibiting background suppression ability.

The few-shot object detection (FSOD) in remote sensing (RS) faces a fundamental challenge in balancing contextual feature learning with the representation stability. Current approaches either excel at capturing rich contextual relationships through multimodal architectures but struggle with catastrophic forgetting or maintain stable feature representations through incremental learning frameworks at the cost of contextual understanding. This limitation is particularly acute in RS, where complex spatial-contextual dependencies form distinctive co-occurrence patterns important for accurate object detection. We present a memory-augmented detection transformer (MemDeT) that bridges this gap through three key modules: 1) a contextual layerwise fusion (CLF) module that progressively integrates visual and textual information across transformer layers through an adaptive attention mechanism and unified cross-modal fusion, enabling both fine-grained object feature extraction at lower layers and abstract contextual relationship learning at higher layers; 2) a unified episodic memory (UEM) that serves as a dynamic knowledge repository, employing similarity-surprise-based update mechanisms with structured key-value memory organization to strategically retrieve and update relevant past experiences while preserving base knowledge through contextual scoring and dynamic retrieval; and 3) a memory-augmented decoder (MAD) that generates context-aware queries by combining current visual observations with accumulated contextual knowledge through memory-prioritized attention and progressive query refinement. Extensive experiments on RS datasets, NWPU VHR-10, DIOR, and iSAID, demonstrate that MemDeT significantly outperforms state-of-the-art methods in both contextual understanding and knowledge retention. Cross-dataset evaluation further validates MemDeT’s robust generalization capabilities, successfully transferring knowledge from DIOR to iSAID’s urban scenes, particularly in scenarios requiring strong contextual understanding with limited training examples.

Infrared small target detection is an important and core problem in infrared search and track systems. Many infrared small target detection methods work well under the premise of a static background; however, the detection effect decreases seriously when the background changes dynamically. In addition, the spatiotemporal information of the target and background of the image sequence are not fully developed and utilized, lacking long-term temporal characteristics. To solve these problems, a novel long-term spatial–temporal tensor (LSTT) model is proposed in this paper. The image registration technique is employed to realize the matching between frames. By directly superimposing the aligned images, the spatiotemporal features of the resulting tensor are not damaged or reduced. From the perspective of the horizontal slice of this tensor, it is found that the background component has similarity in the time dimension and correlation in the space dimension, which is more consistent with the prerequisite of low rank, while the target component is sparse. Therefore, we transform the problem of infrared detection of a small moving target into a low-rank sparse decomposition problem of new tensors composed of several continuous horizontal slices of the aligned image tensor. The low rank of the background is constrained by the partial tubal nuclear norm (PTNN), and the tensor decomposition problem is quickly solved using the alternating-direction method of multipliers (ADMM). Our experimental results demonstrate that the proposed LSTT method can effectively detect small moving targets against a dynamic background. Compared with other benchmark methods, the new method has better performance in terms of detection efficiency and accuracy. In particular, the new LSTT method can extract the spatiotemporal information of more frames in a longer time domain and obtain a higher detection rate.

Radar sea clutters are echoes reflected from a patch of ocean surface, which may significantly interfere with the signals from targets, and seriously degrade the performance of marine radar remote sensing. Thus, it is vital to eliminate the effects of sea clutter. In this paper, we aim at normalizing sea clutter to a uniform level. Firstly, a detailed analysis about the characteristics and differences of clutter and targets is presented; then, we present a heuristic processing scheme which works by solving the task of sea clutter normalization as a classification problem followed by energy normalization. Multiscale and speed‐up strategies are incorporated into the dynamic clustering algorithm to found a robust real‐time normalization method. Finally, extensive experiments show state‐of‐the‐art results on challenging sea clutter echoes, which demonstrate the feasibility and robustness of the proposed adaptive clustering normalization method.

This study focuses on the key challenges in detecting and estimating motion parameters of ground maneuvering targets for airborne radar sensors. The complex unknown motion states of the ground maneuvering target, including velocity, acceleration, and jerk, result in range migrations (RMs) and Doppler frequency migrations (DFMs). These effects severely degrade the long-time coherent accumulation performance of the airborne radar, thereby limiting the reliable detection and precise parameter estimation of maneuvering targets. To address this issue, a new detection and motion parameter estimation method based on the range frequency reversal transform (RFRT) and searching Lv’s distribution (SLVD), i.e., RFRT-SLVD, is proposed. Specifically, the third-order RM (TRM) and quadratic DFM (QDFM) are considered. The proposed method operates as follows: First, RMs are eliminated simultaneously via the RFRT operation, which multiplies the echo by its reversed data in the range frequency and slow-time domains, leveraging the symmetric equal-interval sampling property of the range frequency. Subsequently, a phase compensation function (PCF) related to the jerk is constructed to compensate the QDFM. Finally, the LVD is performed to remove residual DFMs and achieve effective signal energy accumulation. Additionally, the case of a fast-moving target with Doppler ambiguity is analyzed, and a method for estimating three motion parameters is provided. A key advantage of the proposed technique is its ability to directly compensate the RMs without requiring prior knowledge of the maneuvering target, while also avoiding the blind speed sidelobe (BSSL) effect. In comparison with existing algorithms, RFRT-SLVD achieves a balanced trade-off between parameter estimation performance and computational efficiency. Numerical analyses and experiments are conducted to validate the method, assessing its detection capability for ground maneuvering targets, Doppler ambiguity resolution in parameter estimation, computational complexity, and method applicability in multi-target scenarios.

High-precision drogue localization during terminal guidance is critical to close-range autonomous unmanned aerial vehicle (UAV) docking and hinges on infrared–depth (IR–D) multimodal detection. Yet, deploying such detection on airborne edge computing platforms faces severe challenges due to modal heterogeneity, feature redundancy, and real-time constraints. A lightweight IR–D fusion detection network, termed AWIE-CGAF, is proposed for airborne edge deployment, which integrates frequency-domain, physics-prior-driven input enhancement with decoupled gated attention-based adaptive feature fusion to achieve efficient multimodal detection. A training-free Adaptive Wavelet Image Enhancement (AWIE) module is designed to differentially modulate image structures and details in the frequency domain, improving the signal-to-noise ratio and feature discriminability. Concurrently, a Cross-Gated Attention Fusion (CGAF) module employs decoupled cross-modal attention with independent gating, preserving modality-specific features while dynamically selecting complementary information, mitigating redundancy and feature contamination. Experiments on the self-constructed Drogue Infrared–Depth (DIRD) dataset showed that AWIE-CGAF achieved 89.5% mAP@0.5 and 58.2% mAP@0.5:0.95 with 13.5 M parameters, while maintaining real-time inference at 51.7 FPS on a Jetson AGX Orin edge platform. Among the evaluated methods, the proposed framework achieved the highest detection accuracy while retaining real-time edge inference capability. These results support the feasibility of AWIE-CGAF for resource-constrained IR–D drogue perception.