Infrared ship detection technology plays a crucial role in ensuring maritime transportation and navigation safety. However, infrared ship targets at sea exhibit characteristics such as multi-scale, arbitrary orientation, and dense arrangements, with imaging often influenced by complex sea–sky backgrounds. These factors pose significant challenges for the fast and accurate detection of infrared ships. In this paper, we propose a new infrared ship target detection algorithm, YOLO-IRS (YOLO for infrared ship target), based on YOLOv10, which improves detection accuracy while maintaining detection speed. The model introduces the following optimizations: First, to address the difficulty of detecting weak and small targets, the Swin Transformer is introduced to extract features from infrared ship images. By utilizing a shifted window multi-head self-attention mechanism, the window field of view is expanded, enhancing the model’s ability to focus on global features during feature extraction, thereby improving small target detection. Second, the C3KAN module is designed to improve detection accuracy while also addressing issues of false positives and missed detections in complex backgrounds and dense occlusion scenarios. Finally, extensive experiments were conducted on an infrared ship dataset: compared to the baseline model YOLOv10, YOLO-IRS improves precision by 1.3%, mAP₅₀ by 0.5%, and mAP_50–95 by 1.7%. Compared to mainstream detection algorithms, YOLO-IRS achieves higher detection accuracy while requiring relatively fewer computational resources, verifying the superiority of the proposed algorithm and enhancing the detection performance of infrared ship targets.

In this paper, a clustering algorithm based on the MaskRCNN instance segmentation network is proposed to address the radar pulse stream clustering problem. Compared with traditional algorithms, the suggested algorithm successfully avoids the problem of preset parameters and algorithm structure constraints by using instance segmentation networks as clustering algorithms, and provides an end-to-end solution for the radar signal sorting issue. A PDW lattice mapping method is proposed to adapt to the input form of the instance segmentation network. The instance segmentation network based on ResNet, FPN, RPN, and RolAlign structure detects and masks the individual from the same or different radar signals. The simulation results show that the suggested algorithm performs the sorting of radar pulse stream under the condition of serious overlapping of radar parameters.

Convolutional neural networks (CNNs) and transformers have boosted the rapid growth of object detection in synthetic aperture radar (SAR) images. However, it is still a challenging task because SAR images usually have the characteristics of unclear contour, sidelobe interference, speckle noise, multiple scales, complex inshore background, etc. More effective feature extraction by the backbone and augmentation in the neck will bring a promising performance increment. In response, we make full use of the advantage of CNNs in extracting local features and the advantage of transformers in capturing long-range dependencies to propose a Swin Transformer-based detector for arbitrary-oriented SAR ship detection. Firstly, we incorporate a convolution-based local perception unit (CLPU) into the transformer structure to establish a powerful backbone. The local-perception-enhanced Swin Transformer (LP-Swin) backbone combines the local information perception ability of CNNs and the global feature extraction ability of transformers to enhance representation learning, which can extract object features more effectively and boost the detection performance. Then, we devise a cross-scale bidirectional feature pyramid network (CS-BiFPN) by strengthening the propagation and integration of both location and semantic information. It allows for more effective utilization of the feature extracted by the backbone and mitigates the problem of multi-scale ships. Moreover, we design a one-stage framework integrated with LP-Swin, CS-BiFPN, and the detection head of R³Det for arbitrary-oriented object detection, which can provide more precise locations for inclined objects and introduce less background information. On the SAR Ship Detection Dataset (SSDD), ablation studies are implemented to verify the effectiveness of each component, and competing experiments illustrate that our detector attains 93.31% in mean average precision (mAP), which is a comparable detection performance with other advanced detectors.

Sparse arrays have gained considerable attention in recent years because they can resolve more sources than the number of sensors. The coprime array can resolve $\mathcal{O}\left(MN\right)$ sources with only $\mathcal{O}(M+N)$ sensors, and is a popular sparse array structure due to its closed-form expressions for array configuration and the reduction of the mutual coupling effect. However, because of the existence of holes in its coarray, the performance of subspace-based direction of arrival (DOA) estimation algorithms such as MUSIC and ESPRIT is limited. Several coarray interpolation approaches have been proposed to address this issue. In this paper, a novel DOA estimation approach via direct coarray interpolation is proposed. By using the direct coarray interpolation, the reshaping and spatial smoothing operations in coarray-based DOA estimation are not needed. Compared with existing approaches, the proposed approach can achieve a better accuracy with lower complexity. In addition, an improved angular resolution capability is obtained by using the proposed approach. Numerical simulations are conducted to validate the effectiveness of the proposed approach.

The uniform linear array (ULA) based modulated wideband converter discrete compressed sampling (CS) receiver is a recently introduced sub‐Nyquist sampling scheme for effective acquisition of the carrier frequency (CF) and direction‐of‐arrival (DOA) of the received radar signal. However, the ULA based system needs to correct the phase differences of the CS data in order to estimate DOA, which would cause a low DOA estimation performance. In this study, the authors propose a modified ULA based system to improve the DOA estimation performance, where another similar branch is added in each antenna to construct the proposed symmetry system. First, the received signal is mixed to basebands. Then, the mixed signals are low‐pass filtered and down‐sampled to get the CS data. Second, the two branches of the CS data received in one antenna can be utilised to estimate CF. The CS data received in the added branches can be used to estimate DOA without correcting the phase differences. Finally, simulations illustrate the validity of the proposed modified ULA based system and show the proposed system outperforms the ULA based system in DOA estimation when signal‐to‐noise ratio is more than −10dB and the number of snapshots is more than 20.

Aiming to address the challenges of inefficient target detection in SAR images caused by complex backgrounds, small ship targets, and significant scale variations, this paper proposes a novel SAR ship target detection model, YOLO-SS, based on YOLOv10n. First, the method incorporates the Bottleneck Transformer (BoTNet) into the Backbone of the baseline network module. This enhancement enables the model to better capture global features and contextual information by establishing global relationships between image regions at different locations, thereby reducing the false and missed detection rates of SAR ship targets in complex environments. Second, an R-BiFPN feature fusion module is introduced in the Neck of the baseline network. This module deeply integrates target features with rich boundary information extracted from the shallow network and highly semantic information captured by the deep network. This fusion mechanism not only improves the model's performance in recognizing complex scenarios, such as small ship targets, fuzzy targets, and dense targets in ports, but also significantly enhances the overall accuracy, robustness, and multiscale perception capability of the model. Furthermore, by incorporating the Normalized Wasserstein Distance (NWD) and combining it with the CIoU loss function, a CIoU-NWD weighted loss function is designed. This reduces the sensitivity of the CIoU loss function to positional offsets of small targets, with only a slight increase in computational and parameter costs, thereby further improving the detection accuracy of small targets. Extensive experiments are conducted on three public datasets—SSDD, HRSID, and SARShip-Dataset—to validate the effectiveness of the proposed method. The experimental results demonstrate that, compared to the baseline model YOLOv10n, the proposed model achieves improvements of 1.2% in mAP50 and 2.1% in mAP50-95 on SSDD, 1.4% in mAP50 and 2.4% in mAP50-95 on HRSID, and 0.9% in mAP50 and 2.0% in mAP50-95 on SAR-Ship-Dataset.