In modern complex environments, achieving accurate and efficient target localization is essential in numerous fields. However, existing systems often face limitations in both accuracy and the ability to recognize small targets. In this study, we propose a bionic stabilized localization system based on CA-YOLO, designed to enhance both target localization accuracy and small target recognition capabilities. Acting as the ”brain” of the system, the target detection algorithm emulates the visual focusing mechanism of animals by integrating bionic modules into the YOLO backbone network. These modules include the introduction of a small target detection head and the development of a Characteristic Fusion Attention Mechanism (CFAM). Furthermore, drawing inspiration from the human Vestibulo- Ocular Reflex (VOR), a bionic pan-tilt tracking control strategy is developed, which incorporates central positioning, stability optimization, adaptive control coefficient adjustment, and an intelligent recapture function. The experimental results show that CA-YOLO outperforms the original model on standard datasets (COCO and VisDrone), with average accuracy metrics improved by 3.94%and 4.90%, respectively. Further time-sensitive target localization experiments validate the effectiveness and practicality of this bionic stabilized localization system.

A synthetic aperture radar (SAR) raw signal simulator is useful for supporting algorithm innovation, system scheme verification, etc. Trajectory deviation is a realistic factor that should be considered in a SAR raw signal simulator and is very important for applications such as motion composition and image formation for a SAR with nonlinear trajectory. However, existing efficient simulators become deteriorated and even invalid when the magnitude of trajectory deviation increases. Therefore, we designed an efficient SAR raw signal simulator that accounts for large trajectory deviation. Based on spatial spectrum analysis of the SAR raw signal, it is disclosed and verified that the 2D spatial frequency spectrum of the SAR raw signal is an arc of a circle at a fixed transmitted signal frequency. Based on this finding, the proposed method calculates the SAR raw signal by curvilinear integral in the 2D frequency domain. Compared with existing methods, it can precisely simulate the SAR raw signal in the case that the deviation radius is much larger. Moreover, taking advantage of the fast Fourier transform (FFT), the computational complexity of this method is much less than the time-domain ones. Furthermore, this method is applicable for multiple SAR acquisition modes and diverse waveforms and compatible with radar antenna beam width, squint angle, radar signal bandwidth, and trajectory fluctuation. Experimental results show its outstanding performance for simulating the raw signal of SAR with large trajectory deviation.

Accurate and effective radar jamming effectiveness evaluation is the key to forming the radar jamming OODA (Observe, Orient, Decide, Act) loop. The current model-driven evaluation method has low confidence and the data-driven evaluation method lacks sufficient high-quality data; thus, the evaluations that are solely model-driven or data-driven are not effective. In order to solve this problem, we propose a radar jamming effectiveness hybrid model–data-driven evaluation method. Firstly, the mechanism of the model is constructed based on the jamming equations. Secondly, the quality of training data is improved by data cleaning and model correction, after which the hybrid model is realized by training with simulated data and fine-tuning it with real-world data. Finally, the validity of the method is proved by simulation experiments, which show that the method is capable of effectively improving the accuracy of prediction and evaluation, and has good practicability. Compared with the model-driven method, the RMSE (Root Mean Square Error) of the prediction results of this method is reduced by 88.26% and the MRE (Mean Relative Error) is reduced by 92.00%.

Radar networks, composed of multiple radar stations and a fusion center interconnected via communication technologies, are widely used in civil aviation and maritime operations. Ensuring the security of radar networks is crucial. While their strong anti-jamming capabilities make traditional electronic countermeasures less effective, the openness and vulnerability of their network architecture expose them to cybersecurity risks. Current research on radar network security risk analysis from a cybersecurity perspective remains insufficient, necessitating further study to provide theoretical support for defense strategies. Taking centralized radar networks as an example, this paper first analyzes their architecture and potential cybersecurity risks, identifying a threat where attackers could potentially execute false data injection attacks (FDIAs) against the fusion center via man-in-the-middle attacks (MITMAs). A threat model is then established, outlining possible attack procedures and methods, along with defensive recommendations and evaluation metrics. Furthermore, for scenarios involving single-link control without traffic increase, the impact of different false data construction methods is examined. Simulation experiments validate the findings, showing that the average position offset increases from 8.38 m to 78.35 m after false data injection. This result confirms significant security risks under such threats, providing a reference for future countermeasure research.

In the field of modern computer vision, pedestrian detection technology holds significant importance in applications such as intelligent surveillance, autonomous driving, and robot navigation. However, single-modal images struggle to achieve high-precision detection in complex environments. To address this, this study proposes a GLNet-YOLO framework based on cross-modal deep feature fusion, aiming to improve pedestrian detection performance in complex environments by fusing feature information from visible light and infrared images. By extending the YOLOv11 architecture, the framework adopts a dual-branch network structure to process visible light and infrared modal inputs, respectively, and introduces the FM module to realize global feature fusion and enhancement, as well as the DMR module to accomplish local feature separation and interaction. Experimental results show that on the LLVIP dataset, compared to the single-modal YOLOv11 baseline, our fused model improves the mAP@50 by 9.2% over the visible-light-only model and 0.7% over the infrared-only model. This significantly improves the detection accuracy under low-light and complex background conditions and enhances the robustness of the algorithm, and its effectiveness is further verified on the KAIST dataset.

Small and dense object detection remains challenging in complex visual scenes. Repeated downsampling weakens discriminative features of tiny objects, while dense object distributions cause severe feature overlap and semantic ambiguity. To address these challenges, this paper proposes Enhanced Feature-Aware YOLO (EFA-YOLO), a lightweight detection framework based on YOLO11n. The framework establishes a collaborative optimization architecture in which feature extraction, cross-scale feature fusion, and small-object localization are jointly optimized through three tightly coupled components. The Multi-Scale Frequency-Aware Attention module uses Haar wavelet as a basic frequency decomposition tool and integrates multi-scale feature extraction, multi-directional high-frequency enhancement, frequency-aware spatial attention, and semantic-guided feature calibration to preserve discriminative information for small and dense objects. The Adaptive Weighted Concatenation Fusion module introduces learnable weighted concatenation, instead of direct weighted feature aggregation, to enhance bidirectional cross-scale feature interaction while preserving fine-grained feature information. In addition, a lightweight high-resolution detection head combines adaptive cross-layer feature fusion with redundant branch pruning to improve localization accuracy for tiny objects with limited computational overhead. Experiments on the VisDrone and COCO datasets show that, compared with YOLO11n, EFA-YOLO improves mAP 50 by 3.7% and 2.8%, respectively. Ablation studies further verify the effectiveness of each component and their collaborative contributions to overall detection performance. The proposed framework achieves a favorable balance between detection accuracy and computational efficiency, demonstrating real-time performance on the evaluated GPU platform and indicating potential for further edge-oriented optimization.