This article proposes an 8×8 high-efficiency low-sidelobe slot antenna array operating at Ka-band frequencies with a feeding network designed by a new printed ridge gap waveguide (PRGW). The device consists of a slot antenna element, a PRGW unequal feeding network and a strip-line to PRGW transition. The substrate integrated waveguide (SIW) slot antenna element fed by the PRGW through a coupling aperture has a wide bandwidth, and the isolation is also increased by adding metallized holes to the array. Compared with other gap waveguide (GWG) antenna arrays, our work realizes an antenna element fed separately due to the use of a PRGW with a compact mushroom cell size, which helps improve the properties of the unequal feeding network. In addition, the transition between the PRGW and strip-line is also designed with a wide band and low insertion loss. The measured reflection coefficient is less than -10 dB over a 6 GHz bandwidth (16%), the gain is more than 23.5 dBi in the working band, and the sidelobe level is -18 dB. In addition, the measured radiation efficiency of the array with a two-dimensional Taylor distribution is 41.69%.

To address the challenge of accurately locating unmanned aerial vehicles (UAVs) in situations where radar tracking is not feasible and visual observation is difficult, this paper proposes an innovative acoustic source localization method based on improved Empirical Mode Decomposition (EMD) within an adaptive frequency window. In this study, the collected flight signals of UAVs undergo smoothing filtering. Additionally, Robust Empirical Mode Decomposition (REMD) is applied to decompose the signals into Intrinsic Mode Function (IMF) components for spectrum analysis. We introduce a sliding frequency window with adjustable bandwidth, which is automatically determined using a Grey Wolf Optimizer (GWO) with a sliding index. This window is used to lock and extract specific frequencies from the IMFs. Based on predefined criteria, the extracted IMF components are reconstructed, and trigger signal times are analyzed and recorded from these reconstructed IMFs. The time differences between sensor receptions are then calculated. Furthermore, this study introduces the Chan–Taylor localization algorithm based on weighted least squares. This advanced algorithm takes sensor time delay parameters as input and solves a set of nonlinear equations to determine the target’s location. Simulations and real-world signal tests are used to validate the robustness and performance of the proposed method. The results indicate that the localization error remains below 5% within a 15 m × 15 m measurement area. This provides an efficient and real-time method for detecting the location of small UAVs.

With the popularity of low-altitude small unmanned aerial vehicles (UAVs), UAVs are often used to take candid photos or even carry out malicious attacks. Acoustic detection can be used to locate UAVs in order to prevent malicious attacks by UAVs. Aiming at the problem of a large error in the time delay estimation algorithm under a low SNR, a time delay estimation algorithm based on an improved weighted function combined with a generalized cubic cross-correlation is introduced. By analyzing and comparing the performance of generalized cross-correlation time delay estimation of different traditional weighting functions, an improved weighting function that combines improved smooth coherent transform (SCOT) and phase transform (PHAT) is proposed. Compared with the traditional generalized cross-correlation weighted function, the improved weighted function has a sharper and higher peak value, and the time delay estimation error is smaller at a low SNR. Secondly, by combining the improved weight function with the generalized cubic cross-correlation, the main peak value is further increased and sharpened, and the time delay estimation performance is better than that when combined with the generalized cubic cross-correlation and the generalized quadratic correlation. Experimental results show that in complex outdoor scenes, the positioning error of the unimproved GCC PHAT method is 45.22 cm, and the positioning error of the improved weighted function generalized cubic cross-correlation algorithm is no more than 22.1 cm. Compared with the unimproved GCC PHAT method, the performance is improved by 35.55%. It is proven that this method is helpful for improving the positioning ability of low-flying UAVs and can provide help for anti-terrorism security against malicious attacks by UAVs.

A novel three-dimensional integrated guidance and control (3D-IGC) approach is developed for sliding-to-turn (STT) missile, which achieves an asymptotic tracking performance with various disturbances. First, a nonlinear state-space model is established to design the 3D-IGC law. Then a control approach based on improved robust integral of the sign of error (RISE) is proposed to guarantee both the angle and angular rates of look of sight (LOS) to be tightly tracked. In addition, a novel error transformation is introduced so as to transmit the mismatched modeling uncertainties to control input channel. Then the improved RISE can handle both mismatched and matched modeling uncertainties together in the design of controller. The closed-loop system stability and the convergence characteristic are rigorously proved via Lyapunov theory. Finally, extensive contrast simulations are implemented to demonstrate the effectiveness and robustness of the proposed control strategy.

High-precision and resilient indoor positioning is a fundamental requirement for the autonomous operation of mobile robots in GNSS-denied environments. While visual sensors are commonly used for odometry, their operational reliability can be compromised in challenging scenarios such as drastic illumination fluctuations and sparse-textured environments. To address these sensor limitations, this study presents MVO, a magneto-visual odometry framework that explores indoor magnetic field anomalies as complementary constraints for visual odometry. By integrating a 30-magnetometer planar array model with a stereo camera, the proposed system establishes a multi-modal perception framework for indoor spaces. In the frontend, magnetic field gradient information is utilized to provide relative-pose constraints, which assist in the matching of image feature points and help maintain tracking continuity under visual degradation. In the backend, a factor graph optimization (FGO) framework incorporates magnetic relative-pose factors and visual reprojection factors into a unified optimization objective, which is then solved using the incremental smoothing and mapping 2 (iSAM2) algorithm. Frontend-level simulations are conducted to analyze the effects of magnetometer spatial configuration, sensor number, and calibration-error sensitivity on magnetic relative-pose estimation and covariance. Trajectory-level evaluations are further performed on the EuRoC dataset augmented with high-fidelity synthesized magnetic field data, including localization accuracy and computational load. Under this synthesized magnetic field setting, MVO shows improved localization accuracy and moderate computational load compared with the selected MSCKF-Stereo and VINS-Fusion reference baselines. These results provide a simulation-based feasibility validation of integrating magnetic field constraints with visual information for indoor odometry, while validation with real magnetometer array measurements remains future work.

To support the early identification of potential drowning-risk states and improve rescue response efficiency, this paper proposes a UAV-oriented dual-scale detection-pose cascade for frame-level drowning-risk recognition. The framework first performs high-recall preliminary detection on wide-field input images to identify potential drowning targets. The detected target regions are subsequently extracted and resized to construct localized inputs for the second-stage pose-based verification. This software-based target-region refinement simulates the localized high-resolution observation that could be provided by a telephoto camera in a future physical dual-camera UAV implementation. By focusing subsequent analysis on the localized target regions, the second-stage pose model can exploit finer-scale human structural information for drowning-risk state verification, thereby providing decision support for potential drowning detection. To address the challenges of small target scales and severe background interference in wide-field images, a lightweight YOLOv8n-based detection model is developed. An enhanced edge-feature-guided residual convolutional block attention module (EGRCBAM) is introduced, together with a recall-oriented FPIoU loss function designed for hard sample optimization, improving the recall of the drowning category by 17%. For localized target verification, an enhanced YOLOv8n-Pose model is constructed by incorporating a coordinate-aware pose head, spatial attention mechanism, and skeletal structure constraints to enhance human-region localization and pose-based drowning-versus-swimming recognition. The model improves Box mAP@0.5 from 0.768 to 0.816. Comparative experiments on the self-collected dataset demonstrate the effectiveness of the proposed detection and pose-based verification framework. The proposed framework provides a lightweight vision-based solution or UAV-oriented frame-level drowning-risk recognition and offers a potential algorithmic basis for future integration with physical dual-camera UAV platforms.