When tracking a unmanned aerial vehicle (UAV) in complex backgrounds, environmental noise and clutter often obscure it. Traditional radar target tracking algorithms face multiple limitations when tracking a UAV, including high vulnerability to target occlusion and shape variations, as well as pronounced false alarms and missed detections in low signal-to-noise ratio (SNR) environments. To address these issues, this paper proposes a UAV detection and tracking algorithm based on a low-frequency communication network. The accuracy and effectiveness of the algorithm are validated through simulation experiments using field-measured point cloud data. Additionally, the key parameters of the algorithm are optimized through a process of selection and comparison, thereby improving the algorithm's precision. The experimental results show that the improved algorithm can significantly enhance the detection and tracking performance of the UAV under high clutter density conditions, effectively reduce the false alarm rate and markedly improve overall tracking performance metrics.

With the rapid growth of connected devices, traditional edge-cloud systems are under overload pressure. Using mobile edge computing (MEC) to assist unmanned aerial vehicles (UAVs) as low altitude platform stations (LAPS) for communication and computation to build air-ground integrated networks (AGINs) offers a promising solution for seamless network coverage of remote internet of things (IoT) devices in the future. To address the performance demands of future mobile devices (MDs), we proposed an MEC-assisted AGIN system. The goal is to minimize the long-term computational overhead of MDs by jointly optimizing transmission power, flight trajectories, resource allocation, and offloading ratios, while utilizing non-orthogonal multiple access (NOMA) to improve device connectivity of large-scale MDs and spectral efficiency. We first designed an adaptive clustering scheme based on K-Means to cluster MDs and established communication links, improving efficiency and load balancing. Then, considering system dynamics, we introduced a partial computation offloading algorithm based on multi-agent deep deterministic policy gradient (MADDPG), modeling the multi-UAV computation offloading problem as a Markov decision process (MDP). This algorithm optimizes resource allocation through centralized training and distributed execution, reducing computational overhead. Simulation results show that the proposed algorithm not only converges stably but also outperforms other benchmark algorithms in handling complex scenarios with multiple devices.

Low earth orbit (LEO) satellite communication which can provide global wireless service plays a critical role in the future wireless communication networks. However, due to the high speed of satellite motion, numerous narrow beams, and complex satellite-terrestrial channels, the initial access between the LEO satellites and user terminals (UEs) becomes more complicated. To establish a stable link, a beam search is required between the satellite and the UE. However, traditional beam search methods (e.g., exhaustive search) have high time complexity which is not suitable in high-speed scenarios. Therefore, in this paper, a sensing-aided hierarchical beam search method is proposed, which is performed in two stages. In the first stage, wide beam scanning is performed to find the optimal angular range. In the second stage, after determining the directions of narrow beams via sensing the direction of arrival (DOA) of satellite signals, the narrow beams generated at estimated directions are used to sweep the satellite beams. This method can help fast beam alignment and obtain high beam search accuracy, which is verified by simulation results. Moreover, we analyze the gain of beam alignment from the two-stage beam search method.

In response to challenges posed by complex backgrounds, diverse target angles, and numerous small targets in remote sensing images, alongside the issue of high resource consumption hindering model deployment, we propose an enhanced, lightweight you only look once version 8 small (YOLOv8s) detection algorithm. Regarding network improvements, we first replace traditional horizontal boxes with rotated boxes for target detection, effectively addressing difficulties in feature extraction caused by varying target angles. Second, we design a module integrating convolutional neural networks (CNN) and Transformer components to replace specific C2f modules in the backbone network, thereby expanding the model’s receptive field and enhancing feature extraction in complex backgrounds. Finally, we introduce a feature calibration structure to mitigate potential feature mismatches during feature fusion. For model compression, we employ a lightweight channel pruning technique based on localized mean average precision (LMAP) to eliminate redundancies in the enhanced model. Although this approach results in some loss of detection accuracy, it effectively reduces the number of parameters, computational load, and model size. Additionally, we employ channel-level knowledge distillation to recover accuracy in the pruned model, further enhancing detection performance. Experimental results indicate that the enhanced algorithm achieves a 6.1% increase in mAP50 compared to YOLOv8s, while simultaneously reducing parameters, computational load, and model size by 57.7%, 28.8%, and 52.3%, respectively.

Passive bistatic radar (PBR) frequently experiences interference from direct signal waves when detecting maritime targets, which can completely mask target echoes, particularly for distant targets or weak targets with low radar cross-section (RCS). To mitigate this, the paper proposes a direct signal interference (DSI) suppression method. The approach involves dual-channel reception of digital video broadcast satellites (DVB-S) signals from the China Sat-9, followed by signal preprocessing. The reference and surveillance channel signals are then segmented. After segmentation, the signals undergo fast Fourier transformation (FFT), and an adaptive filtering clutter suppression method is applied at each frequency point. Finally, an inverse fast Fourier transform (IFFT) is performed on the suppressed signals to obtain the DSI-suppressed output. Compared to traditional clutter suppression techniques, this method is not only faster but also achieves more effective suppression. Simulation experiments involving both single and multiple targets validate the superiority of the proposed algorithm.

In this study, a solution based on deep Q network (DQN) is proposed to address the relay selection problem in cooperative non-orthogonal multiple access (NOMA) systems. DQN is particularly effective in addressing problems within dynamic and complex communication environments. By formulating the relay selection problem as a Markov decision process (MDP), the DQN algorithm employs deep neural networks (DNNs) to learn and make decisions through real-time interactions with the communication environment, aiming to minimize the system’s outage probability. During the learning process, the DQN algorithm progressively acquires channel state information (CSI) between two nodes, thereby minimizing the system’s outage probability until a stable level is reached. Simulation results show that the proposed method effectively reduces the outage probability by 82% compared to the two-way relay selection scheme (Two-Way) when the signal-to-noise ratio (SNR) is 30 dB. This study demonstrates the applicability and advantages of the DQN algorithm in cooperative NOMA systems, providing a novel approach to addressing real-time relay selection challenges in dynamic communication environments.

Poly (m-phenylene isophthalamide) (PMIA), a key aromatic polyamide, is widely used for its outstanding mechanical strength, high thermal stability, and excellent insulation properties. However, different applications demand varying dielectric properties, so tailoring its dielectric performance is essential. PMIA was first synthesized in this study, followed by introducing pores and developing porous PMIA films and PMIA-based composites with reduced dielectric constants. Porous PMIA films were fabricated using the wet phase inversion process with N, N-dimethylacetamide (DMAC) solvent and water as the non-solvent. The impact of casting solution composition and coagulation bath temperature on pore structures was analyzed. A film produced with 18% PMIA and 5% LiCl in a 35 ℃ coagulation bath achieved the lowest dielectric constant of 1.76 at 1 Hz, 48% lower than the standard PMIA film, which had a tensile strength of 18.5 MPa and an initial degradation temperature of 320 ℃.

This paper investigates the power generation characteristics of solar cells mounted on unmanned aerial vehicles (UAVs) under the coupled influence of flight conditions and the surrounding environment. Firstly, the study reveals that the voltage, current, and power output of the solar cells undergo consistent temporal variations throughout the day, primarily driven by voltage fluctuations, with a peak occurring around noon. Secondly, it is observed that the cells’ performance is significantly more influenced by temporal variations in external light intensity than by temperature changes resulting from variations in flight speed. Finally, the study finds that the impact of flight altitude on the cells’ performance is slightly more pronounced than the influence of temporal variations in external light intensity.