With the rapid growth of the global drone market, a variety of small drones have posed a certain threat to public safety. Therefore, we need to detect small drones in a timely manner so as to take effective countermeasures. At present, the method based on deep learning has made a great breakthrough in the field of target detection, but it is not good at detecting small drones. In order to solve the above problems, we proposed the IRSDD-YOLOv5 model, which is based on the current advanced detector YOLOv5. Firstly, in the feature extraction stage, we designed an infrared small target detection module (IRSTDM) suitable for the infrared recognition of small drones, which extracted and retained the target details to allow IRSDD-YOLOv5 to effectively detect small targets. Secondly, in the target prediction stage, we used the small target prediction head (PH) to complete the prediction of the prior information output via the infrared small target detection module (IRSTDM). We optimized the loss function by calculating the distance between the true box and the predicted box to improve the detection performance of the algorithm. In addition, we constructed a single-frame infrared drone detection dataset (SIDD), annotated at pixel level, and published an SIDD dataset publicly. According to some real scenes of drone invasion, we divided four scenes in the dataset: the city, sky, mountain and sea. We used mainstream instance segmentation algorithms (Blendmask, BoxInst, etc.) to train and evaluate the performances of the four parts of the dataset, respectively. The experimental results show that the proposed algorithm demonstrates good performance. The $A{P}_{50}$ measurements of IRSDD-YOLOv5 in the mountain scene and ocean scene reached peak values of 79.8% and 93.4%, respectively, which are increases of 3.8% and 4% compared with YOLOv5. We also made a theoretical analysis of the detection accuracy of different scenarios in the dataset.

Images acquired by different sensors exhibit different characteristics because of the varied imaging mechanisms of sensors. The fusion of visible and infrared images is valuable for specific image applications. While infrared images provide stronger object features under poor illumination and smoke interference, visible images have rich texture features and color information about the target. This study uses dual optical fusion as an example to explore fusion detection methods at different levels and proposes a multimodal decision-level fusion detection method based on category probability sets (CPROS). YOLOv8—a single-mode detector with good detection performance—was chosen as the benchmark. Next, we innovatively introduced the improved Yager formula and proposed a simple non-learning fusion strategy based on CPROS, which can combine the detection results of multiple modes and effectively improve target confidence. We validated the proposed algorithm using the VEDAI public dataset, which was captured from a drone perspective. The results showed that the mean average precision (mAP) of YOLOv8 using the CPROS method was 8.6% and 16.4% higher than that of the YOLOv8 detection single-mode dataset. The proposed method significantly reduces the missed detection rate (MR) and number of false detections per image (FPPI), and it can be generalized.

Side-scan sonar systems play an important role in tasks such as marine terrain exploration and underwater target identification. Target segmentation of side-scan sonar images is an effective method of underwater target detection. However, the principle of side-scan sonar systems leads to high noise interference, weak boundary information, and difficult target feature extraction of sonar images. To solve these problems, we propose a Double Split Attention (DSA) SOLO. Specially, we present an efficient attention module called DSA which fuses spatial attention and channel attention together effectively. DSA first splits feature maps into two parts along channel dimensions before processing them in parallel. Next, DSA utilizes C-S Unit and S-C Unit to describe relevant features in the spatial and channel dimensions, respectively. After that, the results of the two parts are aggregated to improve feature representation. We embedded the proposed DSA module after the FPN network of SOLOv2, and this approach improves the instance segmentation accuracy to a great extent. Experimental results show that our proposed DSA-SOLO on SCTD dataset achieves 78.4% mAP.5, which is 5.1% higher than SOLOv2.

Distributed beamforming has emerged as a pivotal technology in modern communication systems, radar applications, and collaborative electronic warfare. Through distributed collaboration, these jamming nodes composed of unmanned aerial vehicles (UAVs) can execute flexible electronic jamming toward wireless communication systems despite energy limitations. However, they encounter challenges such as time and positioning inaccuracies, which can diminish the synthesis power of jamming beams and reduce jamming effectiveness. This paper examines the impact of angle and distance errors on jamming effectiveness, deriving upper error bounds under various suppression coefficients. The findings offer practical guidelines for enhancing the precision and effectiveness of distributed beamforming systems in electronic warfare applications.

Imaging sonar systems play an important role in underwater target detection and location. Due to the influence of reverberation noise on imaging sonar systems, the task of sonar target segmentation is a challenging problem. In order to segment different types of targets in sonar images accurately, we proposed the gated fusion-pyramid segmentation attention (GF-PSA) module. Specifically, inspired by gated full fusion, we improved the pyramid segmentation attention (PSA) module by using gated fusion to reduce the noise interference during feature fusion and improve segmentation accuracy. Then, we improved the SOLOv2 (Segmenting Objects by Locations v2) algorithm with the proposed GF-PSA and named the improved algorithm Attentive SOLO. In addition, we constructed a sonar target segmentation dataset, named STSD, which contains 4000 real sonar images, covering eight object categories with a total of 7077 target annotations. The experimental results show that the segmentation accuracy of Attentive SOLO on STSD is as high as 74.1%, which is 3.7% higher than that of SOLOv2.

Unmanned Aerial Vehicles (UAVs) are widely used in defense applications. Multiple UAVs equipped with jamming sources can form a sparse array. The sparse array can be quickly deployed and achieve an extensive range of effective jamming. At present, the jamming power in the target area is mainly calculated through the superposition of node waveforms. The algorithm needs to sequentially calculate the angle of the target position relative to each node to obtain the corresponding gain and then calculate the path loss by the transmission model to obtain the total jamming power, which has high algorithmic complexity and needs to recount the power every time after adjusting the position of the node. An equivalent transmitted power estimation algorithm based on the pattern multiplication theorem is proposed in this paper, which regards the sparse array as a whole directional jammer. The power in the target area can be estimated according to the jammer gain, the equivalent transmitted power, and the jamming distance. In the calculation of jamming power, the proposed array-based algorithm reduces the complexity by 50% compared with the waveform superposition algorithm, and the estimation variance of the jamming power is less than 1.4%.