Unmanned aerial vehicles (UAVs) can serve as air relays to improve communication coverage by optimizing positions. UAV position optimisation is a typical nonlinear, nonconvex problem, and obtaining an analytical solution is difficult. Although swarm intelligence algorithms can effectively deal with such complex problems, they often face the challenges of complex parameter adjustment and a difficult balance between exploration and exploitation. Therefore, this paper proposes a biogeography-based optimization algorithm based on hybrid migration and mutation (BBOHMM). The algorithm dynamically combines migration rates with fitness and iteration times to enhance exploration of high-quality solutions. Meanwhile, the function of Gaussian and uniform mutation is coordinated to accelerate the solution space search in the early stage, while in the later stage, the fine exploitation is carried out around the potential optimal solutions, thereby balancing convergence speed with solution accuracy. Compared with the existing algorithms, the experimental results show that the BBOHMM can deploy UAVs more effectively and achieve a wider coverage. The test in a complex environment further verifies that the algorithm has good stability and real-time performance.

Infrared (IR) ship tracking is becoming increasingly important in various applications. However, it remains a challenging task as the information that can be obtained from infrared images is limited. Aiming at enhancing IR ship tracking accuracy, we propose an innovative approach by presenting feature integration module (FIM) and backup matching module (BMM). FIM takes appearance feature, complete intersection over union (CIoU), and motion direction metrics into account. Regarding appearance feature extraction, an end-to-end characteristic learning strategy with a cross-guided multi-granularity fusion network is proposed to obtain more integral appearance features and enhance re-identification accuracy, which helps to distinguish individual IR ship targets better. Besides, a backup matching strategy is then used to match the unmatched tracks and detections after cascaded matching. Virtual trajectories are generated for the matched tracks to optimize parameters by parameter optimization module (POM). The accumulation of errors caused by the lack of observations in the Kalman filter is reduced. Thus, the position of IR ships can be estimated more accurately, and more robust IR ship tracking can be achieved. In addition, we present a sequential frame IR ship tracking dataset, providing the first public benchmark for testing IR ship tracking performance. Experimental results indicate that the MOTA, MOTP and IDs of the proposed method are 73.441, 80.826, and 32, respectively, outperforming other state-of-the-art methods. This demonstrates the superior robustness of the proposed method, particularly when the IR ships are occluded or the target texture information is lacking. Our dataset is available at https://github.com/echo-sky/SFIST.

The inability of conventional algorithms to detect infrared (IR) ship targets in complex scenes led to the development of detection methods based on convolutional neural networks (CNNs). In this study, we propose a CNN-based stacked residual coordinate attention network (SRCANet) for detecting IR ship targets. Three-directional stacked interaction modules and a full-scale skip connection feature fusion scheme are introduced. The proposed network maintains and integrates sufficient contextual information of IR ship targets and obtains clear target boundary information. A cascaded residual coordinate attention module (CRCAM) is designed as the basic node in the SRCANet. Additionally, a residual coordinate attention module (RCAM) is introduced, which combines a two-dimensional convolution layer with batch normalisation and rectified linear unit (CBR), a coordination attention module, and a residual connection. The RCAM enhances the input feature map and improves the representability of objects of interest. The CRCAM comprises several cascading RCAMs that deepen the feature extraction layers. Furthermore, because there is no publicly available IR ship target dataset for segmentation, pixel-level annotations are performed on a set of IR ship target images and released as a single-frame IR ship detection (SISD) dataset. Extensive experiments were conducted on the SISD dataset and the widely used single-frame IR small target dataset to demonstrate the superiority of the proposed method. The results indicate that the SRCANet outperforms the state-of-the-art models, and it is more robust when target texture information is lacking. The SISD dataset is available at https://github.com/echo-sky/SISD.