Under the influence of variable sea conditions, a ship will have an oscillating motion comprising six degrees of freedom, all of which are connected to each other. Among these degrees of freedom, rolling and pitching motions have a severe impact on a ship’s maritime operations. An accurate and effective ship motion attitude prediction method that makes the prediction in a short period of time is required to guarantee the safety and stability of the ship’s maritime operations. Traditional methods are based on time domain analysis, such as the autoregressive moving average (ARMA) models. However, these models have limitations when it comes to predicting the nonlinear and nonstationary characteristics of real ship motion attitude data. Many intelligent algorithms continue to be applied in nonlinear and nonstationary ship attitude prediction, such as extreme learning machines (ELMs) and the long short-term memory (LSTM) neural network, as well as other deep learning methods, showing promising results. By using the sliding window approach, the time-varying dynamic characteristics of the ship’s motion attitude can be preserved better. The simulation results demonstrate that the proposed model performs well in terms of predicting the nonlinear and nonstationary ship motion attitude.

With the development of robotics, the Multiple Mobile Robots System (MMRS) has been an attention-getting subject in recent years and has been widely used in military, civilian and other fields. Cooperative localization is the key technology of the MMRS. And observability analysis for MMRS and nonlinear problem are the foremost problems to cooperative localization. In order to enhance the accuracy of cooperative localization for MMRS, a novel cooperative localization algorithm based on hybrid topology architecture under the framework of Extended Kalman Filter (EKF) is proposed in this paper. In this novel algorithm, a hybrid topology architecture based on the Relative Position Measurement Gragh (RPMG) method is adopted to enhance the usage efficiency of observations, improving the cooperative localization accuracy. Meanwhile, EKF is used in this novel cooperative localization algorithm to solve the nonlinearity of the MMRS. Finally, a real dataset is used to verify the availability and superiority of this novel algorithm.

The method proposed in this letter for synthetic aperture radar (SAR) image classification has two main stages. In the first stage, a convolutional neural network (CNN) is trained for normal SAR image classification task. After training, the sample features can be obtained by extracting the output of middle layer in the forward propagation process of CNN. In the second stage, an end-to-end metric network is trained to measure the relations between sample features. The method proposed in this letter is tested with some of the larger targets in OpenSARShip data set which is collected from Sentinel-1 satellite, and it is also tested with the MSTAR data set which is created by the U.S. Air Force Laboratory. The experimental results show that our method can get a higher recognition accuracy than normal CNN structure.

The demand for heterogeneous unmanned aerial vehicles (UAVs) equipped with various types and complementary resources to perform complex tasks is increasing. Existing researches on game theory-based resource allocation primarily focus on continuous resources, by establishing models and transforming them into convex optimization problems. However, the discrete task resource allocation problem considered in this article does not meet the assumptions of continuous optimization models, therefore, a reasonable allocation method is needed. To enhance the allocation efficiency for multi-UAVs operating under discrete resource constraints, we design a heuristic task allocation method based on game theory. Our method integrates multiple critical factors, including task priority and dynamic task benefits, into the resource matching process, offering a robust solution tailored to the diverse demands. Specifically, we first establish an optimization model encompassing UAVs, requirements and revenue, then we adopt a utility function to depict these relationships. Furthermore, considering the distributed cooperative characteristics of UAVs during task execution, we incorporate the correspondence between UAVs and tasks into the framework of a coalition formation game (CFG). Building on this foundation, we develop a heuristic allocation method based on task validity matching and an ineffective resource exit mechanism. We also introduce tabu lists to avoid ineffective searches, aiming to enhance resource utilization while ensuring task utility. Finally, we analyze the convergence of our proposed algorithm and conduct simulation validations across various scenarios. The results indicate that our algorithm can adapt to dynamic changes in task demands, exhibits good scalability, and is capable of enhancing task utility.

Positioning based on LEO opportunistic signals can serve as a supplement for the global satellite navigation system, especially in areas where the global satellite navigation signal is denied. The previous positioning method based on LEO opportunistic signals has shown that it is difficult to obtain an accurate initial solution in environments without positioning reference information, which leads to large positioning errors and even failure to converge to the correct solution. To address this issue, a comprehensive particle swarm optimization Doppler shift correction (PSODC) algorithm is proposed in this article to obtain an initial coarse solution. Based on this initial coarse solution, the obtained satellite orbit spatiotemporal information is corrected to reduce the errors caused by satellite orbit spatiotemporal discrepancies. Both simulation and experiment results show the effectiveness of the PSODC method compared with the existing methods. The horizontal positioning accuracy is improved by more than 30% with the assistance of an altimeter. The novel positioning method proposed in this article is of great significance for obtaining the initial solution of Iridium NEXT satellites and improving the positioning accuracy of opportunistic signals.