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Different from an ordinary project, a large group mission like the unmanned aerial vehicle (UAV) swarm cooperative strike mission is performed by multiple executors and needs to be strictly carried out according to the plan. Because of the complex cooperative relationships between the sub-missions that make up a large mission, a small disturbance may cause a delay in the entire plan. Therefore, the mission process must be precisely controlled in real time to resist disturbances and ensure that the mission proceeds as planned. To address the real-time process control problem of large group missions, we propose a novel dual-code group network plan diagram model that enables plan description and process tracking for complex group missions. Additionally, a mission process closed-loop feedback control system is designed that models the mission process control problem as a mapping from the mission state observation to plan adjustment. Furthermore, an analytic-based mission process control strategy is proposed and rigorously proven to converge and be effective, as well as demonstrate the maximum anti-disturbance capability. Finally, the control strategy is tested on a UAV swarm cooperative strike mission containing 56 sub-missions. The simulation results demonstrate that the proposed control strategy is capable of achieving high, fast, and accurate control for the mission process and enhancing the anti-disturbance capability of the plan by adjusting the mission plan in real time. This will provide a valuable reference for the management of large group missions. [ABSTRACT FROM AUTHOR]

Heterogeneous image change detection is a very practical and challenging task because the data in the original image have a large distribution difference and the labeled samples of the remote sensing image are usually very few. In this study, we focus on solving the issue of comparing heterogeneous images without supervision. This paper first designs a self-paced multi-scale joint feature mapper (SMJFM) for the mapping of heterogeneous data to similar feature spaces for comparison and incorporates a self-paced learning strategy to weaken the mapper's capture of non-consistent information. Then, the difference information in the output of the mapper is evaluated from two perspectives, namely noise robustness and detail preservation effectiveness; then, the change detection problem is modeled as a multi-objective optimization problem. We decompose this multi-objective optimization problem into several scalar optimization subproblems with different weights, and use particle swarm optimization to optimize these subproblems. Finally, the robust evaluation strategy is used to fuse the multi-scale change information to obtain a high-precision binary change map. Compared with previous methods, the proposed SMJFM framework has the following three main advantages: First, the unsupervised design alleviates the dilemma of few labels in remote sensing images. Secondly, the introduction of self-paced learning enhances SMJFM's capture of the unchanged region mapping relationship between heterogeneous images. Finally, the multi-scale change information fusion strategy enhances the robustness of the framework to outliers in the original data. [ABSTRACT FROM AUTHOR]

Combat effectiveness evaluation of fighter plays an important role in the development and purchase of weapons and equipment, and in the comparison of the combat strength of both sides. An improved multi-index evaluation model based on technique for order preference by similarity to ideal solution(TOPSIS) is proposed in this article to build an air combat effectiveness evaluation model. This New model improves the shortcoming that the traditional evaluation method can only make a comprehensive evaluation of air combat effectiveness on a macro level, but cannot make a micro evaluation by comparing different aircraft strengths and weaknesses. Different index systems have been established for the over-the-horizon and dogfight air warfare tasks, and according to the defects of subjective method and objective method when evaluating the weights of indicators, the combined weighting method is constructed by using the minimum entropy principle to synthesize the two different types of weighting technologies:structural entropy weight and entropy weight. The algorithm verification shows that not only can the combined weighting method reflect the different characteristics of the index importance degree under the two line-of-sight conditions, but also the improved model can correct the evaluation errors of the original model, indicating that the method proposed in this paper is feasible and effective.

The traditional trajectory prediction models have the problems of large model simplification and less consideration. Combined with the characteristics of flight trajectory continuity, time series and interactivity, a trajectory prediction model based on bidirectional long short term memory(Bi-LSTM) neural network is proposed. The position, heading, pitch, roll and relative information of the intruder UAV are simultaneously used as the input of the trajectory prediction model, which is more in line with the true trajectory change law. The established Bi-LSTM based trajectory prediction model is trained with adaptive learning rate learning algorithm considering momentum and speed, and performed with simulation contrastive analysis with trajectory prediction model based on Elman neural network. The results show that, compared with the trajectory prediction model based on Elman neural network, the average absolute error of the proposed model predicted by 200 points in different directions is less than 4 m, the 3D prediction effect is better, and can perform the trajectory prediction more accurately.

Data mining technology is the process of extracting hidden knowledge and potentially useful information from a large number of incomplete, noisy, and random practical application data. The clustering algorithm based on multi-objective evolution has obvious advantages compared with the traditional single-objective method. In order to further improve the performance of evolutionary multi-objective clustering algorithms, this paper proposes a multi-objective automatic clustering model based on evolutionary multi-task optimization. Based on the multi-objective clustering algorithm that automatically determines the value of k, evolutionary multi-task optimization is introduced to deal with multiple clustering tasks simultaneously. A set of non-dominated solutions for clustering results is obtained by concurrently optimizing the overall deviation and connectivity index. Multi-task adjacency coding based on a locus adjacency graph was designed to encode the clustered data. Additionally, an evolutionary operator based on relevance learning was designed to facilitate the evolution of individuals within the population. It also facilitates information transfer between individuals with different tasks, effectively avoiding negative transfer. Finally, the proposed algorithm was applied to both artificial datasets and UCI datasets for testing. It was then compared with traditional clustering algorithms and other multi-objective clustering algorithms. The results verify the advantages of the proposed algorithm in clustering accuracy and algorithm convergence. [ABSTRACT FROM AUTHOR]