In this work, we present a novel method to intention recognition, based on electroencephalogram (EEG) and eye movement in human-computer interaction(HCI). The fusion of EEG and eye movement will allow the utmost of the advantages of the two physiological signals. Signals of EEG and eye movement were collected for feature extraction, recognition network of machine learning pattern was input for intent recognition, final recognition result was attained by decision-level fusion. We compare the results of the Intention Recognition Algorithms to those of an experiment involving the intention recognition of the operator in a simulated flight mission. In most every case, results show that the intention recognition algorithms performed better than solely rely on single signal.

This study addresses the challenge of intelligent decision‐making for command‐and‐control systems in air defense combat operations. Current autonomous decision‐making systems suffer from limited rationality and insufficient intelligence during operation processes. Recent studies have proposed methods based on deep reinforcement learning (DRL) to address these issues. However, DRL methods typically face challenges related to weak interpretability, lack of convergence guarantees, and high‐computing power requirements. To address these issues, a novel technique for large‐scale air defense decision‐making by combining a DRL technique with game theory is discussed. The proposed method transforms the target assignment problem into a potential game that provides theoretical guarantees for Nash equilibrium (NE) from a distributed perspective. The air‐defense decision problem is decomposed into separate target selection and target assignment problems. A DRL method is used to solve the target selection problem, while the target assignment problem is translated into a target assignment optimization game. This game is proven to be an exact potential game with theoretical convergence guarantees for an NE. Having simulated the proposed decision‐making method using a digital battlefield environment, the effectiveness of the proposed method is demonstrated.

Intelligent decision‐making in air defense operations has attracted wide attention from researchers. Facing complex battlefield environments, existing decision‐making algorithms fail to make targeted decisions according to the hierarchical decision‐making characteristics of air defense operational command and control. What’s worse, in the process of problem‐solving, these algorithms are beset by defects such as dimensional disaster and poor real‐time performance. To address these problems, a new hierarchical reinforcement learning algorithm named Hierarchy Asynchronous Advantage Actor‐Critic (H‐A3C) is developed. This algorithm is designed to have a hierarchical decision‐making framework considering the characteristics of air defense operations and employs the hierarchical reinforcement learning method for problem‐solving. With a hierarchical decision‐making capability similar to that of human commanders in decision‐making, the developed algorithm produces many new policies during the learning process. The features of air situation information are extracted using the bidirectional‐gated recurrent unit (Bi‐GRU) network, and then the agent is trained using the H‐A3C algorithm. In the training process, the multihead attention mechanism and the event‐based reward mechanism are introduced to facilitate the training. In the end, the proposed H‐A3C algorithm is verified in a digital battlefield environment, and the results prove its advantages over existing algorithms.

The driving cycle is a speed-to-time curve, a fundamental technique in the automotive industry, and also a basis to set standards for fuel consumption and emissions of vehicles. A driving cycle is developed based on firsthand driving data collected from fieldwork. First, bad data in the original dataset are preprocessed, the time-series standard smoothing algorithm is used to smoothen the data, and Lagranges interpolation is used to realize data interpolation. Next, the rules for kinematic fragment extraction are set to divide the data into kinematic fragments. Last, an evaluation system of kinematic fragment feature parameters is built. On that basis, the K-means clustering method is used to cluster the dimensionally reduced data, and the adaptive mutation particle swarm optimization (AMPSO) algorithm is employed to select the optimal fragments from candidate fragments to develop a driving cycle. The experiment result shows that the developed driving cycle can represent the kinematic features of the experiment car and provides a basis for the development of a driving cycle for Fuzhou.

Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decision-making, such as large computational amount, slow solution speed, and low calculation accuracy, combined with deep reinforcement learning theory, an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed, which uses a double noise mechanism to expand the search range of the action, and introduces a priority experience playback mechanism to effectively achieve data utilization. Finally, the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield. The results of the experiment show that, under the framework of the deep neural network for intelligent weapon-target assignment proposed in this paper, compared to the traditional RELU algorithm, the agent trained with reinforcement learning algorithms, such as Deep Deterministic Policy Gradient algorithm, Asynchronous Advantage Actor-Critic algorithm, Deep Q Network algorithm performs better. It shows that the use of deep reinforcement learning algorithms to solve the weapon-target assignment problem in the field of air defense operations is scientific. In contrast to other reinforcement learning algorithms, the agent trained by the improved Deep Deterministic Policy Gradient algorithm has a higher win rate and reward in confrontation, and the use of weapon resources is more efficient. It shows that the model and algorithm have certain superiority and rationality. The results of this paper provide new ideas for solving the problem of weapon-target assignment in air defense combat command decisions.

Intelligent wars can take place not only in the physical domain and information domain but also in the cognitive domain. The cognitive domain will become the key domain to win in the future intelligent war. A Lanchester equation considering cognitive domain is proposed to fit the development tendency intelligent wars in this paper. One party is considered to obtain the exponential enhancement advantage on combat forces in combat if it can gain an advantage in the cognitive domain over the other party according to the systemic advantage function. The operational effectiveness of the cognitive domain in war is considered to consist of a series of indicators. Hesitant fuzzy sets and linguistic term sets are powerful tools when evaluating indicators, hence the indicators are scored by experts using hesitant fuzzy linguistic terms sets here. A unique hesitant fuzzy hybrid arithmetical averaging operator is used to aggregate the evaluation.