The overview for UAV Air-Combat Decision method

Fu, Li; Xie, Fuhuai; Wang, Dongzheng; Meng, Guang

doi:10.1109/ccdc.2014.6852760

Cited by 26 publications

(11 citation statements)

References 15 publications

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“…From a macro point of view, air combat decision making refers to one party in air combat providing corresponding control instructions to fighter jets after analyzing and judging battlefield information so that it can complete the dominant attack position occupying the enemy. Decision making is the core of air combat, and its rationality will determine the final outcome of air combat [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Research on UCAV Maneuvering Decision Method Based on Heuristic Reinforcement Learning

Wang

Zhang

Rong

et al. 2022

Computational Intelligence and Neuroscience

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With the rapid development of unmanned combat aerial vehicle (UCAV)-related technologies, UCAVs are playing an increasingly important role in military operations. It has become an inevitable trend in the development of future air combat battlefields that UCAVs complete air combat tasks independently to acquire air superiority. In this paper, the UCAV maneuver decision problem in continuous action space is studied based on the deep reinforcement learning strategy optimization method. The UCAV platform model of continuous action space was established. Focusing on the problem of insufficient exploration ability of Ornstein–Uhlenbeck (OU) exploration strategy in the deep deterministic policy gradient (DDPG) algorithm, a heuristic DDPG algorithm was proposed by introducing heuristic exploration strategy, and then a UCAV air combat maneuver decision method based on a heuristic DDPG algorithm is proposed. The superior performance of the algorithm is verified by comparison with different algorithms in the test environment, and the effectiveness of the decision method is verified by simulation of air combat tasks with different difficulty and attack modes.

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Section: Introductionmentioning

confidence: 99%

Research on UCAV Maneuvering Decision Method Based on Heuristic Reinforcement Learning

Wang

Zhang

Rong

et al. 2022

Computational Intelligence and Neuroscience

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“…Unmanned aerial vehicles (UAVs) have attracted research interest on account of their simple structures, low price, high mobility, and high flexibility. Due to their significant advantages, they have broad application prospects in military [1] and civilian [2]. However, the nonlinear characteristics and strong coupling of UAVs increase the difficulty of UAV control [3].…”

Section: Introductionmentioning

confidence: 99%

Full-State-Constrained Adaptive Control for a Class of UAVs Suffering from Coupled Uncertainties Using the HOBLF

Chang

Wang

Chen

et al. 2021

Mathematical Problems in Engineering

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Nowadays, the practical tasks of UAVs are becoming more and more complicated and diversified. In the practical flight process, the large-scale changes of the flight environment, the modeling errors, and the external disturbances may induce the instability of the UAV flight system. Meanwhile, the constraints of the UAV attitudes also have to be guaranteed during the flight process. However, most existing control methods still have limitations in handling the constraints and the multisource disturbances simultaneously. To address this problem, in this paper, we focus on the actual output tracking control for the UAV systems with full-state constraints and multisource disturbances. Firstly, a high-order tan-type barrier Lyapunov function (HOBLF) has been constructed for the UAV to maintain the full-state constraints. Secondly, by combining the adaptive backstepping technique and the fuzzy logic systems, the modeling errors and the unknown nonlinearities of the UAV attitude control system can be handled. Moreover, by properly constructing several adaptive laws, the time-varying disturbances existing in the UAV attitude control system can be suppressed. Finally, the full-state-constrained antidisturbance controller is formed, ensuring that the tracking error approaches arbitrarily to small neighborhood and does not violate the given constraints. The simulation results illustrate the feasibility and the advantages of the proposed method.

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“…Advantage matrix -Building the advantage matrix which represents the preference of behaviors -Easy to interpret -High cost for building matrix [4], [5] Genetic algorithm -Exploring the solution space to improve particular objectives -Long time to obtain solutions -Local optimum [6], [7], [8], [9] Reinforcement learning -Finding an optimal policy that maximize total future reward -Global optimum -Difficult to define reward for each behavior [10], [11], [12] Matrix factorization -Estimating latent factors that explain the implcit attributes of state features -Latent factor -Robust to the data sparseness [13] timal behavior by using a predefined situation-behavior (SB) matrix [4], [5], [14]. The results obtained by the methods are easy to interpret because optimal behavior is identified by comparing values of elements in the given AM.…”

Section: Introductionmentioning

confidence: 99%

“…Although GA did not require building a matrix, chromosome and crossover/mutation schemes should be predefined to perform the algorithm [8], [16]. [14]. One of the limitations with this method is that the schemes adjusted for a specific domain are often challenging to apply to similar problems [9].…”

Section: Introductionmentioning

confidence: 99%

A Behavior Optimization Method for Unmanned Combat Aerial Vehicles Using Matrix Factorization

et al. 2020

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One of the fundamental technologies for unmanned combat aerial vehicles and combat simulators is behavior optimization, which finds a behavior that maximizes the probability of winning a battle. With the advent of military science, combat logs became available, allowing machine learning algorithms to be used for the behavior optimization. Due to implicit attributes such as the experience of an operator that are not explicitly presented in log data, existing methods for behavior optimization have limitations in performance improvement. Furthermore, specific behaviors occur with low frequency, resulting in a dataset with imbalanced and empty values. Therefore, we apply a matrix factorization (MF) method, which is one of latent factor models and known for sophisticated imputation of empty values, to the behavior optimization problem of unmanned combat aerial vehicles. A situation-behavior matrix, whose elements are ratings indicating the optimality of behaviors in situations, is defined to implement the MF based method. Experiments for performance comparison were conducted on combat logs, in which the proposed method yielded satisfactory results. INDEX TERMS behavior optimization, unmanned vehicle, matrix factorization, reinforcement learning, situation-behavior matrix ABBREVIATIONS AM Advantage matrix. FOV Field of view. GA Genetic algorithm. LOS Line of sight. MF Matrix factorization. nDCG Normalized discounted cumulative gain. RL Reinforcement learning. SB Situation-behavior. UV Unmanned vehicle.

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The overview for UAV Air-Combat Decision method

Cited by 26 publications

References 15 publications

Research on UCAV Maneuvering Decision Method Based on Heuristic Reinforcement Learning

Research on UCAV Maneuvering Decision Method Based on Heuristic Reinforcement Learning

Full-State-Constrained Adaptive Control for a Class of UAVs Suffering from Coupled Uncertainties Using the HOBLF

A Behavior Optimization Method for Unmanned Combat Aerial Vehicles Using Matrix Factorization

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