A new approach to multi-aircraft air combat assignments

Su, Mu Chun; Lin, Shih Chieh; You, Liang Fu

doi:10.1016/j.swevo.2012.03.003

Cited by 18 publications

(9 citation statements)

References 5 publications

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“…It is assumed that the red side has fighters and the blue side has fighters and that the red side's early warning aircraft can accurately identify the model, speed, spatial position, and other basic information of the blue fighters. This section uses the air combat situation in reference [21] as shown in Figure 1 for threat modeling.…”

Section: Air Combat Threatmentioning

confidence: 99%

Improved Ant Colony Optimization for Weapon-Target Assignment

Luo²,

Zhang

et al. 2018

Mathematical Problems in Engineering

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Weapon-target assignment (WTA) which is crucial in cooperative air combat explores assigning weapons to targets with the objective of minimizing the threats from those targets. Based on threat functions, there are four WTA models constrained by the payload and other tactical requirements established. The improvements of ant colony optimization are integrated with respect to the rules of path selection, pheromone update, and pheromone concentration interval, and algorithm AScomp is proposed based on the elite strategy of ant colony optimization (ASrank). We add garbage ants to ASrank; when the pheromone is updated, the elite ants are rewarded and the garbage ants are punished. A WTA algorithm is designed based on the improved ant colony optimization (WIACO). For the purpose of demonstration of WIACO in air combat, a real-time WTA simulation algorithm (RWSA) is proposed to provide the results of average damage, damage rate, and kill ratio. The following conclusions are drawn: (1) the third WTA model, considering the threats of both sides and hit probabilities, is the most effective among the four; (2) compared to the traditional ant colony algorithm, the WIACO requires fewer iterations and avoids local optima more effectively; and (3) WTA is better conducted when any fighter is shot down or any fighter’s missiles run out than along with the flight.

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Section: Air Combat Threatmentioning

confidence: 99%

Improved Ant Colony Optimization for Weapon-Target Assignment

Luo²,

Zhang

et al. 2018

Mathematical Problems in Engineering

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“…% Entropy of the whole swarm (11) e n df o r (12) endfor (13) endfor (14) end while Algorithm 2: Initialization based on information entropy [29].…”

Section: Self-adaptive Visualmentioning

confidence: 99%

“…In the matter of problem formulation, multiple factors such as relative distance, relative angle, and relative velocity are taken into consideration. In domain of algorithms, several sophisticated search and heuristic algorithms have been proposed, such as genetic algorithms [4][5][6][7], simulated annealing [8], discrete particle swarm optimizations [9][10][11][12], permutation and tabu search heuristics [13], and other algorithms [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Weapon-Target Assignment in Cooperative Air Combat

Chang

Kou

et al. 2017

Mathematical Problems in Engineering

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A new approach to solving weapon-target assignment (WTA) problem is proposed in this paper. Firstly, relative superiority that lays the foundation for assignment is calculated based on the combat power energy of the fighters. Based on the relative superiority, WTA problem is formulated. Afterwards, a hybrid algorithm consisting of improved artificial fish swarm algorithm (AFSA) and improved harmony search (HS) is introduced and furthermore applied to solve the assignment formulation. Finally, the proposed approach is validated by eight representative benchmark functions and two concrete cooperative air combat examples. The results show that the approach proposed in this paper achieves good performances in solving WTA problem in cooperative air combat.

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“…For the cooperative air combat decision making of multi-UAV over the horizon, most scholars are investigating the cooperative target assignment problem of multi-UAV, similarly to multi-aircraft cooperative target assignment problems, that is, determining how to effectively allocate the target for each UAV to confront on the premise of satisfying the constraints [14], [15]. For example, Su et al calculated the overall predominance value of a UAV to a target based on four predominance factors (e.g., the velocity, the distance, the angle and the approaching time); then, a mixed integer linear programming model was established, and a self-organizing feature map-based optimization algorithm was proposed to find the optimal cooperative target assignment solution [14]. Based on Su's work, Hu et al developed an improved ant colony optimization algorithm to solve the model [16].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Occupancy Decision Making of Multi-UAV in Beyond-Visual-Range Air Combat: A Game Theory Approach

Wang

et al. 2020

IEEE Access

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In this paper, a new cooperative occupancy decision-making problem for multiple unmanned aerial vehicles (UAVs) in beyond-visual-range (BVR) air combat is proposed; that is, UAV formation on each side first decides the occupancy positions of their own UAVs, then decides the target that each UAV will confront, so that each side can occupy the greatest predominance and be subject to the smallest threat possible. By analyzing the influence of the occupancy positions of the UAVs on the predominance gained by both sides and the threat against them, the problem is described as a zero-sum matrix game. Because the strategy combinations of this game are very numerous, as the best algorithm for the zero-sum matrix game, the Double Oracle (DO) algorithm still has difficulty solving the game model effectively. Therefore, we propose a DO combined with neighborhood search (DO-NS) algorithm to deal with the large-scale zero-sum matrix game. The results show that compared with the DO algorithm, the DONS algorithm can ensure the effectiveness of the computational time and solution quality. INDEX TERMS Multiple UAVs cooperative occupancy decision making, BVR air combat, zero-sum matrix game, multiple UAVs cooperative target assignment, double oracle algorithm.

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A new approach to multi-aircraft air combat assignments

Cited by 18 publications

References 5 publications

Improved Ant Colony Optimization for Weapon-Target Assignment

Improved Ant Colony Optimization for Weapon-Target Assignment

A New Approach to Weapon-Target Assignment in Cooperative Air Combat

Cooperative Occupancy Decision Making of Multi-UAV in Beyond-Visual-Range Air Combat: A Game Theory Approach

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