Autonomous Target Allocation Recommendations

To solve the problem of dwell time management for multiple target tracking in Low Probability of Intercept (LPI) radar network, a Nash bargaining solution (NBS) dwell time allocation algorithm based on cooperative game theory is proposed. This algorithm can achieve the desired low interception performance by optimizing the allocation of the dwell time of each radar under the constraints of the given target detection performance, minimizing the total dwell time of radar network. By introducing two variables, dwell time and target allocation indicators, we decompose the dwell time and target allocation into two subproblems. Firstly, combining the Lagrange relaxation algorithm with the Newton iteration method, we derive the iterative formula for the dwell time of each radar. The dwell time allocation of the radars corresponding to each target is obtained. Secondly, we use the fixed Hungarian algorithm to determine the target allocation scheme based on the dwell time allocation results. Simulation results show that the proposed algorithm can effectively reduce the total dwell time of the radar network, and hence, improve the LPI performance.

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Warship and aircraft coordinated air defense target allocation method based on attack timing

AOPC 2019: AI in Optics and Photonics ◽

10.1117/12.2547840 ◽

2019 ◽

Author(s):

Yunhui Wang ◽

zhijuan zhan ◽

pingyu deng ◽

qing zhou

Keyword(s):

Air Defense ◽

Allocation Method ◽

Target Allocation

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Offense–defense confrontation decision making for dynamic UAV swarm versus UAV swarm

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410019853982 ◽

2019 ◽

Vol 233 (15) ◽

pp. 5689-5702

Author(s):

Dongjing Xing ◽

Ziyang Zhen ◽

Huajun Gong

Keyword(s):

Decision Making ◽

Information Exchange ◽

Social Force ◽

Collective Behaviors ◽

Behavioral Rules ◽

Defensive Behaviors ◽

Uav Swarm ◽

Local Decision ◽

Aerial Vehicle ◽

Target Allocation

This paper studies a dynamic swarm versus swarm unmanned aerial vehicle (UAV) combat problem and proposes a self-organized offense–defense confrontation decision-making (ODCDM) algorithm. This ODCDM algorithm adopts the distributed architecture to account for real-time implementation, where each UAV is treated as an agent and able to solve its local decision problem through the information exchange with neighbors. At each decision making step, the swarm seeks an optimal target allocation scheme and each UAV further selects the corresponding behavioral rules, leading to emergent offensive and defensive behaviors. Therefore, the offense–defense confrontation decision-making process is divided into the target allocation decision based on distributed consensus-based auction algorithm (CBAA) and social-force-based swarm motion decision. An offense–defense preference is introduced to the target allocation optimization model, providing the tactics options for UAV to adopt more offensive or more defensive posture. On the basis of classic collective behaviors of cohesion, separation and alignment, a combat stimulus is considered to drive UAV towards the assigned target. Finally, simulation experiments are carried out to verify the effectiveness of the ODCDM algorithm, and analyze the influences of the external deployment and internal tactics on the combat results.

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Genetic Algorithm-Based Variable Value Control Method for Solving the Ground Target Attacking Weapon-Target Allocation Problem

Mathematical Problems in Engineering ◽

10.1155/2019/6761073 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Chao Wang ◽

Guangyuan Fu ◽

Daqiao Zhang ◽

Hongqiao Wang ◽

Jiufen Zhao

Keyword(s):

Genetic Algorithm ◽

Large Scale ◽

Control Method ◽

Search Space ◽

Initial Population ◽

Improved Genetic Algorithm ◽

Mutation Strategy ◽

Ground Target ◽

Value Range ◽

Target Allocation

Key ground targets and ground target attacking weapon types are complex and diverse; thus, the weapon-target allocation (WTA) problem has long been a great challenge but has not yet been adequately addressed. A timely and reasonable WTA scheme not only helps to seize a fleeting combat opportunity but also optimizes the use of weaponry resources to achieve maximum battlefield benefits at the lowest cost. In this study, we constructed a ground target attacking WTA (GTA-WTA) model and designed a genetic algorithm-based variable value control method to address the issue that some intelligent algorithms are too slow in resolving the problem of GTA-WTA due to the large scale of the problem or are unable to obtain a feasible solution. The proposed method narrows the search space and improves the search efficiency by constraining and controlling the variable value range of the individuals in the initial population and ensures the quality of the solution by improving the mutation strategy to expand the range of variables. The simulation results show that the improved genetic algorithm (GA) can effectively solve the large-scale GTA-WTA problem with good performance.

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