Author(s)

TianJian Zhong

Affiliation(s)

School of Automation, Central South University, Changsha 410083, Hunan, China.

Corresponding Author

TianJian Zhong

ABSTRACT

This paper systematically investigates optimal deployment strategies for smoke decoys launched by unmanned aerial vehicles (UAVs) to defend against high-speed incoming missiles in complex dynamic scenarios. The research is of significant importance for enhancing the survivability of high-value assets by providing a theoretical foundation and practical optimization tools for intelligent and adaptive smoke countermeasure systems. The research spans from high-precision calculation of effective duration to continuous multi-decoys shielding by a single UAV. First, for calculating effective shielding duration under fixed parameters, a three-dimensional kinematic model was established to characterize the spatial relationships among the missile, the drone's parabolic motion, and the uniform descent of the smoke cloud. Using three-dimensional geometric shielding criteria and a high-resolution time-stepping method, the effective shielding duration achieved by the FY1 drone deploying a single decoy against the M1 missile was precisely calculated to be approximately 1.412 seconds. Subsequently, the problem was elevated to single-deployable parameter optimization. A nonlinear constrained optimization model was constructed with the objective of maximizing masking duration, incorporating decision variables such as the UAV's heading angle, flight speed, deployment timing, and detonation delay. To address the non-differentiable nature of this objective function, a hybrid genetic algorithm and particle swarm optimization method was employed for global search and local refinement. This approach ultimately maximized effective masking duration to 4.690 seconds, with analysis indicating deployment timing and detonation delay as the most critical parameters. Finally, for a single-UAV multi-munition continuous shielding strategy, this paper designed a two-stage analytical-numerical hybrid model: "inverse solution for optimal detonation points followed by forward inversion of UAV strategy." Through the synergistic evolution of the particle swarm optimization framework and GA-PSO, the deployment interval constraints between multiple decoys were successfully resolved, achieving seamless relay between two decoys. The total effective masking duration reached 9.020 s, demonstrating that the proposed model can obtain high-quality solutions with both accuracy and robustness under complex constraints. 

KEYWORDS

Smoke decoy deployment strategy; Genetic algorithm; Particle swarm optimization

CITE THIS PAPER

TianJian Zhong. Smoke decoy deployment strategies based on hybrid intelligent optimization models. Journal of Computer Science and Electrical Engineering. 2025, 7(8): 47-54. DOI: https://doi.org/10.61784/jcsee3110.

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