Author(s)

JinSong Zhang^*, JunRui Mu

Affiliation(s)

School of Mathematical Sciences, Chengdu University of Technology, Chendu 610000, Sichuan, China.

Corresponding Author

JinSong Zhang

ABSTRACT

This study investigates smoke-screen interference tactics deployed by unmanned aerial vehicles (UAVs) against incoming M1 missiles. An intuitive geometric-physical model is established for missile trajectories, UAV motion, and smoke-screen projectile descent/detonation. The effective shielding effect of smoke clouds is quantified by combining geometric cone inclusion criteria with linear distance assessment methods. To address the calculation of effective shielding duration for a single smoke grenade, a variable stride search algorithm is employed, yielding an effective shielding duration of 1.391975 seconds under initial conditions. To formulate an optimal jamming strategy, four decision parameters are introduced to explore the optimal deployment strategy for a single drone with a single smoke grenade. A hybrid strategy combining genetic algorithms and particle swarm optimization is adopted, increasing the maximum effective shielding time to 4.585 seconds. Building upon this foundation, this paper analyzes the optimization scenario of coordinated shielding by a single drone deploying multiple flares. A multivariate optimization model is established to account for the composite shielding effect of multiple smoke clouds. Through the basin-jumping particle swarm optimization algorithm, the maximum composite effective shielding time reaches 6.3020 seconds. These research findings provide optimization strategies and actionable solutions for maximizing smoke flare interference effectiveness and coordinated multi-flares interference. 

KEYWORDS

Multivariate optimization model; Variable-step search algorithm; Particle swarm optimization algorithm

CITE THIS PAPER

JinSong Zhang, JunRui Mu. Smoke jammer deployment strategies for drones under multivariate optimization. World Journal of Engineering Research. 2025, 3(5): 36-43. DOI: https://doi.org/10.61784/wjer3058.

REFERENCES

[1] Huang Na, Niu Xinglin, Luo Zhengyou, et al. Research on Manufacturing and Performance Optimization of Smoke Disturbance Grenades in Special Ammunition. China Military-Civilian Conversion, 2025(09): 22-23.

[2] Huang Na, Luo Zhengyou, Niu Xinglin, et al. Research and Development of Novel Materials for Smoke Distraction Grenades. China Military-Civilian Conversion, 2025(08): 27-28.

[3] Luan Yongchao, Zhang Bin, Li Chenkai, et al. Method for Measuring Muzzle Bullet Velocity Under Heavy Smoke Interference. Journal of Ordnance Engineering, 2025, 46(03): 191-201.

[4] Luan Yongchao, Zhang Bin, Li Chenkai, et al. Method for Measuring Muzzle Projectile Initial Velocity Under Strong Smoke Interference. Journal of Ordnance Engineering, 2025: 1-11.

[5] Zhang Jun, Leng Zhihui, Xu Lei, et al. Application Research of Smoke Disruption Pods on a Certain Aircraft Model for Aerial Disruption. Trainer Aircraft, 2023(01): 16-19.

[6] Yang D Y, Qiu X B, Li C L, et al. Rapid Algorithm for Mie Scattering Coefficient in Smoke-Induced Interference of Optically Guided Weapons. Firepower and Command Control, 2017, 42(08): 56-60.

[7] Lei Dan. Research on Video-Based Smoke Detection Methods for Highway Tunnels. Dalian Maritime University, 2017.

[8] Zhang Xinyue. Signal Processing Technology for DSP-Based Frequency-Modulated Continuous Wave Laser Ranging. Beijing Institute of Technology, 2016.

[9] Jia B H, Feng Y, Jia W H. Application of dual-ring coaxial fiber optic sensors in blade tip clearance measurement. Advances in Lasers and Optoelectronics, 2015, 52(10): 91-97.

[10] Chen Zhibin, Zhang Chao, Song Yan, et al. Application of Gray-Scale Stretch Retinex in Smoke Image Enhancement with Large Dynamic Range. Infrared and Laser Engineering, 2014, 43(09): 3146-3150.