An Improved Mutant Firefly Algorithm Optimized Particle Filter Algorithm for UAV Target Positioning

doi:10.12382/bgxb.2024.0549

Abstract

Abstract:

In response to the significant reduction in target positioning accuracy caused by severe nonlinear factors affecting UAV electro-optical platforms, an algorithm based on improved mutant firefly algorithm-particle filter (IMFA-PF) is proposed for UAVs to accurately locate ground targets. Firstly, the state equations and measurement equations for target observation from UAV electro-optical platform are established. And then the IMFA-PF algorithm is utilized to estimate the geographic locatio of a target, and the interaction patterns among particles are altered by introducing multiple mutation strategies and an elasticity mechanism, thereby addressing the particle degradation issues caused by severe nonlinear factors and excessive optimization. Finally, the effectiveness of the algorithm is verified through a one-dimensional nonlinear unstable simulation system and actual flight experiments. Experimental results indicate that the proposed algorithm can improve the particle distribution’s resilience to observational nonlinearity and effectively tackle particle degradation issues, showing better robustness and positioning accuracy compared to the existing positioning methods.

Key words: unmanned aerial vehicle, target positioning, particle filter, swarm intelligence optimization, nonlinear factor

CLC Number:

V249

YAN Xiaojia, ZHU Huimin, SUN Shiyan, SHI Zhangsong, JIANG Shang. An Improved Mutant Firefly Algorithm Optimized Particle Filter Algorithm for UAV Target Positioning[J]. Acta Armamentarii, 2025, 46(5): 240549-.

Figures/Tables 13

References 25

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各滤波方法	α	λ	f_min	f_max	ε_s	N_i	β₀	γ	r_j	ρ_th	k	F
BAPF	0.5	0.5	0	2
SFA-PF					0.5	10	1	0.01	1	0.6	1
IMFA-PF								0.1	10	0.6	1	0.5

各滤波方法	α	λ	f_min	f_max	ε_s	N_i	β₀	γ	r_j	ρ_th	k	F
BAPF	0.5	0.5	0	2
SFA-PF					0.5	10	1	0.01	1	0.6	1
IMFA-PF								0.1	10	0.6	1	0.5

滤波算法	RMSE_mean/m			RMSE_var/m			T_mean/s
滤波算法	50	100	200	50	100	200	50	100	200
PF	4.1681	3.7164	3.5702	0.2642	0.3028	0.0935	0.0879	0.0936	0.1154
BA-PF	2.9312	2.5331	2.4170	0.0906	0.0693	0.0742	0.1117	0.1421	0.2132
SFA-FA	2.2711	2.1028	2.0057	0.0842	0.0730	0.0704	0.1313	0.1759	0.2698
IMFA-PF	2.0693	1.9371	1.9022	0.0791	0.0734	0.0699	0.1549	0.2037	0.3214

滤波算法	RMSE_mean/m			RMSE_var/m			T_mean/s
滤波算法	50	100	200	50	100	200	50	100	200
PF	4.1681	3.7164	3.5702	0.2642	0.3028	0.0935	0.0879	0.0936	0.1154
BA-PF	2.9312	2.5331	2.4170	0.0906	0.0693	0.0742	0.1117	0.1421	0.2132
SFA-FA	2.2711	2.1028	2.0057	0.0842	0.0730	0.0704	0.1313	0.1759	0.2698
IMFA-PF	2.0693	1.9371	1.9022	0.0791	0.0734	0.0699	0.1549	0.2037	0.3214

算法	性能指标	目标
算法	性能指标	T₁	T₂	T₃	T₄	T₅
PF	平均RMSE/m	29.21	29.45	31.69	34.54	35.97
	运算时间/s			0.926
BA-PF	平均RMSE/m	17.33	17.99	17.46	18.97	19.31
	运算时间/s			1.024
SFA-FA	平均RMSE/m	14.20	15.63	15.44	17.69	18.14
	运算时间/s			1.309
IMFA-PF	平均RMSE/m	10.82	10.69	11.34	12.46	12.90
	运算时间/s			1.554